The role of epidemiology in chronic disease prevention and health promotion programs

National and Regional Rates of Chronic Diseases and All-Cause Mortality in Saudi Arabia-Analysis of the 2018 Household Health Survey Data

The disease burden and mortality were estimated in Saudi Arabia between 2010 and 2017 but were unknown in 2018. This study aims to assess the 2018 national and regional rates of chronic diseases and all-cause mortality among the total and Saudi populations. In this descriptive cross-sectional study, we obtained data from 24,012 households from the 2018 household health survey. We included doctor-diagnosed chronic conditions such as diabetes mellitus (DM), hypertension (HTN), cardiovascular diseases (CAD), and cancer (CN). A secondary analysis was performed by the total and Saudi populations. Both citizens and residents comprised the total population. Makkah and Al-Medina had greater rates among the total population; however, Al-Baha and Ha'il had high rates of chronic diseases and related mortality in the Saudi population. Age-adjusted mortality rates were 286 per 100,000 population-year. The age-adjusted mortality rate among those aged 65 and above was 3428 per 100,000 population in the same age group. Men had a rate of 1779 per 100,000 men, which was higher than the rate of 1649 for women. In 2018, most citizens in Ha'il had DM, most Al-Baha had HTN and CAD, and most Al-Qassim had CN. People aged 65 and older had the highest death rate.

Understanding the impact of racial self-identification on perceptions of health-related quality of life: a multi-group analysis

PURPOSE

Multiple group analysis is used to determine whether the health-related quality of life (HRQoL) model developed by Wilson and Cleary (1995) is equivalent across racial categories. Using data from the Centers for Disease Control's Behavioral Risk Factor Surveillance System, this study compares racial groups (African American vs. White; Hispanic vs. White) to determine whether they perceive HRQoL similarly.

METHODS

This secondary data analysis of 2007 New York State Behavioral Risk Factor Surveillance System data (n = 6,103 cases) uses the multi-group analysis function in structural equation modeling to test for equivalency across the named ethnic/racial groups.

RESULTS

The White subsample achieved good fit indices and produced significant estimates for all structural components of the hypothesized model. Noteworthy differences, however, were found for the African American and Hispanic samples. In both cases, the data failed to support the Wilson and Cleary model as operationalized. This was most pronounced in the Hispanic versus White comparison, where the findings suggest fundamental differences between the two groups at the basic concept measurement level.

CONCLUSIONS

The substantial discrepancies that the findings suggest for the subsamples call into question not only the structural integrity of the Wilson and Cleary model for minorities but also suggest that racial groups, particularly Hispanics, may perceive concepts of health-related quality of life differently than Whites.

The utility of the behavioral risk factor surveillance system (BRFSS) in testing quality of life theory: an evaluation using structural equation modeling

PURPOSE

This study uses structural equation modeling to assess the utility of the behavioral risk factor surveillance system (BRFSS)--the Centers for Disease Control's premier surveillance tool for monitoring behavioral risk factors-in predicting health-related quality of life (HRQoL).

METHODS

Using SPSS/AMOS (version 18), the study utilizes New York State data extracted from the 2007 BRFSS national dataset to test a well-known HRQoL model developed by Wilson and Cleary (J Am Med Assoc 59-65, 2). The analysis represents an exploratory study that seeks to identify new applications for this important epidemiological database as well as a theoretical evaluation that examines the robustness of our current understanding of HRQoL.

RESULTS

Findings support the Wilson and Cleary (J Am Med Assoc 59-65, 2) model, with the final model producing fit indices well within the thresholds traditionally used as benchmarks of good fit.

CONCLUSIONS

The integrity of the Wilson and Cleary (J Am Med Assoc 59-65, 2) model was substantiated, and the utility of BRFSS data for operationalization of HRQoL concepts was demonstrated successfully. This study has: (1) expanded the role of epidemiological research to include whole theory testing; and (2) successfully operationalized the Wilson and Cleary (J Am Med Assoc 59-65, 2) model using available, non-clinical data, which represents a major methodological contribution to the study of HRQoL.

Researchers and policymakers: travelers in parallel universes

Public policy, in the form of laws, guidelines, and regulations, has a profound effect on our daily lives and health status. Reasons for a lack of consistent and systematic translation of public health research into public policy is examined, including differences in decision-making processes, poor timing, ambiguous findings, the need to balance objectivity and advocacy, personal demands of the process, information overload, lack of relevant data, and the mismatch of randomized thinking with nonrandom problems. Next, several actions are suggested that should help bridge the chasm between science and policy, such as greater involvement in the process, better understanding of political decision making, building of effective teams, and development of political champions. Scientists are obligated not only to discover new knowledge but also to ensure that discoveries are applied to improve health.