Perceived neighborhood problems: multilevel analysis to evaluate psychometric properties in a Southern adult Brazilian population

There has been renewed interest in epidemiology on how aspects related to the place in which people live may affect health outcomes. Place-based features are hypothesized to affect health over and above other individual characteristics through multiple pathways [1–7].

Studies on the association between neighborhood conditions and health have mostly characterized neighborhoods using measures derived from census databases [2, 3]. Despite practical advantages, the use of aggregate measures implies important limitations including decennial periodicity of data gathering, changes in boundaries of units over time, and the sensitivity of some measures to the dynamic process of migration and emigration in neighborhoods. Moreover they are only indirect proxies for specific neighborhood aspects that may be relevant to health outcomes. The use of indirect indicators can hinder causal inferences regarding neighborhood effects on health in observational studies [2, 8].

A variety of other approaches are available to directly measure neighborhood attributes. These include systematic social observation [9], the use of geographic information systems to create measures about resource availability and access [10], and administration of questionnaires to residents, in order to obtain information on their perception of neighborhood conditions [8]. Each approach provides different and complementary information [8].

Measures of resident’s perceptions of neighborhood attributes can be examined in two ways. On one hand they can be studied in relation to individual-level health outcomes in individual level analyses. A second approach involves building contextual variables, through the aggregation of the responses of all residents of a given neighborhood. The underlying assumption is that the process of aggregating individual perceptions results in a more valid measure of objective attributes [8]. Analytical approaches involving the use of three-level multilevel analyses have been used to create these measures [11, 12].

Physical attributes of places in which people live, and theirs perception about them, are important health determinants beyond socioeconomic influence [13]. The perception of place in which people dwell may impact on individual health and may be a more telling indicator for individual health than objective neighborhood characteristics. Studies that combine residents perceived neighborhoods aspects and census measures have been tending to find stronger relationships between environment and outcomes than those studies that rely only on aggregate data [14]. Studies have suggested, indeed, that objective and subjective measures of neighborhood may contribute independently to health and well-being [15].

Perceptions of neighborhood problems may be influenced by individual-level characteristics of respondents (such as age, gender and individual-level social and economic characteristics) as well as by the objective features of the neighborhoods in which individuals reside [16, 17] Neighborhood socioeconomic disadvantage broadly reflects aggregate features of households. Furthermore, the clustering of households-level deprivation in neighborhoods appears to generate local contexts that induce health-related processes [18]. Diez-Roux & Mair [2] added that low income participants may live closer to areas with adverse environments than high income individuals even living at the same neighborhood [2].

Neighborhoods with higher levels of problems can be less attractive to people with higher income, and reach lower prices in the real estate market. Effects of land use with industrialization have had highly uneven effects at local levels as households experiencing a diverse range of disadvantages are increasingly clustered in poor neighborhoods [19]. Notably, in Brazil a large process of rural exodus with overcrowding in metropolitan regions encouraged poverty concentration in some areas, with worst life conditions, considered more vulnerable in periphery areas devoid of amenities, services, and even social spaces, and designed to poorer people [20].

Several studies have examined factors related to neighborhood perceptions in high-income countries [12, 17] Factors found to be related to neighborhood perceptions have included neighborhood characteristics (such as levels of poverty) as well as individual-level characteristics such as education and occupational status, age and time spent in the neighborhood a [12, 17].

Studies in different populations have reported association among perceived neighborhood aspects and distinct health outcomes, as: smoke [21], self-rated health [17, 22], cardiovascular disease [8], emotional health, and others [22].

However, few studies have examined the predictors or measurement properties of neighborhood perceptions in medium and low income countries, which may be different from those high income countries due the contextual socioeconomic and cultural differences [23]. This paper aimed to assess the ecometric and psychometric properties of a scale assessing the perception of neighborhood problems by adults living in Florianopolis, SC, a city in Southern Brazil with half a million inhabitants.

This paper investigated the measurement properties of scales utilized to measure neighborhood problems in an urban area in Brazil. We also examined whether neighborhood problems were associated with selected individual and census tract level characteristics. Two neighborhood problems scales were identified from the 16 items: one measuring physical characteristics and the other measuring social characteristics. The internal consistency of the scales was high (0.67 to 0.81). The ecometric properties of the scales measured by ICC and reliability were good, in the order of 0.24 to 0.28 for ICC and 0.94 to 0.96 for reliability. Higher values on the scales representing higher level of problems in the physical and social domains were associated with younger age, more length of time residing in the neighborhood and lower census tract income level.

The ecometric and psychometric properties of the scales were similar to those found in other studies. Using data from three in three United States sites (Baltimore, Maryland; Forsyth County, North Carolina; and New York, New York) Mujahid et al. [12] reported ICCs ranging 0.05 to 0.51 for activities with neighbors, and aesthetic quality, respectively. Friche et al. [24] in Belo Horizonte, Brazil tested ten scales and reported ICCs ranging from 0.02 to 0.33 for social cohesion, and walking environment, respectively. Friche et al. [23] investigated physical and social disorder scales similar to ours and reported ICCs of 0.14 and 0.13, for physical and social disorder, respectively. The ICC quantifies the percentage of variability in the scale that lies between neighborhoods. High value of ICC indicates greater agreement between respondents within a neighborhood [12]. The values of reliability observed in the Epifloripa Study were high, and similar to those found in a Southeastern Brazilian metropolis [23]. These results indicate that the mean of scores are good estimators of the true neighborhood scores for each scale [12].

