Socioeconomic differentials in hypertension based on JNC7 and ACC/AHA 2017 guidelines mediated by body mass index: Evidence from Nepal demographic and health survey.

BACKGROUND: Unlike developed countries, higher socioeconomic status (SES-education, and wealth) is associated with hypertension in low and middle-income countries (LMICs) with limited evidence. We examined the associations between SES and hypertension in Nepal and the extent to which these associations vary by sex and urbanity. The body mass index (BMI) was examined as a secondary outcome and assessed as a potential mediator.
MATERIALS AND METHODS: We analyzed the latest Nepal Demographic and Health Survey data (N = 13,436) collected between June 2016 and January 2017, using a multistage stratified sampling technique. Participants aged 15 years or older from selected households were interviewed with an overall response rate of 97%. Primary outcomes were hypertension and normal blood pressure defined by the widely used Seventh Report of the Joint National Committee (JNC7) and the American College of Cardiology/American Heart Association (ACC/AHA) 2017.
RESULTS: The prevalence of hypertension was higher in Nepalese men than women. The likelihood of being hypertensive was significantly higher in the higher education group compared with the lowest or no education group for men (OR 1.89 95% CI: 1.36, 2.61) and for women (OR 1.20 95% CI: 0.79, 1.83). People in the richest group were more likely to be hypertensive compared with people in the poorest group for men (OR 1.66 95% CI: 1.26, 2.19) and for women (OR 1.60 95% CI: 1.20, 2.12). The associations between SES (education) and hypertension were partially modified by sex and fully modified by urbanity. BMI mediated these associations.
CONCLUSIONS: The higher SES was positively associated with the higher likelihood of having hypertension in Nepal according to both JNC7 and ACC/AHA 2017 guidelines. These associations were mediated by BMI, which may help to explain broader socioeconomic differentials in cardiovascular disease (CVD) and related risk factors, particularly in terms of education and wealth. Our study suggests that the mediating factor of BMI should be tackled to diminish the risk of CVD in people with higher SES in LMICs.

Introduction

Hypertension is a growing public health problem in low and middle-income countries (LMICs) with concurrent risks of cardiovascular and kidney diseases [1, 2]. A review warned that although about three-quarters of people with hypertension (639 million people) live in LMICs, there is no improvement in awareness or control rates [1]. Hypertension is a major contributor to death and disability in South Asian countries, including Nepal with a low level of control and awareness [3-6]. The World Health Organization (WHO) implemented `STEP-wise approach to surveillance' (STEPS) using nationally representative sample in 2008 and 2013 reported an increasing trend of prevalence of hypertension among 15–69 years Nepalese population ranging from 21.5% in 2008 to 26.0% in 2013 [7, 8]. Based on the recent Nepal Demographic and Health Survey (NDHS) 2016, Kibria and colleagues reported that the estimated prevalence of hypertension in Nepal using the widely used Seventh Report of the Joint National Committee (JNC7) guideline was 21.2%, and the corresponding prevalence was 44.2% when using a new hypertension guideline recommended by the American College of Cardiology/American Heart Association 2017 (ACC/AHA 2017) [9, 10]. This study demonstrated that the prevalence of hypertension increased to 23% when using new ACC/AHA guideline, with the highest increase in the richest and obese population [11].

Despite an increasing prevalence of hypertension in Nepal, research exploring complex interrelationship between socioeconomic status (SES), indicated by education levels and wealth quintiles, and hypertension is limited. Moreover, this association is complex, unlike developed countries, in LIMCs. For instance, the prevalence of hypertension is higher among low SES groups in developed countries, while it is substantially higher among high SES groups in LMICs [12-15].

