Female sex and cardiovascular disease risk in rural Uganda: a cross-sectional, population-based study

We conducted a cross-sectional study to describe AHA ideal CVH metrics in southwestern Uganda. Participants were community-dwelling adults, residing in Nyakabare Parish, Mbarara District, who attended one of five voluntary health fairs in June 2015. The parish is characterized by a subsistence pastoral-agrarian economy in which both food and water insecurity are common [15]. It is also the location of an ongoing longitudinal study that conducted a census in 2014 and collected data on 98% of all adults (1814/1851) residing there. Participation in the health fair study was limited to participants in the parent census study. Recruitment was enriched through radio advertisements and announcements at social and religious gatherings. The institutional review boards of Mbarara University of Science and Technology, Uganda, and Partners Healthcare, Boston, approved the protocol, and all study participants gave written informed consent. Consistent with national guidelines, we also obtained clearance for the study from the Ugandan National Council of Science and Technology and from the Research Secretariat in the Office of the President.

Surveys were administered to elicit age; sex; educational attainment; medical history; and active medication use for hypertension, dyslipidemia, heart failure, asthma or diabetes mellitus. We assessed socioeconomic status using household-owned assets and housing characteristics aggregated into an asset wealth index and divided in tertiles [16]. Questionnaires based on the International Physical Activity Questionnaire (IPAQ) [17] and the WHO STEPS instrument [18] were administered to collect data on physical activity, tobacco use, and fruit and vegetable intake. Physical activity was measured as metabolic equivalent of task (MET) in minutes per week [17].

We also measured height, weight, and BP. BP was measured in a seated position using automated sphygmomanometers (Omron HEM 705 LP, Omron Healthcare, Inc., Bannockburn, IL). Venous blood was collected to assess serum lipids, C-reactive protein (CRP), and performed at Epicentre Research Base in Mbarara, Uganda using a Cobas c111 (Roche, Basel, Switzerland) serum chemistry analyser and point-of-care glycated haemoglobin (HbA1c) (Siemens DCA Vantage, Munich, Germany).

We categorized each of the seven CVH metrics defined by the AHA as poor, intermediate, or ideal based on published guidelines [1]. For our primary analyses, we added CRP as an additional metric, due to its strong correlation with CVD risk in multiple populations [19], allowing for a total of eight categories in our ideal CVH metrics score (Table 1). We also assigned two AHA sub-categories of ideal CVH, based on AHA recommendations, as: (1) five ideal CVH factors, which included BP, TC, HbA1c, CRP and BMI; and (2) three ideal CVH behaviors, which included diet, physical activity, and smoking.

We characterized the CVH metrics as poor, intermediate, or ideal as indicated in Table 1. In place of plasma fasting glucose in the standard AHA score, we measured HbA1c, and selected the three categories based on international consensus [20] HbA1c thresholds of < 5.7%, 5.7–6.4% and ≥ 6.5%, respectively, as cut-offs for normal, pre-diabetic, and diabetic states. CRP was categorized as ideal (< 1.0 mg/dL), intermediate (1-3 mg/dL), and poor (≥3 mg/dL) corresponding to standard definitions of CRP and CVD risk [21]. Complete data were available for only one of the five AHA components used to score the diet metric. We therefore used fruit and vegetable intake to define ideal (≥20 servings/week) and non-ideal (< 20 servings/week) diet as previously described [22]. Physical activity was converted to metabolic equivalents of task (METs) in minutes, and categorized as ≥1500 MET min/week for ideal, 600–1500 MET min/week for intermediate, and < 600 MET min/week for poor physical activity [17].

A total of 857 individuals attended a health fair out of 1814 (47%) cohort participants. Analyses were confined to 756 (42%) participants with complete data to calculate all 7 AHA CVH metrics plus CRP. Compared to non-attendees, health fair attendees were older (p < 0.001), more likely to be female (p < 0.001), with less formal educational attainment (p < 0.001). Notably, attendees were twice as likely as non-attendees to report very bad or bad health (1.4% vs. 0.7, and 26.5% vs. 13.1%, respectively, p < 0.001) (Additional file 1: Table S4). There were no significant differences in cardiovascular risk characteristics between health fair attendees who were included or excluded from the analysis due to missing data (Additional file 1: Table S5).

