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ORIGINAL ARTICLE
Year : 2019  |  Volume : 63  |  Issue : 2  |  Page : 101-106  

Concordance between two versions of world health organization/international society of hypertension risk prediction chart and framingham risk score among postmenopausal women in a rural area of Bangladesh


1 Lecturer, Department of Noncommunicable Diseases, Bangladesh University of Health Sciences, Dhaka, Bangladesh
2 Assistant Professor and Head, Department of Noncommunicable Diseases, Bangladesh University of Health Sciences, Dhaka, Bangladesh
3 Assistant Professor, Department of Noncommunicable Diseases, Bangladesh University of Health Sciences, Dhaka, Bangladesh
4 Honorary Professor, Department of Biochemistry and Cell Biology, Bangladesh University of Health Sciences, Dhaka, Bangladesh

Date of Web Publication18-Jun-2019

Correspondence Address:
Lingkan Barua
Department of Noncommunicable Diseases, Bangladesh University of Health Sciences, 125/1 Darus Salam, Mirpur-1, Dhaka-1216
Bangladesh
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Source of Support: None, Conflict of Interest: None


DOI: 10.4103/ijph.IJPH_178_18

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   Abstract 


Background: Prevention of cardiovascular disease (CVD) among postmenopausal women with limited resource is a great challenge for a country like Bangladesh. Objectives: This study aimed to evaluate the level of agreement among different risk prediction tools to find out the cost-effective and suitable one that can be applied in a low-resource setting. Methods: This was a cross-sectional study conducted from February through December 2016 among 265 postmenopausal women of 40–70 years age. Data were collected from the outpatient department of a rural health-care center situated in the village Karamtola of Gazipur district, Bangladesh. The CVD risk was estimated using the World Health Organization/International Society of Hypertension (WHO/ISH) “with” and “without” cholesterol risk charts and the Framingham Risk Score (FRS). Concordance among the tools was evaluated using Cohen's kappa (κ), prevalence-adjusted bias-adjusted kappa (PABAK), and first-order agreement coefficient (AC1). Results: The “without” cholesterol version showed 79% concordance against the “with” cholesterol and 75.4% concordance against the FRS. In between the WHO/ISH risk charts, slight-to-substantial levels of agreement (κ = 0.14, PABAK = 0.58, and AC1 = 0.72; P = 0.023) were observed. With FRS, the “without” cholesterol version showed higher agreement (κ = 0.38, fair; PABAK = 0.50, moderate; and AC1 = 0.60, moderate; P = 0.000) compared to “with” cholesterol version (κ = 0.13, slight; PABAK = 0.30, fair; and AC1 = 0.44, moderate; P = 0.013). Predictability of CVD risk positive (≥10%) cases was similar for both the versions of WHO/ISH risk charts. Conclusion: In a low-resource setting, the “without” cholesterol version of WHO/ISH risk chart is a good option to detect and target the population with high CVD risk.

Keywords: Bangladesh, cardiovascular risk assessment, concordance, Framingham Risk Score, postmenopausal women, World Health Organization/International Society of Hypertension risk charts


How to cite this article:
Barua L, Faruque M, Banik PC, Ali L. Concordance between two versions of world health organization/international society of hypertension risk prediction chart and framingham risk score among postmenopausal women in a rural area of Bangladesh. Indian J Public Health 2019;63:101-6

How to cite this URL:
Barua L, Faruque M, Banik PC, Ali L. Concordance between two versions of world health organization/international society of hypertension risk prediction chart and framingham risk score among postmenopausal women in a rural area of Bangladesh. Indian J Public Health [serial online] 2019 [cited 2019 Aug 25];63:101-6. Available from: http://www.ijph.in/text.asp?2019/63/2/101/260596




   Introduction Top


The global burden of cardiovascular diseases (CVDs) is increasing rapidly among the women compared to men.[1] It has documented that the mortality burden of CVD will increase by 120% for the women of all developing countries, and in the next two decades, it will triple among them.[2] Besides these, due to increase of life expectancy, the numbers of postmenopausal women will increase from 467 million in 1990 to 1200 million in 2030.[3] Hence, understanding the postmenopausal impact on women's health is becoming gradually important.