The reasons for the high prevalence of hypertension in the low SES group in developed countries include higher smoking rates, higher body mass index (BMI), and lack of exercise compared with higher SES groups [16]. The opposite pattern is observed in LMICs, where a higher prevalence of these risk factors is observed in higher SES groups compared with low SES groups. A recent review found that people in higher SES groups in LMICs were less likely to be physically active and consume more fats, salt, and processed food than low SES groups [17]. Furthermore, studies also found that BMI is exponentially increasing in people in LIMCs, which are the key modifiable risk factors for hypertension [18-20]. Thus, we hypothesized that there would be positive associations between high SES and hypertension in Nepal, and the level of BMI will at least partially mediate these associations. The primary aim of this study was (i) to assess the associations between SES and hypertension in Nepal, and the extent to which these associations vary by gender and urbanity; and (ii) to examine whether BMI attenuates the associations between SES and hypertension and, the extent to which BMI explain these associations. The secondary aim of this study was to examine associations of BMI with SES and the extent to which these associations vary by gender and urbanity.

Materials and methods

Data source

The study analyzed the nationally representative Nepal Demographic and Health Survey (NDHS) 2016 data, collected between June 2016 and January 2017. The Nepal Health Research Council and the ICF International institutional review board approved the NDHS 2016 survey protocol. The household head provided written informed consent before the interview. For the current study, we obtained approval to use the data from ICF in June 2018.

Survey design and study populations

The updated version of the census frame of the National Population and Housing Census 2011 conducted by the Central Bureau of Statistics was used as the sampling frame for the NDHS 2016. The households of the NDHS 2016 were selected in two ways based on the urban/rural locations. Firstly, the two-stage stratified sampling process was used in rural areas where wards were selected in the first stage as a primary sampling unit (PSUs), and households were selected in the second stage. Secondly, three-stage stratified sampling was used in urban areas to select households where wards were selected in the first stage (PSUs), enumeration areas (EA) were selected from each PSU in the second stage and households were selected from EAs in the third stage. There were 14 sampling strata in the NDHS 2016, where wards were selected randomly from each stratum. A total of 383 wards were selected altogether, 184 from urban and 199 from rural areas. Finally, a total of 11,490 households (rural- 5,970 and urban-5,520) were selected for the NDHS 2016 [21]. Flowchart of the analytic sample selection process is given as a supplementary figure (S2 File).

The trained interviewers collected data visiting the households. The overall response rate was approximately 97%. Blood pressure (BP) was measured among 15,163 individuals with 6,394 men and 8,769 women aged 15 years and above. In our study, a total unweighted sample was 13,371 comprising men (5,535) and women (7,836), after excluding participants aged <18 years and discarding the missing and extreme values. The total weighted analytic sample was 13,436 participants (men 5,646 and women 7,790) aged 18 years and above. Details of the NDHS 2016, including survey design, sample size determination, and questionnaires, have been described elsewhere [21].

Measures of outcomes: Blood pressure outcomes

Hypertension and normal blood pressure were considered as the outcome variables in the study defined by both JNC7, and ACC/AHA 2017 guidelines (Table 1) [9, 10]. Three measurements of blood pressure (systolic and diastolic blood pressures) were taken for each participant with an interval of 5 minutes between the measurements by UA-767F/FAC (A&D Medical) blood pressure monitor. Systolic blood pressure (SBP) and diastolic blood pressure (DBP) were defined by taking the average of three SBP and three DBP measurements, respectively. We used both ‘measurement-only’ and ‘medical/clinical’ definitions to generate independent binary outcomes for ‘hypertension’ and ‘normal blood pressure’ based on both guidelines. The ‘measurement-only’ definition was developed solely based on the cut-off points that accounted for the average of three SBP and three DBP measurements. The ‘medical/clinical’ definition accounted for ‘measurement-only’ definition plus medical diagnosis by a health professional as having high blood pressure and/or taking blood pressure-lowering medication (Table 1).

Table 1

Definitions of blood pressure outcome used in the study.

Blood pressure outcomes	Measurement-only definitions	Medical definitions
Hypertension (JNC7)†	SBP ≥140mmHg or DBP ≥ 90 mmHg	Meet any of the following three criteria:(1) SBP ≥ 140mmHg or DBP ≥ 90mmHg(2) Doctor/nurse diagnosed high blood pressure(3) Taking blood pressure-lowering medication
Hypertension (ACC/AHA 2017)‡	SBP ≥130mmHg or DBP ≥ 80 mmHg	Meet any of the following three criteria:(1) SBP ≥ 130mmHg or DBP ≥ 80mmHg(2) Doctor/nurse diagnosed high blood pressure(3) Taking blood pressure-lowering medication
Normal blood pressure (JNC7 or ACC/AHA 2017)	SBP <120mmHg and DBP <80 mmHg	SBP ≤ 120mmHg and DBP ≤ 80 mmHg, no diagnosis of high blood pressure, and not taking blood pressure-lowering medication