Corsi et al. [34] analyzing ecometric properties of responses to questionnaire items from 2,360 individuals residing in communities of 5 countries (China, India, Brazil, Colombia and Canada) of the Environmental Profile of a Community’s Health (EPOCH) study, found reliabilities ranged from 0.81 for community social cohesion in urban communities to 0.96 for knowledge of the health effects of smoking in a rural communities.

The sixteen collected items on perceived neighborhood problems were grouped in two scales reflecting social and physical dimensions. Similar clustering of neighborhood measures has also been reported by others [27, 35]. The neighborhood physical problems scales aspects was primarily linked to environmental problems such as garbage, uneven pavements, unpleasant smells and air. On the other hand, aspects connected with social problems such burglaries, assaults, drug use, vandalism and murders loaded on a separate social disorder scale.

Neighborhood physical problems and social disorder scales were moderately correlated, suggesting that they may measure distinct although interrelated constructs [16, 17]. Neighborhood problems would be expected to be greater in residential areas with more social problems; for example, concern about issues such as litter and walking around after dark may be more severe in places in which antisocial behavior is not proscribed [14].

The convergent validity of the scale, attested for their relation with other variables, in expected direction was good. There is some evidence linking individual characteristics and perceived neighborhood problems [12, 17]. In Epifloripa Study people aged 50 years and over perceived fewer problems in their neighborhood, when compared with youngest. Similarly, in a study of Canadian adults, Pampalon et al. [36], observed that people aged 45 years and over perceived fewer neighborhood social and environmental problems than the younger persons [36].

The length of residence in the neighborhood was associated with higher scores for neighborhood social disorder. Ellaway et al. [27] found that length of residence and neighborhood stability were found to be significant in two ways: longer residence is associated, up to a point (15 years), with a stronger sense of ‘neighboring’; and longer intended residence is itself a key element of ‘attraction to neighborhood’ [27]. People living longer in the same neighborhood may have a sense of the changes that have occurred in their neighborhood over time, and perceive more violence problems in their neighborhoods [37].

As expected, people living in lower income areas reported more problems than those living in higher income areas. For a number of reasons that have to do with inequities in power, resource distributions, and access to opportunities socially disadvantaged areas are likely to face adverse physical and social environments. However, contrary to expectation we found that individual–level income was only weakly associated with neighborhood problems; people in the higher income tertile presenting slightly lower scores of problems in neighborhood. For educational attainment the association was the opposite of expectation: with lower scores for groups with lower educational attainment years. However associations with individual level SES were generally weak and often not statistically significant. Friche et al. [23] noted similar patterns in the association of neighborhood scales with socioeconomic indicators. They [23] pointed out the contrasts between poor areas adjacent to rich areas, typically observed in Brazilian urban centers, as a possible explanations for the weak associations observed. This may influence the individual responses, because regardless of socioeconomic level, people may share some similar environments and services available for a broader area, resulting in similar perceptions across economically diverse adjacent neighborhoods [23].

Like other studies [12, 23] our results also suggest that there is variation in responses within neighborhoods. Part of this may be due to the arbitrary geographic definition of neighborhoods that we used. Although clearly census tracts capture some spatial heterogeneity (as indicated but the neighborhood ICCs) there is likely to be substantial spatial heterogeneity which is not captured by census tracts [12]. Brazilian census tracts were defined to be the smallest territorial unit that can be reasonably covered in fieldwork. Each census tract has around 300 households [26].

Additional sources of variability within neighborhoods may be attributed to subjectivity inherent to perceptions and the error in measurement. The occurrence of within-neighborhood differences suggests that it may be beneficial to average over neighborhood respondents or raters in estimating neighborhood characteristics [12].

Sample size in multilevel analyses has been an ongoing area of work. The number of respondents in each cluster of Epifloripa Study ranged 10 to 40, only one of 63 census tract evaluated, had only 10 observations. The average number of individuals per group-level unit was 27.3. Mujahid et al. [12] reported that 25–30 participants per cluster often maximizes reliability [33] Mass e Hoss [38] performed a serial of simulations with different numbers of groups and individuals in each group, and observed non-significant bias for most regression and variance components under conditions similar to those observed in the Epifloripa Study. However they did report that he standard errors of the second-level variance can be underestimated when the number of groups is substantially lower than 100 [4].

There is an ongoing debate on the nature of variables involved in neighborhood analyses. Cummins et al. [4] argue that the distinction we often make between composition and context is somewhat artificial. This can acquire special relevance when it comes to residents perceptions about their own neighborhood, because an individual-level variable is used to capture neighborhood-level realities. However, disaggregated individual and group sources of variability can be useful. The fact that variance in reported can be decomposed into between area and within area variability indicates that these perceptions are at least in part capturing truly contextual features [38].

The findings of this study showed acceptable ecometric properties of the proposal scales, and documented associations of perceptions with individual and contextual socioeconomic characteristics. Those scales have been applied in analysis of EpiFloripa study against other outcomes, i.e. self-rated health, showing important association even after adjustment for other socioeconomic, demographic, health related behaviors and health status variables. Place specific characteristics related to broader geographic and social contexts such as cities or countries may influence the relationship between perceived neighborhood problems and objective socioeconomic measures at both the individual and census tract level. Future works can apply these scales to examine how places influence health.