Menopause is a physiological phase of women's life, which is characterized by central adiposity, abnormal lipid profile, and increase of sympathetic tone due to alteration of sex hormones, especially estrogen.[4] Subsequently, all of these predispose to develop intermediate risk factors of CVD, namely obesity, diabetes, and hypertension. It is important that both hypertension and diabetes are important risk factors with greater relative risk to develop CVDs.[5]

In Bangladesh, the usual age of menopause is 45–55 years and the life expectancy of women is 70.3 years that means one-third of their life passes in postmenopausal period.[6] However, their health is almost neglected here and they are not aware about their risk of CVD. Again, no CVD risk prediction tool is still applied and validated among Bangladeshi postmenopausal women, neither in rural nor in urban setting. As Bangladesh is a developing country and there are resource constraints, search for a cost-effective CVD risk assessment tool is in need and absolutely recommended. Hence, this study aimed at evaluating the concordance level between the Framingham Risk Score (FRS) and the World Health Organization/International Society of Hypertension (WHO/ISH) risk charts among postmenopausal rural women of Bangladesh.


   Materials and Methods Top


This was a cross-sectional study conducted from February to December in 2016, among the postmenopausal women who were visited the outpatient department of a rural health-care center situated in the village Karamtola of Gazipur district. A total of 265 postmenopausal women of 40–70 years old were selected through convenient sampling technique who categorized as having “no CVDs” based on self-reported statement, clinical history, and medical records' review. Those presented with an acute illness were excluded from the study. Menopausal status was defined as no menstrual bleeding for at least 12 months and no other clinical condition causing amenorrhea.[7] The sample size was determined using a CVD risk prevalence resulted from a study conducted among postmenopausal women of Nigeria.[8] Data were collected through face-to-face interview of the outdoor patients using a standardized pretested questionnaire following informed written consent. Based on the objectives and variables of the study, behavioral information was collected following STEP-wise approach to Surveillance of Noncommunicable Diseases (NCDs) risk factors (STEPS) questionnaire of WHO with appropriate modification.[9]

Physical measurements (height, weight, waist circumference, hip circumference, and blood pressure [BP]) were carried out following standard method described in “noncommunicable disease risk factor survey Bangladesh 2010,” and the values were recorded in the checklist.[10] Generalized obesity was determined by body mass index according to the international guideline, and it was derived as weight (kg)/height in square meter.[11] Again, central obesity was determined by waist–hip ratio (WHR) measured by a female assistant with maintaining adequate privacy. Central obesity was defined as WHR >0.85 for women.[12] Hypertension was diagnosed based on the “Seventh Report of the Joint National Committee on the Prevention, Detection, Evaluation, and Treatment of High Blood Pressure (JNC 7)” criteria when systolic BP was ≥140 mmHg and/or diastolic BP was ≥90 mmHg or a person was a known hypertensive or on antihypertensive drug.[13] At the end of this stage, the participants of the study had undergone recommended blood tests in the laboratory.

Oral glucose tolerance test for nondiabetics and only fasting blood glucose for diabetic participants were measured by a standard method. In this study, diabetes population comprised newly diagnosed diabetes using WHO criteria (fasting plasma glucose ≥ 7.0 mmol/l or 2-h plasma glucose ≥ 11.1mmol/l) and self statement of a person as known diabetic or on anti diabetic medication.[14] Hypercholesterolemia was defined by total blood cholesterol level ≥240 mg/dl.[15]

Ethical approval to conduct the study was taken from the ethical review committee of the corresponding institution. Report of the blood test and CVD risk category were delivered to the participants after 3 days. If any life-threatening condition detected in a participant, she was referred to a secondary or tertiary health-care center to treat the condition.

Data processing, analysis, and cardiovascular disease risk calculation

Statistical analysis was performed using the computer software Statistical Package for Social Science version 20.0 for Windows (SPSS, Inc. Chicago, IL, USA). Categorical data were expressed as the frequency with percentage, and continuous variables were calculated as mean ± standard deviation; CVD risk was expressed as percentage and difference among the risk classification was presented using inferential error bar at 95% confidence level. Concordance among the tools was evaluated using Cohen's kappa (κ), prevalence-adjusted bias-adjusted kappa (PABAK), and first-order agreement coefficient (AC1). In addition to Cohen's kappa, we used PABAK and AC1 as kappa is highly influenced by the prevalence and bias of the two tools. To overcome the limitations of kappa and more clarification of agreement among the tools, we also reported bias index (BI) and prevalence index (PI). Here, PABAK adjusted the imbalances caused by differences in the prevalence and bias. On the other hand, AC1 overcomes the well-known paradoxes (low kappa at high agreement and high kappa at unbalance marginal distribution) in Cohen's kappa, and it is not affected by bias at all. The BI ranges from 0 to 1, with 0 indicating no bias and 1 implying that one tool never identifies the condition whereas the other tool always does. The PI also ranges from 0 to 1, with 0 indicating the prevalence of the condition is 50% and 1 suggesting the prevalence of the condition is 0 or 100%.[16],[17] In our study, the κ value was considered statistically significant at a threshold of P < 0.05, and the levels of agreement were interpreted using Landis and Koch's approach.[18]

