SciELO - Scientific Electronic Library Online

 
vol.35 issue2Early adolescent childbearing in Colombia: time-trends and consequencesAssociation of breastfeeding, maternal anthropometry and body composition in women at 30 years of age author indexsubject indexarticles search
Home Pagealphabetic serial listing  

Services on Demand

Journal

Article

Indicators

Related links

Share


Cadernos de Saúde Pública

Print version ISSN 0102-311XOn-line version ISSN 1678-4464

Cad. Saúde Pública vol.35 no.2 Rio de Janeiro  2019  Epub Feb 18, 2019

http://dx.doi.org/10.1590/0102-311x00016418 

ARTICLE

Perception of neighborhood disorder and blood pressure in adults: a multilevel population-based study

Carla Zanelatto1 
http://orcid.org/0000-0002-6168-9898

Doroteia Aparecida Höfelmann2 
http://orcid.org/0000-0003-1046-3319

Maruí Weber Corseuil Giehl3 
http://orcid.org/0000-0002-4460-3116

Waleska Nishida1 
http://orcid.org/0000-0002-4937-5124

João Luiz Bastos1 
http://orcid.org/0000-0002-1816-0745

1 Centro de Ciências da Saúde, Universidade Federal de Santa Catarina, Florianópolis, Brasil.

2 Departamento de Nutrição, Universidade Federal do Paraná, Curitiba, Brasil.

3 Departamento de Ciências da Saúde, Universidade Federal de Santa Catarina, Araranguá, Brasil.

ABSTRACT

The aim of this study was to verify whether the perception of neighborhood physical and social disorder is associated with increased systolic (SBP) and diastolic blood pressure (DBP), as well as to examine the influence of the residential census tract’s socioeconomic status on this association. This was a cross-sectional study that included a representative sample of 1,720 adults 20 to 59 years of age living in Florianópolis, Santa Catarina State, Brazil. Two blood pressure measurements were taken, and information was collected on the perception of neighborhood disorder. The contextual variable was the mean head-of-household’s years of schooling in the selected census tracts. Statistical analysis included multilevel models with the first level represented by individuals and the second by census tracts. Interaction terms were examined between schooling tertiles in the census tract and tertiles of perception of neighborhood disorder on blood pressure. No statistically significant overall associations were identified between neighborhood disorder and SBP or DBP. However, the study showed a mean increase in SBP of 7.88mmHg (95%CI: 1.38; 14.40) in subjects that perceived more neighborhood disorder and lived in census tracts with less schooling, when compared to the reference category. Public policies aimed at lowering or that have an impact on SBP and DBP in the population should also address the characteristics of the context where the population lives, specifically in contexts marked by lower levels of schooling.

Keywords: Arterial Pressure; Urban Health; Multilevel Analysis

Introduction

High blood pressure is one of the most relevant risk factors for morbidity and mortality related to cardiovascular diseases such as acute myocardial infarction, chronic renal failure, and stroke, and there are multiple individual biological, behavioral, and sociodemographic factors (e.g., income and schooling) related to its pathogenesis 1. Associated nutritional factors include high sodium and alcohol intake and excess body weight 2,3,4. Recent studies have also shown that altered blood pressure levels may be related to other factors not exclusively at the individual level, such as contextual or environmental characteristics, including their perception about social and physical disorder in the neighborhood 5,6.

The potential mechanisms for the effect of social and physical disorder on health include limited access to resources promoting physical activity and healthy eating, in addition to chronic stress and discouragement of physical activity. Evidence suggests that exposure to social disorder in the neighborhood (vandalism, theft, robberies, muggings, homicides, and kidnappings) can lead to individuals’ perception that the environment poses a constant threat, triggering physiological responses linked to increased blood pressure. These responses include modification of the sympathetic nervous system and increased production of hormones like cortisol 7,8. Thus, exposure to sources of stress along with fewer opportunities and resources for treatment can result in rising blood pressure.

Previous studies have shown an association between lack of safety in neighborhoods and high blood pressure. Malambo et al. 5 conducted a cross-sectional study of 671 South African adults over 35 years of age and found that those who perceived their neighborhood as exposed to crime showed higher odds of hypertension (OR = 1.44; 95%CI: 1.03; 2.02). A study by Kaiser et al. 9 with 3,382 adults 45-84 years of age in six different locations in the United States and covering ten years (2000-2011) found that those who developed hypertension lived in areas that were less favorable to physical activity and with lower presence of healthy foods. In particular, after adjusting for sociodemographic variables, a healthy food environment (fostering the adoption of adequate eating habits and physical activity) was inversely associated with the development of systemic arterial hypertension (HR = 0.95; 95%CI: 0.89; 1.00). A possible explanation for this finding is that the presence of alternative routes, bike lanes, and parks, as well as healthy food outlets in the neighborhoods allow physical activity and the consumption of healthy foods, with a reduction in hypertension.

Understanding the way context affects blood pressure is relevant for the development of public policies to prevent hypertension. The existing initiatives have particularly targeted the individual characteristics involved in this process, which is necessary but insufficient to deal with the problem. For example, in 2001 the Brazilian Ministry of Health 10 proposed the Plan for the Reorganization of Health Care for Hypertension and Diabetes, supplying states and municipalities with a computerized system called HiperDia. The proposal, aimed at guaranteeing diagnosis and the patient’s linkage to basic health units, essentially addresses education, lifestyle changes, and medication as strategies to treat the disorder.