The weighted study population mean age was 39.2 years (1.2), (Table 2). The population was 52.0% female, and 4.1 and 3.3%, respectively, had a history of heart failure or stroke. Compared to men, women were significantly older (41.5 vs. 36.8 years; p = 0.016), were less likely to have formal education (none: 20.1% vs. 5.2%), were more likely to be poor (44.2% vs. 29.1%), and had a higher BMI (mean BMI 26.0 vs. 22.4 kg/m2; p < 0.001). Three times as many women as men self-reported heart failure history (6.3% vs. 1.8%: p = 0.012).

The distribution of individual CVH metrics in the total population, and stratified by age and sex, are shown in Additional file 1: Table S1 and Fig. 1. Figures 2 and 3 depict the distribution of the number of ideal components of cardiovascular health, behaviors and factors by sex. Overall, only 3.2% of the weighted population had all 7 AHA metrics and CRP at ideal CVH levels. Though more women than men had all 8 metrics (CRP plus 7 AHA) at the ideal level (4.4% vs. 1.8%; p = 0.018), men had a higher number of metrics at ideal level out of 8 (5.1 vs. 4.7; p = 0.010) and higher proportion with at least 6 ideal CVH metrics (39.7% vs. 29.0%; p = 0.025) (Additional file 1: Table S2).

Men and women tended to have similar levels of ideal CVH categories for BP and physical activity (Fig. 1). However, men had a higher frequency of BMI, TC, HbA1c and CRP achieving ideal health status than the women. Overall, men had better CVH factor profiles (5 out of 5 metrics at ideal level) than women (16.3% vs. 9.4%; p < 0.001). However, women had the more favorable CVH behaviors profile (3 out of 3 metrics at ideal level) (8.8% vs. 20.4%; p = 0.027) (Figs. 2 and 3).

In univariable models, female sex was associated with worsening CVH status (OR 0.59; 95% CI: 0.40–0.87; p = 0.008) (Table 3). After adjusting for age, asset wealth and education, female sex remained correlated with worse CVH metrics, although this did not achieve statistical significance (adjusted OR 0.70; 95%CI: 0.47–1.03). Interestingly, women were more likely than men to be classified into higher categories of ideal CVH behaviors (adjusted OR 2.87; 95% CI: 1.72–4.78; p < 0.001), but less likely to be classified into higher categories of ideal CVH factors (adjusted OR 0.32; 95% CI: 0.21–0.49; p < 0.001) (Tables 4 and 5). In all models, increasing age was significantly associated with having fewer CVH metrics and factors. In both adjusted and unadjusted models, having some formal education, relative to no education, was positively correlated with ideal CVH behaviors. In contrast, relative household wealth was negatively associated with ideal CVH metrics and ideal health factors. Compared to the poorest quartile of asset ownership, increasing wealth predicted worsening CVH metrics and health factors. However, we found no apparent relationship between relative household wealth and health behaviors. The mean adjusted probabilities of having all 3 behaviors at the ideal CVH level, if age, wealth and educational attainment were equal to mean for all participants, were 18.0% (95% CI: 12.8–23.2%) for women and 7.1% (95% CI: 3.9–10.4) for men. The corresponding adjusted probabilities of having all 5 factors attaining ideal CVH level were 5.8% (95% CI: 3.1–8.6) for women and 16.0% (95% CI: 9.0–23.0) for men (Tables 6 and 7).

There are a number of limitations to our study. The absence of longitudinal data on ideal CVH metrics in our study population prevents us from estimating temporal trends in CVD risk, and confirming whether CVD risk factors predict CVH morbidity or mortality. The lack of population-based cardiovascular outcomes data in Uganda largely precludes such estimation of the clinical and population health implications of our findings. As a cross-sectional study, we are further limited to correlational inferences between ideal CVH metrics, sex/gender and social determinants. Finally, our study used definitions of dietary intake and glucose metabolism that differed than those recommended by the AHA. This should be considered when making direct comparisons with other studies of ideal CVH metrics.

Our study also had important strengths. As a relatively large population-based study, with participants drawn from a complete census, we were able to make population level estimates and generalize our results to similar areas of rural Uganda. Differential participation in the health fairs was accounted for with the use of IPTW-adjusted models to derive population-level estimates. We were also able to include both unique biomedical factors, such as CRP, and social determinants of health, such as wealth, education, and sex to contextualize our findings beyond basic CVD risk estimation.