In this study, cardiovascular risk was evaluated using both the versions of WHO/ISH risk prediction chart and FRS. By the WHO/ISH risk prediction chart, approximate estimates of CVD risk in asymptomatic people can be measured. We used Southeast Asian region: D of the WHO/ISH risk prediction chart to calculate the CVD risk among the study subjects. According to this tool, there were two sets of risk prediction charts – “with” and “without” blood cholesterol. Both sets require data on sex, age in years, systolic BP in mmHg, smoking status, and the presence or absence of diabetes mellitus to estimate the risk of a cardiovascular event in the next 10 years. In this study, we used four risk categories: low, <10%; moderate, 10%–19.9%; high, 20%–29.9%; and very high risk, ≥30%.[19] Both the versions of WHO/ISH chart were calculated at different risk levels assuming the “with” cholesterol as the reference or gold standard to define “overestimate” and “underestimate.” On the other hand, FRS was used as reference when both the versions of WHO/ISH risk chart were calculated separately at different risk levels against the FRS to determine the level of agreement between the two tools.

The cardiovascular risk assessment based on the FRS (version 2008) includes gender, age, systolic BP, total blood cholesterol, high-density lipoprotein, smoking status, treatment of hypertension, and the presence or absence of diabetes mellitus. For each risk factor category, there was a risk point which was different for men and women. According to gender, the individual scores were obtained by summing up the risk points for each risk factor. Then, using the total risk point, CVD risk (%) was determined. If the points obtained were below 10%, it was considered as low risk, within 10%–20% as intermediate risk, or above 20% as high risk. However, in this study, we used online version of FRS to calculate and categorize the CVD risk automatically.[20]

To evaluate agreement among the tools, we reclassified the risk category into dichotomize variables as risk positive and risk negative. Here, the “risk positive” included those who were at moderate-, high-, or very-high-risk level and risk negative participants were those detected as low-risk level. In our study, if CVD risk was classified as “risk positive” by the tested tool (presented in the column) against the “risk negative” row of the reference tool, we considered it as an overestimate. In the same way, if CVD risk was classified as “risk negative” by the tested tool against the “risk positive” row of the reference tool, we considered it as an underestimate.


   Results Top


Distribution of individual risk factors used in cardiovascular disease risk assessment tools

The mean age of the participants was 53.5 ± 7.47 years and almost all of the risk factors were highly prevalent among the 50–59 years' age group women. Among the cardiovascular risk factors, central obesity was highly prevalent (73.2%), followed by tobacco use (46.4%), hypertension (41.9%), high cholesterol level (24.9%), and diabetes mellitus (20.4%) (data not presented in the tables).

Distribution of “absolute” cardiovascular disease risk using the World Health Organization/International Society of Hypertension risk prediction charts and the Framingham Risk Score individually

Ten-year absolute CVD risk levels are presented in [Figure S1]. In both the tools, most of the participants remained below 20% risk level. When we use the “with” cholesterol version of WHO/ISH risk prediction chart, 94.7% of the postmenopausal women were found <20% CVD risk which was 95.5% using “without” cholesterol risk chart. Again, ≥20% (high and very high) risk group constitute 5.2% of the participants using “with” cholesterol version which was 4.6% when “without” cholesterol version used. However, compared to both the versions of WHO/ISH chart, the FRS included more participants (7.2%) in high CVD risk group. According to [Figure 1], a significant risk difference was present between risk-positive (moderate, high, and very high risk) and risk-negative (low risk) group of population.
Figure 1: Distribution of 10-year absolute cardiovascular risk classification among postmenopausal women using both the versions of WHO/ISH SEAR D risk chart and Framingham Risk Score (n = 265)

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Inferential error bars are representing the magnitude of difference in various CVD risk classification (low, moderate, high, and very high) and presented at 95% confidence interval. The risk classification is presented along the horizontal axis and percentage of each risk category is presented along the vertical axis. Bar of the low-risk category shows no overlapping with rest of the risk categories. Hence, there is a possibility of significant risk difference between CVD risk-positive group (moderate, high, and very high) and CVD risk-negative group (low risk).