In 2008 the Ministry of Health proposed guidelines and recommendations for comprehensive care for noncommunicable diseases (NCDs) 11, including the first official mention in Brazil that behavioral risk factors are potentiated by environmental factors. However, there was no explanation of what these factors were, and neither were interventions proposed to address the effects of residential context on hypertension. This emphasizes the need for studies highlighting the role of context on blood pressure in order to develop measures to deal with this health problem.

Beyond government policies and measures to deal with hypertension, the scientific literature is still incipient on the relationship between neighborhood disorder and hypertension 5,9,12. A major limitation is that most studies consulted in the literature were conducted in high-income countries, and the results may differ from those in middle and low-income countries due to the diverse contextual characteristics. In particular, there may be interference in people’s perception of neighborhood issues in light of ethnic composition, culture, and socioeconomic conditions, thus requiring the study of different communities 13. One should also assume that the infrastructure for physical exercise, safety, social support, and protection from pollution in high-income countries differs from that of middle-income countries like Brazil, which also displays striking social inequalities 14.

Low schooling level in the neighborhood, a marker of low socioeconomic status, is an important factor that can independently increase the size of negative effect on health through physical and social disorder, in addition to other neighborhood and individual economic factors. This is because residents of neighborhoods in areas characterized by lower schooling in the census tract are generally more exposed to stressors like crime and worse physical and social infrastructure 15,16.

On the other hand, higher average schooling level in the census tract may reflect the residents’ knowledge of the importance of investing in resources to improve the neighborhood’s recreation, safety, and infrastructure, in addition to favoring the residents’ collective mobilization for common benefits. Such collective movements may thus have a positive impact on residents’ health 17.

Studies in different contexts and with comparable methods are useful and necessary because they allow monitoring and identifying relevant differences and similarities between cities and/or countries, besides informing the future effectiveness of specific interventions in the neighborhoods. The results of such research can also contribute to the identification of the population at risk of developing hypertension. The current study thus proposes to verify whether the perception of physical and social disorder in the neighborhood is associated with increased systolic (SBP) and diastolic blood pressure (DBP), as well as to examine the influence of the census tract’s socioeconomic status on this association in a city in the South of Brazil.

Methods

This was a cross-sectional population-based study nested in a longitudinal survey entitled the EpiFloripa Adult Study, whose baseline was conducted from September 2009 to June 2010. The study included a representative sample of adults 20 to 59 years of age living in the urban area of Florianópolis, capital of Santa Catarina State, Brazil. The city has a population of 485,000 according to projections by the Brazilian Institute of Geography and Statistics (IBGE) for 2017 (Panorama de Florianópolis, 2017. https://cidades.ibge.gov.br/brasil/sc/florianopolis/panorama, accessed on Mar/2018).

The sample for EpiFloripa was determined according to the following parameters: unknown prevalence of the outcome (50%), 95% confidence interval (95%CI), sampling error of 3.5 percentage points, design effect of 2.0 (due to the cluster sampling), plus 10% for possible losses and/or refusals. An additional 15% was added to the final sample size to allow controlling confounding factors in multivariate analyses, resulting in a sampling size of 2,016 adults 18. Sixty of the city’s 420 urban census tracts were selected, organized according to the deciles of the head-of-family’s income (ranging from BRL 192.80 to BRL 13,209.50) 19. A systematic sample was selected with six census tracts in each income decile. All the selected census tracts were visited by the fieldwork team, having previously numbered the residential blocks. To decrease the coefficient of variation in the number of households between the census tracts, the tracts were merged and divided again. This process resulted in 63 census tracts with 16,755 eligible households. Eighteen households were selected systematically in each tract, totaling 1,134 households and an average of 32 adults selected in each census tract.

Eligibility and exclusion criteria, losses, and refusals

All adults 20 to 59 years of age living in the selected households in each tract were considered eligible. Individuals were excluded if they presented physical or cognitive disabilities that prevented answering the questionnaire. Pregnant women and women who had given birth in the six months prior to the study did not have their blood pressure measured. Individuals that declined to participate in the interview were defined as refusals. Individuals not located in the selected households after four visits, with at least one on the weekend and one in the evening, were defined as losses.

Data collection and pilot study

The data were collected by 35 interviewers/examiners who had undergone a training and standardization process before conducting the fieldwork. Prior to the data collection, a pilot study was conducted with 100 adult individuals residing in two census tracts selected intentionally and that were not included in the cohort’s baseline.

Outcome

The study’s outcome variables were SBP and DBP, obtained with two blood pressure measurements taken during the visit. Resting time prior to the blood pressure measurements was at least 30 minutes, and the interval between the two measurements was approximately 15 minutes. The SBP and DBP values (in mmHg) were obtained with an electronic pulse sphygmomanometer with digital readout (Techline, São Paulo, Brazil), previously calibrated by the Brazilian National Institute of Metrology, Standardization, and Industrial Quality (INMETRO). During the blood pressure measurements, each participant remained sitting with their feet on the floor, arm resting on a table and level with the heart, palm turned upward 20. The statistical analysis explored the outcome as continuous, with the mean of the two measurements as the reference 1,21.

