A random-digit-dialing, community-based survey identified 430 respondents who qualified to participate in the study. Table 1 shows the demographic characteristics of the study sample. The mean age was 51.9 years. Overall, the racial and ethnic breakdown consisted of 38.1% African-American, 44.9% Latina and 17.0% of other racial and ethnic groups. A majority (60.9%) of the sample had a high-school education or less. Nearly half (46.7%) of the sample reported total annual household incomes of <$20,000. Nearly two-thirds (64.4%) of the sample reported having some form of health insurance and the sample was almost evenly split between being married and not married (51.9% vs. 48.1%).

Data analyses revealed that the intervention and comparison group assignees were comparable in reference to all variables measured at baseline, suggesting that our randomization procedure was effective. African Americans comprised 40.6%, Latinas 41.1%, and respondents of other racial and ethnic groups comprised the remaining 18.3% of the intervention group.
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One goal of the study was to test the hypothesis that the mean screening mammography utilization rates of the participants assigned to the intervention (36.8%o) and control groups (29.0%) were equal. A difference of 7.8% was detected that was found not to be statistically significant; consequently, the null hypothesis was accepted. A small sample size may explain our inability to detect statistically significant differences between the two study groups. Power calculations suggests that a total sample of 1,080 (540 participants in each group) would have been required to detect a statistically significant difference between the means of the two groups with a=0.05 and p=0.80.

Having a screening mammogram during the time interval between baseline and the six-month follow-up assessment was the main outcome variable of interest in this study. This self-reported behavior was obtained by telephone during the six-month follow-up interview. Variables in Table 3 were included in Chi-squared analyses to determine associations between the dependent variable (obtaining screening mammograms at follow-up) and all potential covari-ates. All continuous predictor variables were dichotomized for bivariate analyses. Groups were divided into roughly equal sizes. Study participants were asked during telephonic recruitment which race/ethnic group described them best. A list of possible responses were read, including: a) African-American, b) Asian/Pacific Islander, c) Caucasian/ white, d) Hispanic/Latina (specify), e) Native American, f) other (specify), (g) don’t know or h) refused to identify. All study participants provided a self-identification of their racial or ethnic group. For analytical purposes, race and ethnicity are categorized as African-American (38.1%), Hispanic/Latina (44.9%) and other (17.0%). This racial and ethnic distribution is representative of the study area. Viagra Soft Tabs

Table 3. A comparison of study participants who had a screening mammograms during the six-month follow-up period (n=117) and those who did not (n=237). Seventy-six participants were lost to follow-up at six months.

Bivariate analyses revealed the following variables to be statistically significant at p<0.05: doctor recommended having a mammogram, doctor recommendation caused me to get a mammogram, my knowledge of the age to begin having regular mammograms, ever having a mammogram, likelihood of getting a mammogram in the next 12 months and having a professional breast examination. female viagra online

Table 3 continued

Multiple logistic regressions were conducted to examine the independent impact of each covariate in determining the likelihood of obtaining mammograms during the intervention period (Table 4). Tests of collinearity were conducted among the variables that were found to have significant associations during the bivariate analyses. These tests found strong associations between professionals’ recommendations to get mammograms and “doctors’ recommendations caused me to get mammograms”. Consequently, professionals’ recommendations to get mammograms were deleted from the equation. Four factors emerged as significant predictors of women getting screening mammograms in the multiple logistic regressions analyses. These factors included: 1) age, 2) study group, 3) prior mammograms, and 4) knowledge of the age that a woman should begin getting mammograms on a regular basis. Cialis Jelly

Table 4. Linear multiple logistic regression analysis of having a screening mammograms during the six-month follow-up

Younger participants were two times more likely to report having mammograms during the follow-up period than older participants (p<0.05, OR=2.22, CI0.98-5.02), and participants in the intervention group were nearly twice as likely to report having mammograms as their counterparts in the comparison group (p<0.05, OR=1.76, CI 1.06-2.92). Participants who reported having prior mammograms at baseline were 2.5 times more likely to report having mammograms during the follow-up period as those who reported never having mammograms (p<0.05, OR=2.51, CI 1.39-4.56), and participants who reported that a woman should begin having mammograms regularly before the age of 40 years were slightly over more than half as likely to report having mammograms during the follow-up period as those who reported that a woman should start getting mammograms regularly at the age of >40 years (p<0.05, OR=0.55, CI 0.33-0.92).  levothyroxine medication