Concordance of World Health Organization/International Society of Hypertension risk prediction charts and Framingham Risk Score

In this study, concordance was found between the two charts for 209 (78.9%) participants [Table 1]. Using the with cholesterol version as the gold standard, the chart without cholesterol overestimated CVD risk in 27 (10.2%) participants and underestimated risk in 29 (10.9%) participants. Slight agreement was found for “with” cholesterol and “without” cholesterol versions using kappa statistics (κ = 0.116; P = 0.013). However, PABAK showed moderate and AC1 showed substantial agreement between these two tools.
Table 1: Concordance of the World Health Organization/International Society of Hypertension Southeast Asian region D cardiovascular disease risk chart with and without cholesterol versions among postmenopausal women (n=265)

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Again, using the FRS as gold standard, concordance was assessed with both the versions of WHO/ISH risk tool separately. Concordance was found between FRS and “with” cholesterol version for 173 (65.3%) participants [Table 2] and “without” cholesterol version for 199 (75.1%) participants [Table 3]. Among the WHO/ISH risk charts, both the overestimation and underestimation were higher in the “with” cholesterol version (28.3% and 6.4%, respectively) compared to “without” cholesterol version (23% and 1.9%, respectively). With FRS, slight agreement was found for “with” cholesterol version (κ = 0.122; P = 0.002) and fair agreement was found for “without” cholesterol version (κ = 0.284; P = 0.000). When we used PABAK and AC1, the “without” cholesterol version showed a moderate (PABAK = 0.50 and AC1 = 0.60) agreement, which was also better than the PABAK (0.30) and similar to the AC1 value (0.44) of “with” cholesterol version.
Table 2: Concordance of the World Health Organization/International Society of Hypertension Southeast Asian region D Cardiovascular disease risk chart with cholesterol version and Framingham Risk Score among postmenopausal women (n=265)

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Table 3: Concordance of the World Health Organization/International Society of Hypertension Southeast Asian region D Cardiovascular disease risk chart without cholesterol version and Framingham Risk Score among postmenopausal women (n=265)

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   Discussion Top


Application of three different tools on the same population showed that overall 17% of postmenopausal women are at high risk (>20%) of a CVD event in the next 10 years and about 10% of them are identified with the two versions of WHO/ISH risk chart, and the rest are by FRS. Proportion of the detection of high-risk group is similar whether the “with” or “without” cholesterol version is applied. The total proportion of high risk (other than low and moderate risk) identified by the FRS and WHO/ISH together is much higher than that of other two studies of Bangladesh conducted among general population in rural areas.[21],[22] In consideration of the use of both the versions, the current risk prediction in “high-risk” category coincides with the first one.[21] However, in consideration of only female participants, “high-risk” proportion of the current study is half (9.8%) of the first study (17.8%). Again, when we consider only “without” cholesterol version, the proportion of “high risk” in the current study is twice (4.6%) of the second study (2.1%) for Bangladeshi rural population.[22] In case of CVD risk positivity by the WHO/ISH risk tools, findings of the current study (14.4%) did not differ so far with the first study (18.6%) for female participants.

Another CVD risk tool, FRS in this study, detected 36% of the postmenopausal women as the 10-year absolute CVD risk positive which is much higher than that of the WHO/ISH risk tools, however it was half of the recently reported another study of Bangladesh that used FRS to predict CVD risk among rural women.[23] The exact cause of these risk differences in different population group in the same geographical area is unknown, but the possibility includes variation in sociodemographic factors, cultural background, environmental factors, and genetics of the participant.