Principal exposure

The perception of problems with neighborhood disorder was investigated using the answers to a 16-item list adapted from Ellaway et al. 22. The complete process of cross-cultural adaptation to Portuguese and the construction of scales for perception of neighborhood disorder are described in detail in Höfelmann et al. 23. The variables were grouped in two dimensions: (1) social disorder (vandalism, theft, robbery, mugging, homicide, kidnapping, illegal drug use, hazardous walking in the area after dark, bad reputation, and problems with the police) and (2) physical disorder (presence of garbage, irregular sidewalks, speeding, and urban transportation) 24. The set of items was operationalized in three different ways: (a) the overall sum of the physical and social disorder items; (b) sum of the physical disorder items; and (c) sum of the social disorder items. Multilevel models (three levels) were used to develop the scales, leading to bayesian estimates, used to transform individuals’ perceptions of the neighborhoods into group-level variables 22,25. Level 1 referred to individual items, level 2 to persons grouped inside neighborhoods, and level 3 the neighborhoods. For the analyses, the perception scales were divided into tertiles: lower tertile (fewer problems in the neighborhood); intermediate and upper tertiles (more problems in the neighborhood) 23.

Covariables

The individual-level covariables included gender, age (20-29; 30-39; 40-49; 50-59 years), schooling (≤ 4 years, 5-8 years, 9-11 years, and ≥ 12 years), total monthly household income (divided into tertiles: first tertile - BRL 0.00-BRL 2,000.00; second tertile - BRL 2,050.00-BRL 4,000.00; third tertile - BRL 4,018.00-BRL 100,000.00) - and use of antihypertensive medication in the 30 days prior to the data collection (yes or no). All the covariables were collected during the interviews. Gender was observed and noted by the interviewer, and age was calculated from date of birth, while total monthly household income was recorded during application of the questionnaire.

Contextual variable

The contextual variable was mean head-of-household’s years of schooling in each of the census tracts 19. This variable was divided into tertiles. Data were collected from the 2000 Brazilian Population Census (IBGE. http://www.ibge.gov.br) for each of the 63 census tracts.

Statistical analyses

Stata statistical package version 13.1 (https://www.stata.com) was used for the analyses. Initially, a descriptive statistic of the sample was performed according to individual and contextual characteristics. The variables were included stepwise in groups, in a sequence of multilevel linear regression models, with the first level consisting of individuals and the second level consisting of census tracts. These models were adjusted to test the association between the perception of neighborhood disorder and SBP and DBP, jointly with the selected covariables, including the contextual variable (mean head-of-family’s schooling). The analyses were corrected for design effect and sampling plan for the estimates of prevalence rates, and the multilevel models considered the complex sampling design (weights and clusters) in the calculation of the standard errors, confidence intervals, and probability values.

From the empty model with the intercept, the variables were included in the analysis in blocks, with three models for each outcome in total. Model 1 was adjusted for the demographic variables (gender, age), while in model 2 the adjustment was by family income and schooling, in addition to gender and age. Finally, model 3 included a health status variable (use of antihypertensive medication), along with the variables listed in model 2. All the models included the principal exposure (perception of physical and social disorder in the neighborhood) in the analysis.

The Akaike (AIC) and Bayesian information criteria (BIC) were used to assess the models’ fit, and intraclass correlation (ICC) of the multilevel linear regression was calculated for each model as follows: variance of level 2/(variance of level 2 + variance of level 1). This measure provides an estimate of the total variance in blood pressure attributed to the census tract’s level and explained by each model. The fixed effects with their 95%CI as well as the random effects were also calculated and presented for each model. All the study’s associations were examined in the context of multilevel regression models, and specifically the modifying effect of the census tract’s schooling on the principal target association was tested by inserting interaction terms in the analysis. In particular, the p-values of the interaction tests were adjusted by the Bonferroni algorithm, considering p < 0.001 as statistically significant interaction.

Ethical aspects

All the participants received and signed a free and informed consent form explaining the study’s objectives and potential risks. The study was approved by the Ethics Research Council of Federal University of Santa Catarina (UFSC), under protocol 351/08 of December 15, 2008.

Results

The final sample consisted of 1,720 adults, which represented 85.3% of the initially calculated sample (2,016 adults). The majority of participants were women (55.3%) and had 12 years of schooling or more (44.2%). Mean age was 38.0 years (SD = 11.6). Table 1 describes the entire sample. Mean SBP and DBP were 133.0mmHg (SD = 19.7) and 85.0mmHg (SD = 14.1), respectively (information not shown in the table).

Table 1 Descriptive characteristics of the study participants. Florianópolis, Santa Catarina State, Brazil, 2009/2010. 

Variables n % 95%CI
Gender
Male 734 44.7 42.5; 46.9
Female 909 55.3 53.1; 57.4
Age (years)
20-29 524 31.9 27.9; 36.1
30-39 378 23.0 20.4; 25.8
40-49 422 25.7 22.7; 28.9
50-59 319 19.4 17.2; 21.9
Schooling (years)
0-4 148 9.0 6.8; 11.7
5-8 228 13.9 11.3; 17.0
9-11 541 32.9 28.5; 37.6
≥ 12 726 44.2 37.3; 51.3
Family income
First tertile (BRL 0-2,000) 623 37.9 31.8; 44.5
Second tertile (BRL 2,050-4,000) 498 30.3 26.9; 34.0
Third tertile (BRL 4,018-100,000) 522 31.8 25.8; 38.4
Use of medication for hypertension
No 1,548 94.2 92.8; 95.3
Yes 95 5.8 4.6; 7.2
Total 1,643

95%CI: 95% confidence interval.

Table 2 shows the overall effect of each principal independent variable on blood pressure. In relation to SBP, compared to the null model, the intraclass correlation decreased by 1.5%, both for the overall scale and for physical and social disorder, while there was a 2.2% increase in intraclass correlation for DBP when the effect of physical disorder was analyzed. In the upper tertile of social disorder, SBP and DBP were 0.53mmHg (95%CI: -3.18; 2.09) and 0.25mmHg (95%CI: -1.57; 2.07) higher than in the lower tertile, respectively, although neither difference reached statistical significance (p < 0.05).

