Paper Example on Behavioral Risk Factor Surveillance System

Introduction

Behavioral Risk Factor Surveillance System also abbreviated as BRFSS, according to CDC (2018), is described as the states' premier scheme of health-associated telephone studies which gather a nation data related to the residents of the United States. This data includes the utilization of preventive services, chronic health conditions, and health-connected risk behaviors. BRFSS was developed to monitor the leading contributors for mortality and morbidity in the US not only at local and state level but also at national levels. It differs greatly from the infectious disease surveillance system which encompasses delivery of the health care system, epidemiologists, and public health laboratory. Additionally, CDC (2018), defines surveillance system as a national collaboration which ensures that public health at all levels such as regional, territorial, state, and federal as well as international levels provide notifiable ailment-related health data. This data is furthermore used to screen, regulate, and prevent the emergence and spread of federal-reportable and nationally notifiable non-infectious and infectious ailments, outbreaks, and conditions. BRFSS over the last one decade has been found to be essential in providing essential data for various studies conducted in the United States.

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Application of BRFSS in a Research

According to a survey conducted by Iachan et al. (2016), it utilized data from BRFSS which was significant in National weighting. Their research examined optional methodologies for yielding national weights. The data file employed in their evaluation was derived from the 2013 BRFSS data for public usage. Furthermore, their methodologies started with the state-level weights which currently are calculated in BRFSS. The standard approach which they used for data comparisons was a simple technique. It concatenated data with the present weights at the state level. There were some restrictions experienced using the simple method. However, the present BRFSS technique for weighting at the state-level included a ranking procedure which ensured weights totaled to the known sum of populations for the main demographic present in every state. Where they found research outcomes being linked with features of demographics, they anticipated that equating national distributions decreased estimates bias of such results at the national level.

In their study results, Iachan et al. (2016), compared the methodologies used to generate national weights in relation to bias and projected variance of the resulting weighted study approximations. They gauged the estimated variances in two manners. Firstly, they evaluated a pure influence on the unequal weighting of the study variances as well as the impacts of design, in terms of weights variability. Secondly, they used an empirical technique where they examined the projected variances for main indicators of health which included those of obesity, lack of insurance, asthma, arthritis, diabetes, and current smoking. Ultimately, a single indicator for diabetes was assessed by the demographic sub-division to determine whether some techniques could perform better particularly with the estimates of sub-groups.

Iachan et al. (2016), found that the escalated BRFSS sampling uniformity and weighting techniques throughout the states from 2011 made data collection more effective. This was when compared to the study conducted from 1990s. At this era, weighting methodology and sampling variation throughout the states developed additional challenges. Additionally, unequal selection possibilities across state samples were found to be the incentive to use BRFSS data weighting techniques on population totals at the national level.

Conclusion

The approach described in their survey offered national weights for the state-centered BRFSS. Iachan et al. (2016), argued that users of data who combined data derived from different state greatly benefit from utilization of novel national weights. Conversely, people utilizing data derived from several states would discover that weights related to the population at the state level would be appropriate for their studies. Therefore, Iachan et al. (2016), suggested that data users should often take precaution to encompass compound sample designs such as BRFSS data, in their studies since they are gathered by employing weighted and stratified designs. New methodologies which included BRFSS resulted in a weighted distribution which precisely bred national population characteristics for the main demographic cluster unlike in the normal aggregated techniques. They concluded that matching national distributions decreased biases of estimation results at the national level where study results are related to population characteristics.

References

CDC - BRFSS. (2018). Retrieved from https://www.cdc.gov/brfss/index.html

Iachan, R., Pierannunzi, C., Healey, K., Greenlund, K. J., & Town, M. (2016). National weighting of data from the behavioral risk factor surveillance system (BRFSS). BMC medical research methodology, 16(1), 155. Retrieved from https://bmcmedresmethodol.biomedcentral.com/articles/10.1186/s12874-016-0255-7