Bangladesh is situated in Southeast Asian region and is classified as lower-middle-income country. Like Bangladesh, another lower-middle-income country, Mongolia, detects 8.8% women aged 40–60 years as CVD risk positive using both the versions of WHO/ISH chart, which is nearly half of the current study.[24] Actually, in Southeast Asian region, no study is available that conducts CVD risk prediction using the WHO/ISH risk charts and FRS among postmenopausal women. Available studies among the countries of the Indian subcontinent (India, Pakistan, Bangladesh, Sri Lanka, and Nepal) using the WHO/ISH tool reveal that only women of Pakistan and India have similar CVD risk profile as the current study.[25],[26] Nevertheless, compared to Sri Lanka and Nepal, more women in the current study were identified with high CVD risk and 10-year absolute CVD risk positive by the WHO/ISH tools. In other regions of the world, CVD risk prediction is also lacking among postmenopausal women using the WHO/ISH risk tools. A cross-sectional study conducted among Argentine postmenopausal women with WHO/ISH chart of cholesterol version showed that the proportion of high risk of CVD is less than that of the current study.[27] Regarding application of FRS among Asian postmenopausal women, two studies of Iran showed that most of the participants within low-risk group and the proportion of CVD risk-positive respondents are less than the current study.[28],[29] However, surprisingly, the proportion of “high risk” for CVD is much higher among the postmenopausal women of less developed African countries compared to this study.[8],[30],[31]

The main objective of this study was to justify the level of agreement between both the versions of WHO/ISH risk chart each other and with FRS individually. However, comparison between FRS and WHO risk tool is difficult as globally data regarding their concordance are absolutely lacking and inconsistent. In this regard, concordance of this study between the two versions of WHO/ISH risk chart is 78.9% among the postmenopausal women, which is less than the concordance found among the women (90.6%) of another Bangladeshi rural study mentioned before.[21] Slight agreement is found between the two versions of WHO/ISH tool. On the other hand, “without” cholesterol version is fairly agreed with FRS, where “with” cholesterol shows slight agreement. One Malaysian study detected the same level of agreement (κ = 0.11) as the current study between FRS and WHO “with” cholesterol version.[32] The above-mentioned Bangladeshi study[21] and Malaysian study[32] reported Kappa as their key measurement which is not a valid method for reporting concordance among the tools. This is because there is a phenomenon known as kappa paradox occurs when the observed proportion of agreement is high but the kappa value is low. This low kappa value is misleading in the presence of kappa paradox. Hence, researchers suggested reporting additional values in addition to kappa to provide a clear picture of agreement.[33] In the current study, the observed proportion of agreement (P0, not presented in tables) is high [Table 1], P0= 0.788; [Table 2], P0= 0.652; and [Table 3], P0= 0.750] and the kappa value is low [Table 1], κ = 0.14; [Table 2], κ = 0.13; and [Table 3], κ = 0.38]. Hence, we reported our agreement using PI, BI, PABAK, and AC1 in addition to kappa. In comparison to kappa, use of PABAK and AC1 shows progressive improvement of agreement levels among the tools which is similar to the mentioned Bangladeshi study and better than the Malaysian study. Overestimation and underestimation is similar for “without” cholesterol version against the “with” cholesterol as gold standard. On the other hand, the “without” cholesterol version overestimated and underestimated less number of participants compared to “with” cholesterol version when FRS is considered as gold standard. Besides this, the current study shows that the predictability of high CVD risk is similar in both FRS (7.2%) and WHO/ISH risk prediction charts (9.8%) using “with” and “without” cholesterol versions together. Moreover, the “without” cholesterol version and the “with” cholesterol version showed a similar risk prediction ability (14.7% CVD risk positive vs. 14% CVD risk positive, respectively). Therefore, CVD risk prediction using “without” cholesterol version of WHO/ISH chart is a feasible option to apply in a low-resource setting.

Several factors may influence the results of this study. The results may not be generalized due to relatively small sample size. The study design was cross-sectional which limits the ability to draw inferences regarding trends and causality. Again, due to this cross-sectional design, the risk prediction ability of a tool could not be evaluated.

Other than these limitations, the study is important, as it is the first study that predicted CVD risk among the postmenopausal women of Bangladesh in a rural setting. Moreover, first time, the WHO/ISH “with” and “without” versions of CVD risk prediction charts are compared with a gold standard CVD risk tool (FRS) in Bangladeshi population. From statistical point of view, use of several measures to verify and compare the rate of concordance among the tools also increases the strength of the study.


   Conclusion Top


In the current study, the “without” cholesterol version of WHO/ISH risk charts showed high concordance against the “with” cholesterol version and FRS. It indicates the applicability of “without” cholesterol version of WHO/ISH risk chart in a minimal resource setting to estimate CVD risk and thereby saving a greatest number of lives at lowest cost.

Financial support and sponsorship

Nil.

Conflicts of interest

There are no conflicts of interest.



 
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