Table 2 Overall effect of neighborhood disorder on systolic (SBP) and diastolic blood pressure (DBP). Florianópolis, Santa Catarina State, Brazil, 2009/2010 (N = 1,643). 

SBP DBP
Coefficient (95%CI) Coefficient (95%CI)
Null model
AIC 415,925.0 385,062.9
BIC 415,941.2 385,079.1
Intercept 133.0 (131.5; 134.6) 85.0 (84.0; 86.0)
ICC (%) 6.8 4.6
Variance level-1 24.1 8.3
Variance level-2 363.2 191.4
Overall disorder scale
Lower tertile Reference Reference
Intermediate tertile -0.35 (-3.03; 2.32) -0.16 (-1.95; 1.92)
Upper tertile -0.75 (-3.57; 2.06) -0.13 (-2.00; 1.74)
AIC 415,919.0 385,066.3
BIC 415,946.0 385,093.3
Intercept 133.4 (131.2; 135.6) 85.0 (83.5; 86.6)
ICC (%) 6.9 4.6
Variance level-1 24.3 8.3
Variance level-2 363.5 191.4
Physical disorder
Lower tertile Reference Reference
Intermediate tertile 0.69 (-1.95; 3.34) -0.10 (-2.13; 1.93)
Upper tertile -0.37 (-2.96; 2.21) -0.23 (-1.97; 1.52)
AIC 415,905.1 385,065.3
BIC 415,932.2 385,092.3
Intercept 132.9 (131.0; 134.8) 85.1 (83.6; 86.6)
ICC (%) 6.9 4.7
Variance level-1 24.2 8.3
Variance level-2 363.4 191.4
Social disorder
Lower tertile Reference Reference
Intermediate tertile -0.39 (-2.59; 1.80) -0.84 (-2.53; 0.85)
Upper tertile 0.54 (-3.18; 2.09) 0.25 (-1.57;,2.07)
AIC 415,923.3 385,015.1
BIC 415,950.4 385,042.2
Intercept 133.3 (131.4; 135.3) 85.2 (83.7; 86.7)
ICC (%) 6.9 4.6
Variance level-1 24.2 8.2
Variance level-2 363.6 191.2

95%CI: 95% confidence interval; AIC: Akaike information criterion; BIC: Bayesian information criterion; ICC: intraclass correlation.

As for the effect of physical disorder, SBP levels in the intermediate and upper tertiles were, respectively, 1.76mmHg (95%CI: 0.58; 4.11) and 1.26mmHg (95%CI: -1.00; 3.53) higher than in the lower tertile in model 3, although these results were not statistically significant. The intraclass correlation was 8.5 for model 1 (adjusted for gender and age), and there was a reduction of 15.3% when use of antihypertensive medication was included in model 3 (Table 3). There was also an increase in DBP among participants from the intermediate and upper tertiles of disorder, as well as a 9.8% reduction in intraclass correlation when medication was included in model 3 (Table 4).

Table 3 Multilevel linear regression models for association between perception of neighborhood disorder and systolic blood pressure (SBP), adjusted for covariables. Florianópolis, Santa Catarina State, Brazil, 2009/2010 (N = 1,643). 

Model 1 Model 2 Model 3
Coefficient (95%CI) Coefficient (95%CI) Coefficient (95%CI)
Overall scale of disorder
Lower tertile Reference Reference Reference
Intermediate tertile 0.60 (-1.99; 3.19) 0.59 (-2.07; 3.25) 0.51 (-1.94; 2.96)
Upper tertile 0.69 (-1.96; 3.33) 0.77 (-1.85; 3.37) 0.92 (-1.67; 3.52)
AIC 408,630.7 408,072.0 406,231.6
BIC 408,679.3 408,147.7 406,312.6
Intercept 148.5 (145.0; 152.0) 151.2 (146.6; 155.9) 151.1 (146.5; 155.6)
ICC (%) 8.6 7.2 7.3
Variance level-1 25.4 22.0 21.0
Variance level-2 307.0 304.0 293.0
Physical disorder
Lower tertile Reference Reference Reference
Intermediate tertile 1.55 (-0.84; 3.94) 1.82 (-0.59; 4.24) 1.76 (-0.58; 4.11)
Upper tertile 0.91 (-1.37; 3.19) 1.16 (-1.12; 3.44) 1.26 (-1.00; 3.53)
AIC 408,591.1 408,013.8 406,180.2
BIC 408,639.8 408,089.4 406,261.3
Intercept 147.9 (144.3;151.4) 150.8 (145.9; 155.6) 150.6 (145.8; 155.4)
ICC (%) 8.5 7.0 7.2
Variance level-1 25.2 21.7 20.7
Variance level-2 306.4 303.7 292.2
Social disorder
Lower tertile Reference Reference Reference
Intermediate tertile 0.37 (-1.71; 2.46) 0.22 (-1.85; 2.29) 0.50 (-1.47; 2.48)
Upper tertile 0.28 (-2.18; 2.75) 0.18 (-2.18; 2.54) 0.49 (-1.96; 2.93)
AIC 408,164.7 407,604.4 405,763.2
BIC 408,213.3 407,680.1 405,844.3
Intercept 148.4 (145.3; 151.4) 151.5 (146.9; 156.2) 151.2 (146.6; 155.8)
ICC (%) 8.7 7.3 7.4
Variance level-1 25.6 22.2 21.2
Variance level-2 307.0 304.3 292.8

95%CI: 95% confidence interval; AIC: Akaike information criterion; BIC: Bayesian information criterion; ICC: intraclass correlation.

