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Walden University Week 4 Colorado Heart Healthy Solutions Program Discussion


4 days ago

Enica Saffold

Understanding Linear Regressions


Understanding Linear Regressions

Effectiveness of a Community Health Worker Cardiovascular Risk Reduction Program in Public Health and Health Care Settings.


Independent Variable: CHD risk factors, overall health, knowledge of risk, frequency of aerobic exercise, BMI (components of the Framingham Risk Scores)

Dependent Variable: At Risk No Retest & At Risk Retested. Dichotomous because one is for and the other is against.

Confounders: age, gender, race, insurance, education, and employment status

Research Question

Would the Colorado Heart Healthy Solutions program prevent coronary heart disease (CHD) with community health workers improve CHD risk in health care settings?

Purpose of Multiple Linear Regression?

Multiple logistic regression is when multiple continuous independent variables in the analysis explain one dependent variable that’s dichotomous or continuous. Multiple regression is used to determine a predictive relationship; results can indicate how much variability within the dependent variable is explained by each independent variable (Polit & Beck, 2017). The authors compared sociodemographic variables between the group with no retest and those with a retest by using the 2-sample t-test for continuous variables and the x2 test for categorical variables. Multiple regression was used to determine factors correlated with changes in Framingham Risk Scores (FRS). Studies have shown that coronary health disease risk factor interventions, in particular, were isolated within health care settings. No studies demonstrate a reduction in CHD risk, which is a vast predictor of nonfatal and fatal cardiovascular events (Yusuf et al., 2004).

Main Results

Participants who received a follow-up phone call before the retest had lower FRS scores than those who did not receive a phone call. It was statistically significant at p =.04. FRS scores didn’t differ significantly amongst participants enrolled in health care facilities compared with local public health agencies (p=.9). There were statistically significant improvements overall in diet, weight, blood pressure, lipids, and FRS amongst those with uncontrolled risk factors. Changes in the FRS scores were similar regardless of region and health care or public health setting (Krantz et al., 2013).


The overall goal is to improve cardiovascular health across Colorado by promoting access to primary care while encouraging healthy behaviors. By collaborating community health care partnerships/agencies to form a resource to help patients improve their health. The ratio of participants with underlying diabetes and hypertension was higher than the average, suggesting that interventions are needed to target a population in need of services. It can be meaning that community health workers operating both public health and clinical sites can improve CHD risk.


Some limitations were specified in the article. First, since this was a pilot study, there is always “room for improvement,” which makes it difficult to discern the program’s contribution to the observed improvement in outcomes. Other factors might have been missed that would later need to be considered. The second is that the study is limited to only one area, Colorado as a whole, relating to the dissemination of the program outside of Colorado. Third, the software system wasn’t suited for a larger scale (population) in counties in Colorado. Fourth, since the program was voluntary and resources aggressively purse participants not retested were limited. The last limitation that stuck out was the authors utilized the traditional FRS that assessed the standard 10-year risk of developing CHD.


Krantz, M. J., Coronel, S. M., Whitley, E. M., Dale, R., Yost, J., & Estacio, R. O. (2013). Effectiveness of a Community Health Worker Cardiovascular Risk Reduction Program in Public Health and Health Care Settings. American Journal of Public Health, 103(1), 19-27.

Polit, D. F., & Beck, C. T. (2017). Nursing research: Generating and assessing evidence for nursing practice (10th ed). Philadelphia, PA: Lippincott Williams & Wilkins.

Yusuf, S., Hawken, S., Ounpuu, S., Dans, T., Avezum, A., Lanas, F., McQueen, M., Budaj, A., Pais, P., Varigos, J., & Lisheng, L. (2004). Effect of potentially modifiable risk factors associated with myocardial infarctions in 52 countries: Case-control study. Lancet, 364(9438), 937-952.

3 days ago

Carlin Nelson

RE: Discussion 1 – Week 4


Article: Blood Mercury Levels and Neurobehavioral Function

Identify variables:

Independent variable(s): Blood Mercury Levels

Dependent variable(s): Neurobehavioral Function

Confounders: age, race, ethnicity, sex, educational achievement, neurobehavioral testing technician, fish consumption and other potential confounders

What was the research question?

Is there an association between mercury exposure and neurobehavioral outcomes?

Why was Multiple Linear Regression used?

Multiple linear regression is a statistical test that attempts to predict the dependent variable based on two or more independent variables (Laerd Statistics, n.d.). For this test to be the most appropriate to use, there are eight assumptions that should be met or rectified if violated. The assumptions are: 1) dependent variable existing on a ratio/interval level, 2) two or more independent variables which can exist on any level (nominal, ordinal, interval, ratio), 3) independence of observations, 4) linearity between dependent variable and each independent variable, 5) homoscedasticity, 6) multicollinearity, 7) no outliers, and 8) normal distribution (Laerd Statistics, n.d.). Multiple Linear Regression was used because the authors aimed to describe the relationship between mercury exposure and 20 different neurobehavioral tests while controlling for various covariates (Well et al. 2005).

What was the main result(s)?

The authors found that there was an association between increased blood mercury with worse performance on the Rey complex figure an exam which measures the ability to recall (β=-0.224, 95% confidence interval= -0.402, -0.047). While there was a decrease in recall abilities there was an increase in finger tapping and Purdue pegboard (β=0.351, 95% confidence interval=0.017-0.686). In comparing the two model it also appears that the decrease in recall abilities remained statistically significant (p-value=0.01) whereas Purdue pegboard loss statistical significance (p-value=0.13).

What was the interpretation?

For each level increase in blood mercury levels, there was a statistically significant decrease in recall ability as measured through the Rey complex figure. Furthermore, for every increase in blood mercury level, there was an increase in finger tapping, a test of manual dexterity as measured by the Purdue pegboard.

What are your thoughts on the limitation(s) of the study?

There were a few limitations reported in this study. The first limitation conveyed concerned the research design being a cross sectional study. Cross Sectional studies are vulnerable because they only capture a relationship in a snapshot or specific period of time not allowing for causality or temporality. This is an important limitation because it allows the reader to understand context. The second limitation stated in this study focused on the data collection method. Fish consumption was self-reported with a questionnaire and it may not have truly captured omega fatty acids. This limitation is significant because while there is validity to the association to mercury and fish consumption, this may encourage future researchers to incorporate specific type of questions to capture omega fatty acids.

The third limitation emphasize the difference in timing of collecting data. The authors captured fish consumption at the second visit but blood mercury levels were collected at the first visit, this may not have truly captured the association. The last limitation noted concentrated on the generalizability of the study/results due to the subsample. The subsample consisted of higher education, higher fish intake and higher assets. When comparing this subsample to the original sample the Baltimore Memory Study, there may be an external validity issue since the subsample may not truly be representative to the population.


Laerd Statistics. (n.d.). Multiple regression analysis using SPSS Statistics.

Well, M., Bressler, J., Parsons, P., Bolia, K., Glass, T. & Schwartz, B. (2005). Blood mercury levels and neurobehavioral function, JAMA, 293(15). 1875-1882


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