Focusing neighborhood context and self-rated health in the Pró-Saúde Study

Abstract

The influence of neighborhood characteristics on self-rated health has been little studied. A multilevel approach using hierarchical models was applied to analyze the relationship between the socioeconomic characteristics in 621 neighborhoods (level 2) in the city of Rio de Janeiro, Brazil, and the self-rated health of 3,054 university employees (level 1) from the baseline of the Pró-Saúde Study. Neighborhoods were created using the SKATER algorithm (Spatial ‘K’luster Analysis by Tree Edge Removal) to cluster census tracts according to four indicators and a minimum population of 5,000 people. After adjustment for individual factors (per capita income, schooling, age, sex, ethnicity, health-related behavior and chronic diseases), low level of neighborhood income and higher numbers of members per household were significantly associated with poor self-rated health. Participants living in medium income-level neighborhoods were 34% more likely to self-rate their health as being poor. Those living in areas with a higher density of members per household were 50% more likely to present poor self-rated health. Neighborhood context influences self-rated health, beyond the effect of individual factors. Worsening neighborhood socioeconomic conditions affect health adversely, which in turn increasing the chance of poor self-rated health.

Self-rated health among teachers: prevalence, predictors, and impact on absenteeism, presenteeism, and sick leave

“…Contrary to the extensive descriptions in the literature, 2 - 4 age was not associated with poor self-rated health among teachers. Older teachers had a slightly higher prevalence of poor self-rated health, but this was insufficient to achieve statistical significance.…”

“…Another aspect is that in the present study poor self-perceived health was made up of the group of teachers who answered that their health was “poor” or “regular”, as commonly occurs in the literature. 2 - 4 Since two of the earlier studies used the same answer structure, 17 , 19 it was possible to compare the results for the same construct. However, in the EDUCATEL study, 18 self-rated health was assessed with different answer options and three categories were clustered to categorize poor and regular answers (very poor, poor, regular).…”

“… 18 Although descriptive data is available in the literature, limited information is available about sociodemographic, occupational, lifestyle, and health-related predictors of self-rated health among teachers and the specific characteristics of this profession prevent generalization of results from the general population. 2 - 6 In addition, it is unknown if poor self-rated health is associated with absenteeism, presenteeism, and sick leave, and, thus, how self-rated health can be used to prevent and monitor these relevant teachers’ health outcomes.…”

Physical Disorders and Poor Self-Rated Health in Adults Living in Four Latin American Cities: A Multilevel Approach

“…Association observed for social disorder in the unadjusted model disappeared when individual-level covariates were adjusted for. Other studies have found that quality of services, mobility, public facilities, safety from traffic, violence, presence of parks, street noise, income, overcrowding, and the water, sewage network, electricity, and garbage collection services were related to health status [12,15,31,[41][42][43]. It is possible that many of these elements are better captured in the physical disorder scale that we used.…”

“…Urban environment features may affect health through a multiplicity of processes including influencing health-related behaviors through features such as walkability, the location of parks and green spaces, and access to and advertising for different types of foods. Other•mechanisms may include community stressors such as violence or lack of safety and the availability of social connections and social support mechanisms [10][11][12][13][14][15][16].…”