Note: model 1: adjusted for gender, age; model 2: adjusted for gender, age, family income, schooling; model 3: adjusted for gender, age, family income, schooling, and use of medication for hypertension.

Table 4 Multilevel linear regression models for the association between perception of neighborhood disorder and diastolic blood pressure (DBP), adjusted for covariables. Florianópolis, Santa Catarina State, Brazil, 2009/2010 (n=1643) 

Model 1 Model 2 Model 3
Coefficient (95%CI) Coefficient (95%CI) Coefficient (95%CI)
Overall scale of disorder
Lower tertile Reference Reference Reference
Intermediate tertile 0.64 (-1.19; 2.46) 0.61 (-1.31; 2.53) 0.56 (-1.23; 2.35)
Upper tertile 0.62 (-1.06; 2.33) 0,62 (-1.02; 2.27) 0.74 (-0.91; 2.39)
AIC 379,872.5 379,405.9 377,905.9
BIC 379,921.2 379,481.5 377,986.9
Intercept 91.8 (89.2; 94.4) 92.3 (88.8; 95.9) 92.2 (88.7; 95.7)
ICC (%) 6.1 5.2 5.4
Variance level-1 9.9 8.8 8.6
Variance level-2 168.7 167.4 162.1
Physical disorder
Lower tertile Reference Reference Reference
Intermediate tertile 0.40 (-1.48; 2.27) 0.52 (-1.40; 2.44) 0.48 (-1.42; 2.39)
Upper tertile 0.43 (-1.22 ;2.08) 0.50 (-1.13; 2.14) 0.57 (-1.03; 2.18)
AIC 379,885.2 379,413.3 377,915.9
BIC 379,933.8 379,489.0 377,997.0
Intercept 91.9 (89.2; 94.6) 92.4 (88.5; 96.3) 92.3 (88.5; 96.1)
ICC (%) 6.1 5.3 5.5
Variance level-1 9.9 8.9 8.7
Variance level-2 168.7 167.4 162.1
Social disorder
Lower tertile Reference Reference Reference
Intermediate tertile -0.30 (-1.80; 1.19) -0.44 (-1.94; 1.06) -0.24 (-1.71; 1.23)
Upper tertile 0.67 (-1.03; 2.37) 0.60 (-1.03; 2.24) 0.81 (-0.86; 2.48)
AIC 379,391.5 378,928.1 377,429.7
BIC 379,440.1 379,003.7 377,510.7
Intercept 92.1 (89.8; 94.5) 92.7 (89.2; 96.2) 92.4 (89.0; 95.9)
ICC (%) 6.0 5.3 5.4
Variance level-1 9.8 8.8 8.6
Variance level-2 168.8 167.4 162.2

95%CI: 95% confidence interval; AIC: Akaike information criterion; BIC: Bayesian information criterion; ICC: intraclass correlation.

Note: model 1: adjusted for gender, age; model 2: adjusted for gender, age, family income, schooling; model 3: adjusted for gender, age, family income, schooling, and use of medication for hypertension.

In relation to the overall and social disorder scales, there were no statistically significant findings in the multilevel linear regression models (Tables 3 and 4). Still, after application of the multiple comparisons command with Bonferroni adjustment, persons from the upper tertile in the overall disorder scale and those living in census tracts with lower average schooling showed mean SBP that was 7.88mmHg higher (95%CI: 1.38; 14.40, p = 0.004) than persons that perceived intermediate levels of overall disorder and lived in neighborhoods with higher average schooling in the census tract. This result suggests possible interaction between average schooling in the census tract and perception of social disorder in the neighborhood.

The models were also tested for the outcome classified as high versus low blood pressure according to the current guidelines of the Brazilian Society of Cardiology 21, which define hypertension as SBP ≥ 140mmHg and/or DBP ≥ 90mmHg. The models were also explored with the new classification of hypertension proposed by Whelton et al. 26, as follows: stage 1 hypertension (SBP: 130-139mmHg or DBP: 80-89mmHg) and stage 2 hypertension (SBP: ≥ 140mmHg or ≥ 90mm Hg). No contextual effect or significant result was observed for any of the strategies for operationalization of the outcome.

Discussion

No statistically significant associations were identified in the study sample between neighborhood disorder and SBP or DBP, even after including the use of antihypertensive medication in the analysis. A possible explanation is the quality of the collected data. Blood pressure was measured by trained examiners with calibrated equipment, but the use of antihypertensive medication was self-reported by the participants. Studies indicate that this self-report measure should be used with caution, since it may underestimate the use of medication 27,28.

Such underestimation may have occurred in the present study, in which few participants reported using medication for hypertension (only 5.8%). Antihypertensive medication aims to help maintain SBP and DBP pressure within adequate levels to preserve the individual’s health. In the current study, some participants may have reported not using antihypertensive drugs when they actually were on such medication and would have presented normal blood pressure, thus attenuating the estimated of effect of neighborhood disorder on blood pressure.

