Who declines to respond to the reactions to race module?: findings from the South Carolina Behavioral Risk Factor Surveillance System, 2016-2017

Background

The inclusion of self-reported differential treatment by race/ethnicity in population-based public health surveillance and monitoring systems may provide an opportunity to address long-standing health inequalities. While there is a growing trend towards decreasing response rates and selective non-response in health surveys, research examining the magnitude of non-response related to self-reported discrimination warrants greater attention. This study examined the distribution of sociodemographic variables among respondents and non-respondents to the South Carolina Behavioral Risk Factor Surveillance System (SC-BRFSS) Reactions to Race module (6-question optional module capturing reports of race-based treatment).

Methods

Using data from SC-BRFSS (2016, 2017), we examined patterns of non-response to the Reactions to Race module and individual items in the module. Logistic regression models were employed to examine sociodemographic factors associated with non-response and weighted to account for complex sampling design.

Results

Among 21,847 respondents, 15.3% were non-responders. Significant differences in RTRM non-response were observed by key sociodemographic variables (e.g., age, race/ethnicity, labor market participation, and health insurance status). Individuals who were younger, Hispanic, homemakers/students, unreported income, and uninsured were over-represented among non-respondents. In adjusted analyses, Hispanics and individuals with unreported income were more likely to be non-responders in RTRM and across item, while retirees were less likely to be non-responders. Heterogeneity in levels of non-responses were observed across RTRM questions, with the highest level of non-response for questions assessing differential treatment in work (54.8%) and healthcare settings (26.9%).

Conclusions

Non-responders differed from responders according to some key sociodemographic variables, which could contribute to the underestimation of self-reported discrimination and race-related differential treatment and health outcomes. While we advocate for the use of population-based measures of self-reported racial discrimination to monitor and track state-level progress towards health equity, future efforts to estimate, assess, and address non-response variations by sociodemographic factors are warranted to improve understanding of lived experiences impacted by race-based differential treatment.

Supplementary Information

The online version contains supplementary material available at 10.1186/s12889-021-11748-y.

Migrant health in Italy: a better health status difficult to maintain-country of origin and assimilation effects studied from the Italian risk factor surveillance data

Background

Many studies on migrant health have focused on aspects of morbidity and mortality, but very few approach the relevant issues of migrants’ health considering behavioral risk factors. Previous studies have often been limited methodologically because of sample size or lack of information on migrant country of origin. Information about risk factors is fundamental to direct any intervention, particularly with regard to non-communicable diseases that are leading causes of death and disease. Thus, the main focus of our analysis is the influence of country of origin and the assimilation process.

Method

Utilizing a surveillance system that has been collecting over 30,000 interviews a year in Italy since 2008, we have studied migrants’ attitudes and behaviors by country of origin and by length of stay. Given 6 years of observation, we have obtained and analyzed 228,201 interviews of which over 9000 were migrants.

Results

While migrants overall present similar conditions to native-born Italians, major differences appear when country of origin or length of stay is considered. Subgroups of migrants present substantially different behaviors, some much better than native-born Italians, some worse. However, integration processes generally produce a convergence towards the behavioral prevalence observed for native-born Italians.

Conclusions

Health programs should consider the diversity of the growing migrant population: data and analyses are needed to support appropriate policies. Many migrants’ subgroups arrive with healthier behaviors than those of their adopted country. However, they are likely to have a less favorable social position in their destination countries that could lead to a change towards less healthy behaviors. Interventions capable of identifying this tendency could produce significant better health for this important part of the future (multicultural) populations.

Increasing gaps in health inequalities related to non-communicable diseases in South Australia; implications towards behavioural risk factor surveillance systems to provide evidence for action

Background

Although Australia is a country cited as having generally low health inequalities among different socioeconomic groups, inequalities have persisted. The aim of this analysis was to highlight how inequalities have evolved over a 13 years period in South Australia (SA).

Methods

Since 2002, over 600 interviews per month have been undertaken with SA residents through a computer assisted telephone survey method (total 77,000+). Major risk factors and chronic diseases have been analyzed providing trends by two socio-economic variables: education and a proxy of income (ability to save).

Results

While income and educational gaps are reducing over time in SA, those that remain in the lower socio-economic groups have a generally higher prevalence of risk factors and chronic diseases. The health disparity gap is still relevant, although at a different extent, for all the variables considered in our study, with most appearing to be stable if not increasing over time.

Conclusions

Surveillance can be a good source of information both to show the evolution of problems and to evaluate possible future interventions. Extensive effort is still required to “close the gap” of health inequalities in SA. More precisely targeted and properly implemented interventions are needed.

Electronic supplementary material

The online version of this article (10.1186/s12889-018-6323-7) contains supplementary material, which is available to authorized users.

The spatial distribution of gender differences in obesity prevalence differs from overall obesity prevalence among US adults

PURPOSE

Although obesity disparities between racial and socioeconomic groups have been well characterized, those based on gender and geography have not been as thoroughly documented. This study describes obesity prevalence by state, gender, and race and/or ethnicity to (1) characterize obesity gender inequality, (2) determine if the geographic distribution of inequality is spatially clustered, and (3) contrast the spatial clustering patterns of obesity gender inequality with overall obesity prevalence.

METHODS

Data from the Centers for Disease Control and Prevention's 2013 Behavioral Risk Factor Surveillance System were used to calculate state-specific obesity prevalence and gender inequality measures. Global and local Moran's indices were calculated to determine spatial autocorrelation.

RESULTS

Age-adjusted, state-specific obesity prevalence difference and ratio measures show spatial autocorrelation (z-score = 4.89, P-value < .001). Local Moran's indices indicate the spatial distributions of obesity prevalence and obesity gender inequalities are not the same. High and low values of obesity prevalence and gender inequalities cluster in different areas of the United States.

CONCLUSIONS

Clustering of gender inequality suggests that spatial processes operating at the state level, such as occupational or physical activity policies or social norms, are involved in the etiology of the inequality and necessitate further attention to the determinates of obesity gender inequality.