Pho.sphhp.buffalo.edu

University at Buffalo
The State University of New York
School of Public Health and Health Professions “Dedicated to improving health through population based research.” Acknowledgement
Primary Study Investigator
Contributors
This paper focuses on the relationship between hypertension and non-steroidal anti- inflammatory medications. Research has indicated that non-steroidal anti-inflammatory drugs increase blood pressure, and in some cases the magnitude of the increase is clinically significant. The data used for this paper are from the National Center for Health Statistics. The data from the National Hospital Ambulatory Medical Care Survey and the National Ambulatory Medical Care Survey were downloaded from the CDC’s website, and concatenated as they both contained the necessary information. Information regarding these data sets is available a /about/major/ahcd/ahcd1.htm. Both data sets used a four-stage probability sample, and the fields of interest were demographics, diagnoses, medications prescribed, and blood pressure (systolic The background for this paper is that research has shown that non-steroidal anti- inflammatory medications increase blood pressure. Particular classes of NSAIDS of interest are ibuprofen, naproxem, toradol, celecoxib, and rofecoxib. This paper attempts to answer the question of how NSAIDS and a diagnosis of hypertension, and the interaction between these two factors, affect systolic and diastolic blood pressure. The NHAMCS and NAMCS data set were downloaded from the NCHS’s website. Both the outpatient and emergency data sets were used from the NHAMCS survey, as the instructions in regards to sample design instructed. The software package SAS was used in this analysis. The procedure PROC SURVEYREG was used to perform the analysis, which is a linear regression procedure that allows for the inclusion of sample design variables. The sample design variables for cluster (cpsum), for strata (cstratm), and for weight (patwt) were included in the data sets, and were appropriately included in the SAS procedure, documentation is available at the siteThe actual linear model fitted was: BP   0  1* HTN   2 * NSAID   3* (HTN * NSAID)   4 * RACE   5 * SEX   6 * AGE where terms are defined in the following table and subsequent paragraph. Also, note that HTN stands for a diagnosis of hypertension, NSAID refers to an individual being prescribed one or more NSAID medications, HTN*NSAID refer to the four possible combinations of the previous two variables, the RACE variable has four levels (white, African-American, Hispanic, and other), the sex variable has two levels, and age is integer valued but is considered in the model as continuous. This model was fitted for both systolic and diastolic blood pressure. The analyses involved computing average least square estimates for blood pressure given distinct combinations of the HTN and NSAID variables. The method for computing the estimates involved the summation of the predicted value of blood pressure for each distinct combination of race, sex, and age and then division by the total number of combinations as follows : 0  1*HTN  2* NSAID  3*(HTN * NSAID)  4*RACE  5*SEX  6* AGE and then this quantity is divided by the total number of summations performed (note that a represents age, s represents sex, r represents race. In terms of the types of the variables, the In regard to both the model for systolic and diastolic blood pressure, all of the predictor variables have significant p values (<0.055), except for the interaction term, in both cases, and NSAID for DBP. We can conclude that while the NSAID variable and HTN variable have a significant effect on blood pressure, the interaction between these two factors does not significantly affect blood pressure. In other words the extent to which non-steroidal medications increase blood pressure is statistically similar in both the hypertension and non-hypertension groups. The following graphs visually illustrate this relationship. Table 2 : Model Effects for Diastolic Blood Pressure Table 3 : Model Effects for Systolic Blood Pressure Table 4: Parameter Estimates for Diastolic Model Table 4 : Parameter Estimates for Systolic Model Graph 1: Interaction Effect for Diastolic Model: Graph 2 : Interaction Effect for Systolic Model : This analysis argues that the effect of non-steroidal anti-inflammatory medications is independent of a diagnosis of hypertension. That is the effect of NSAIDS on systolic blood pressure is essentially the same in both the hypertension and non-hypertension groups. The analysis, however, indicates that non-steroidal anti-inflammatory drugs do significantly increase systolic blood pressure. These results suggest that NSAID usage is associated with increase systolic blood pressure independent of hypertensive status.

Source: http://pho.sphhp.buffalo.edu/training/students/reports/NSAID_report.pdf