In high-income countries, findings from previous studies showed an association between lack of safety and worse infrastructure in neighborhoods and high blood pressure. A study in the Netherlands with 1,322 participants (Turkish, Dutch, and Moroccan individuals) 18 to 65 years of age found that after adjusting for age, gender, schooling, and body mass index among Turkish individuals, high neighborhood crime rates and annoyance from motor vehicle traffic were associated with higher DBP (β = 2.96; 95%CI: 0.71; 5.20 and β = 2.83; 95%CI: 0.60; 5.06, respectively) 29. Another study in the United States with 2,612 individuals 45 to 85 years of age found that residents of neighborhoods with better perceived safety were less prone to hypertension (OR = 0.74; 95%CI: 0.63; 0.86) 30. Studies have also mentioned the potential protective effect of an environment that favors access to proper resources for physical activity. Malambo et al. 5, in a study with 671 adults in South Africa, observed that the likelihood of hypertension was significantly lower in persons that perceived the neighborhood infrastructure as favorable for walking and cycling (OR = 0.65; 95%CI: 0.46; 0.90). Likewise, Li et al. 12 found lower SBP and DBP in residents of neighborhoods with favorable conditions for walking.

The lack of association between perception of neighborhood disorder and high blood pressure in the EpiFloripa study can be attributed partly to the fact that Florianópolis is a typical Brazilian city, characterized by the contrast between impoverished areas neighboring on wealthy areas. This may influence individual responses, so that the perception of the neighborhood is similar among respondents, even if they reside in tangibly different locations from the economic, social, and geographic point of view 31. Another factor that may have contributed to the lack of statistical association is the fact that participants were asked about their “neighborhood” without having received a definition of the term. Thus, less uniformity in interpreting the meaning of the term “neighborhood” may have resulted in a non-differential information bias, which tends to attenuate the study’s target associations.

Another relevant point involves the urban characteristics around the households in Florianópolis. According to data from the 2010 Population Census (IBGE. http://www.ibge.gov.br), in a total of 141,956 permanent private households, only 2,398 had garbage accumulated on their lots and only 3,281 had open-air sewage, while the great majority had public lighting and paved streets. In other words, low variability in these characteristics may also have contributed to the lack of a statistically significant association between perceived neighborhood disorder and hypertension.

An important finding from the current study was the level of schooling in the census tract, identified as a possible effect modifier in the relationship between perceived neighborhood disorder and blood pressure. Neighborhoods with high levels of crime, unemployment, and violence are generally located in census tracts characterized by less schooling and lower income, which represent a constant source of stress 15,16. Residents can also be more exposed to environmental stressors such as harmful chemical products and pollution that can have negative health implications 32. Thus, residents in economically underprivileged neighborhoods tend to have worse health when compared to those living in better-off areas, since the context can make individuals more vulnerable to the perception of neighborhood disorder, while these same residents can encounter relatively fewer environmental, financial, and social resources to help them deal with their health problems.

Meanwhile, individuals living in neighborhoods with higher average educational levels not only have more income but may be more inclined to engage in collective work involving the mobilization of resources to improve neighborhood safety, recreation, education, and health 17. All these collective efforts generate the perception of a safe, convenient, and friendly environment, which consequently leads to improvement in the residents’ physical and mental health. Importantly, the context’s schooling represents a measure of socioeconomic status and is more stable than income, which is subject to important fluctuations when recorded in a relatively short time frame 33. This is true because income depends on the country’s economy and can vary substantially across seasons and years. In addition, depending on the situation, seasonal work may be more common than formal employment. The use of “occupation” as a study variable also presents limitations, since persons may be working at temporary jobs without specification of their occupation.

As suggested by some empirical studies, the effects of high levels of schooling in a neighborhood’s residents can benefit all the residents, regardless of the harmful effects of income inequality 17,34,35. Based on the Hawaii Health Survey 2007-2008, Zhang et al. 35 found that education at the neighborhood level independently affects self-rated health and partially mediates the association between ethnicity and health outcomes.

Equally important is that adult individuals may spend a major part of their day away from home, so that not only their residential neighborhood but also other neighborhoods where they work or study may impact their health. In other words, individuals in better financial condition may have more resources to seek alternatives in other areas outside the neighborhoods where they live 36.

The limitations that may have influenced the results feature the way the neighborhood disorder scales were built, based on individual responses, which are influenced by the objective reality but also by personal factors and perceptions 23,25. Although the sample is representative of the city as a whole, it was not representative of each census tract, which may have affected the measured perception of the neighborhood generated in the context of the analyses, possibly influencing the lack of association between perceived neighborhood disorder and high blood pressure. However, we reiterate that the study was conducted with a representative sample of adults and reached 85.3% of the initially calculated sample size, besides adhering to methodological rigor through prior training of the field team and the use of validated data collection instruments. In addition, few studies have been conducted and published in Brazil that have used more robust analytical models such as multilevel models. The use of data from the 2000 Brazilian Population Census, collected nine years before this study, was not considered a limitation, since the impact of neighborhood on health outcomes tends to occur in the long term. There is a minimum latency period between the neighborhood’s characteristics and their effects on blood pressure, and the latter could not have been detected if the study had used data from the more recent 2010 Brazilian Population Census37.

At any rate, public policies are needed that aim to reduce or have an impact on hypertension in the population (in addition to individual characteristics), and that consider and incorporate strategies to address physical and social disorder in neighborhoods, especially in census tracts with lower average schooling. The challenge is thus to make environmental changes through the creation of more spaces for safe and friendly community living in daily activities, as well as to improve transportation, neighborhood lighting, reinforcement of security in more violence-prone areas, and other factors that expand the possibilities for social interaction. Another important point is to expand access to healthcare in areas still lacking sufficient coverage, aimed at assisting or treating persons that are more exposed to stress.

The current study was thus an important point of departure for the development and implementation of environmental intervention programs, since it allowed the analysis and identification of a population apparently more vulnerable to the effects of perceived physical and social disorder. Longitudinal studies are necessary to confirm the effect of physical and social disorder on blood pressure over time. Analyses are also needed on the role of schooling in the census tract, aimed at elucidating the possible mechanisms involved in this relationship.

Acknowledgments

The authors wish to thank the study participants, the Brazilian National Research Council (CNPq; 485327/2007-4) for having supported and funded the EpiFloripa Project 2009, and the Graduate Studies Coordinating Board (Capes) for the PhD scholarship granted to C. Zanelatto.

REFERENCES

1. Chobanian AV, Bakris GL, Black HR, Cushman WC, Green LA, Izzo JL, et al. Seventh report of the Joint National Committee on Prevention, Detection, Evaluation, and Treatment of High Blood Pressure. Hypertension 2003; 42:1206-52. [ Links ]

2. Blaustein MP, Leenen FH, Chen L, Golovina VA, Hamlyn JM, Pallone TL, et al. How NaCl raises blood pressure: a new paradigm for the pathogenesis of salt-dependent hypertension. Am J Physiol Heart Circ Physiol 2012; 302:H1031-49. [ Links ]

3. Brown IJ, Dyer AR, Chan Q, Cogswell ME, Ueshima H, Stamler J, et al. Estimating 24-hour urinary sodium excretion from casual urinary sodium concentrations in western populations. Am J Epidemiol 2013; 177:1180-92. [ Links ]

4. He FJ, MacGregor GA. Reducing population salt intake worldwide: from evidence to implementation. Prog Cardiovasc Dis 2010; 52:363-82. [ Links ]

5. Malambo P, Kengne AP, Lambert EV, De Villers A, Puoane T. Association between perceived built environment and prevalent hypertension among South African adults. Adv Epidemiol 2016; 2016:1038715. [ Links ]

6. Lagisetty PA, Wen M, Choi H, Heisler M, Kanaya AM, Kandula NR. Neighborhood social cohesion and prevalence of hypertension and diabetes in a South Asian population. J Immigr Minor Health 2016; 18:1309-16. [ Links ]

7. Mujahid MS, Diez Roux AV, Cooper RC, Shea S, Williams DR. Neighborhood stressors and race/ethnic differences in hypertension prevalence (the Multi-Ethnic Study of Atherosclerosis). Am J Hypertens 2011; 24:187-93. [ Links ]

8. Kim J. Neighborhood disadvantage and mental health: the role of neighborhood disorder and social relationships. Soc Sci Res 2010; 39:260-71. [ Links ]

9. Kaiser P, Diez Roux AV, Mujahid M, Carnethon M, Bertoni A, Adar SD, et al. Original contribution neighborhood environments and incident hypertension in the multi-ethnic study of atherosclerosis. Am J Epidemiol 2016; 183:988-97. [ Links ]

10. Departamento de Ações Programáticas Estratégicas, Secretaria de Políticas de Saúde, Ministério da Saúde. Plano de reorganização da atenção à hipertensão arterial e ao diabetes mellitus: manual de hipertensão arterial e diabetes mellitus. Brasília: Ministério da Saúde; 2001. [ Links ]

11. Ministério da Saúde. Diretrizes e recomendações para o cuidado integral de doenças crônicas não transmissíveis: promoção da saúde, vigilância, prevenção e assistência. Brasília Ministério da Saúde; 2008. [ Links ]

12. Li F, Harmer P, Cardinal BJ, Naruepon V. Built environment and changes in blood pressure in middle aged and older adults. Prev Med 2009; 48:237-41. [ Links ]

13. Powell-Wiley TM, Ayers CR, Lemos JA, Lakoski SG, Vega GL, Grundy S, et al. Relationship between perceptions about neighborhood environment and prevalent obesity: data from the Dallas Heart Study. Obes (Silver Spring) 2013; 21:1251-65. [ Links ]

14. Florindo AA, Salvador EP, Reis RS. Physical activity and its relationship with perceived environment among adults living in a region of low socioeconomic level. J Phys Act Health 2013; 10:563-71. [ Links ]

15. Chaix B, Bean K, Leal C, Thomas F, Havard S, Evans D, et al. Individual/neighborhood social factors and blood pressure in the record cohort study: which risk factors explain the associations? Hypertension 2010; 55:769-75. [ Links ]

16. Do DP, Roux AVD, Hajatb A, Auchincloss A, Merkind S, Ranjit N, et al. Circadian rhythm of cortisol and neighborhood characteristics in a population-based sample: the Multi-Ethnic Study of Atherosclerosis. Health Place 2011; 17:625-32. [ Links ]

17. Zhagn W, Chen Q, McCubbin H, McCubbin L, Foley S. Predictors of mental and physical health: individual and neighborhood levels of education, social well-being, and ethnicity. Health Place 2011; 17:238-47. [ Links ]

18. Boing AC, Peres KG, Boing AF, Hallal PC, Silva NN, Peres MA. Inquérito de saúde EpiFloripa: aspectos metodológicos e operacionais dos bastidores. Rev Bras Epidemiol 2014; 17:147-62. [ Links ]

19. Instituto Brasileiro de Geografia e Estatística. Censo demográfico 2000: agregado por setores censitários dos resultados do universo. Rio de Janeiro: Instituto Brasileiro de Geografia e Estatística; 2003. [ Links ]

20. Sociedade Brasileira de Hipertensão. V diretrizes brasileiras de hipertensão arterial. Arq Bras Cardiol 2007; 89:24-79. [ Links ]

21. Malachias MVB, Souza WKSB, Plavnik FL, Rodrigues CIS, Brandão AA, Neves MFT, et al. 7ª diretriz brasileira de hipertensão arterial. Arq Bras Cardiol 2016; 107(3 Suppl 3):1-85. [ Links ]

22. Ellaway A, Macintyre S, Kearns A. Perceptions of place and health in socially contrasting neighbourhoods. Urban Stud 2001; 38:2299-316. [ Links ]

23. Höfelmann DA, Diez Roux AV, Leopoldo J, Antunes F, Peres MA. Perceived neighborhood problems: multilevel analysis to evaluate psychometric properties in a Southern adult Brazilian population. BMC Public Health 2013; 13:1085. [ Links ]

24. Höfelmann DA, Diez Roux AV, Antunes JLF, Peres MA. Association of perceived neighborhood problems and census tract income with poor self-rated health in adults: a multilevel approach. Cad Saúde Pública 2015; 31 Suppl 1:S79-91. [ Links ]

25. Mujahid MS, Diez Roux AV., Morenoff JD, Raghunathan T. Assessing the measurement properties of neighborhood scales: from psychometrics to ecometrics. Am J Epidemiol 2007; 165:858-67. [ Links ]

26. Carey RM, Whelton PK; 2017 ACC/AHA Hypertension Guideline Writing Committee. Prevention, detection, evaluation, and management of high blood pressure in adults: synopsis of the 2017 American College of Cardiology/American Heart Association Hypertension Guideline. Ann Intern Med 2018; 168:351-8. [ Links ]

27. Ning M, Zhang Q, Yang M. Comparison of self-reported and biomedical data on hypertension and diabetes: findings from the China Health and Retirement Longitudinal Study (CHARLS). BMJ Open 2016; 6:e009836. [ Links ]

28. Menezes TN, Oliveira ECT, Fischer MATS. Validity and concordance between self-reported and clinical diagnosis of hypertension among elderly residents in Northeastern Brazil. Am J Hypertens 2014; 27:215-21. [ Links ]

29. Agyemang C, van Hooijdonk C, Wendel-Vos W, Ujcic-Voortman JK, Lindeman E, Stronks K, et al. Ethnic differences in the effect of environmental stressors on blood pressure and hypertension in the Netherlands. BMC Public Health 2007; 7:118. [ Links ]

30. Mujahid MS, Diez Roux AV, Morenoff JD, Raghunathan TE, Cooper RS, Ni H, et al. Neighborhood characteristics and hypertension. Epidemiology 2008; 19:590-8. [ Links ]

31. Friche A, Diez Roux AV, César C, Xavier C, Proietti F, Caiaffa WT. Assessing the psychometric and ecometric properties of neighborhood scales in developing countries: Saúde em Beagá Study, Belo Horizonte, Brazil, 2008-2009. J Urban Health 2013; 90:246-26. [ Links ]

32. Diez Roux AV, Mair C. Neighborhoods and health. Ann N Y Acad Sci 2010; 1186:125-45. [ Links ]

33. Howe LD, Galobardes B, Matijasevich A, Gordon D, Johnston D, Onwujekwe O, et al. Measuring socio-economic position for epidemiological studies in low- and middle-income countries: a methods of measurement in epidemiology paper. Int J Epidemiol 2012; 41:871-86. [ Links ]

34. Galea S, Ahern J. Distribution of education and population health: an ecological analysis of New York City neighborhoods. Am J Public Health 2005; 95:2198-205. [ Links ]

35. Zhang W, McCubbin H, McCubbin L, Chen Q, Foley S, Strom I, et al. Education and self-rated health: an individual and neighborhood level analysis of Asian Americans, Hawaiians, and Caucasians in Hawaii. Soc Sci Med 2010; 70:561-9. [ Links ]

36. Célio FA, Xavier CC, Andrade ACS, Camargos VP, Caiaffa WT, Friche AAL, et al. Características individuais associadas à autopercepção da extensão territorial da vizinhança. Cad Saúde Pública 2014; 30:1935-46. [ Links ]

37. Wagner KJP, Boing AF, Subramanian SV, Höfelmann DA, D'Orsi E. Effects of neighborhood socioeconomic status on blood pressure in older adults. Rev Saúde Pública 2016; 50:78. [ Links ]

Received: January 27, 2018; Revised: May 07, 2018; Accepted: June 21, 2018

Correspondence C. Zanelatto Programa de Pós-graduação em Saúde Coletiva, Centro de Ciências da Saúde, Universidade Federal de Santa Catarina. Rua Delfino Conti s/n, Florianópolis SC 88036-020, Brasil. carlaz_nutri@hotmail.com

Contributors

C. Zanelatto, D. A. Höfelmann, M. W. C. Giehl, W. Nishida and J. L. Bastos contributed in the study conception, data analysis and interpretation, writing and critical revision of the article, and approval of the final version for publication.

Additional informations

ORCID: Carla Zanelatto (0000-0002-6168-9898); Doroteia Aparecida Höfelmann (0000-0003-1046-3319); Maruí Weber Corseuil Giehl (0000-0002-4460-3116); Waleska Nishida (0000-0002-4937-5124); João Luiz Bastos (0000-0002-1816-0745).

Creative Commons License Este é um artigo publicado em acesso aberto sob uma licença Creative Commons