†JNC7 = The Seventh Report of the Joint National Committee

‡ACC/AHA 2017 = The 2017 American College of Cardiology/American Heart Association

Measures of exposure: Socioeconomic status

Three indicators such as education levels, wealth quintiles, and employment status are most commonly used in several studies to assess the SES of a participant [12, 15]. However, we omitted employment status from our assessment of SES and subsequent analyses due to a large number of missing values as the majority of the women in South Asia are not involved in the formal employment. The NDHS 2016 provided data for a derived wealth quintile using the principal component analysis taking scores of a household’s durable and nondurable assets. Firstly, households are given scores using principal component analysis based on the number and kinds of consumer goods they own. Secondly, to get the wealth quintiles, the distribution of scores was divided into five equal sizes named as poorest, poorer, middle, richer, and richest. Education was an ordinal measure of self-reported levels of education, which was grouped into four different categories (no education/preschool, primary, secondary, and higher education) in the NDHS 2016. In our study, the SES measures were not indexed for two main reasons. Firstly, different indicators of SES tend to have different theoretical pathways to BMI and blood pressure outcomes. Secondly, SES indicators might be causally related to each other; and they build on each other according to the life course models [22].

Body mass index

The BMI was used in the study as both continuous and categorical variables. We followed both the South-Asian specific and global definition of BMI.

Statistical analysis

Our primary statistical analyses assessed the sex and urbanity stratified associations of educational levels and wealth quintiles with blood pressure outcomes using both the measurement-only and medical definitions. To characterize the shapes of the associations, we calculated sex and urbanity stratified adjusted odds ratios (ORs) and 95% confidence intervals (CIs) within each level of education or wealth quintiles by using the binary logistic regression models. We used a cut off of 10% change in the stratified analysis as well as tested interaction term to identify differences in hypertension by sex and urbanity.

We further tested whether BMI mediates the associations between SES and blood pressure outcomes. We employed the following two approaches for testing mediation effect of BMI: The first approach was the “reduction-in-estimate criterion,” approach- a rule of thumb, which assessed whether the inclusion of mediator variable-BMI attenuated the associations or effects for the main predictors across nested models. Hence, we constructed two nested models stratified by sex, and coefficients were progressively adjusted for age, marital status, urbanity, and second-hand smoking in Models I(a), II(a), III(a). Coefficients were further adjusted for prior determined mediator-BMI in Models I(b), II(b), III(b) to observe changes in the coefficients of predictors. We considered that there is a mediation effect using a cut off of 10% reduction in the effect estimate (coefficients) after adjusting for mediator-BMI in the respective models.

The second step was the “indirect effect” approach, which formally examined the statistical significance of an indirect effect using the product of coefficients approach [23]. For assessing the indirect effect of BMI on these binary outcomes, we used the generalized structural equation modeling (GSEM) in Stata because this approach is commonly used and can detect which variables are continuous and which are binary. It requires information for each link in the proposed mediation process [Mediator Variable (MV) regressed on Independent Variable (IV) and Covariates (CV) and Dependent Variable (DV) regressed on MV, IV, and CV] [24, 25]. In supplemental analyses, we replicated the process for 5000 bootstraps for statistical significance, which provided substantially identical indirect effects along with standard errors and biased-corrected 95% CI for the indirect effect of SES [24].

Additionally, we examined the adjusted associations between SES and BMI as a continuous outcome. Moreover, the adjusted sex and urbanity stratified associations between SES and binary outcome-overweight/obese (using both global and South Asia-specific cut-offs for BMI) were assessed to observe differences in overweight/obesity by sex and urbanity.

For examining the associations between SES and hypertension, all potential confounders for each predictor were selected using prior knowledge and directed acyclic graphs (DAGs) to avoid the ‘Table 2 fallacy’ in a multivariable model and to observe unbiased total effect estimates for predictors [26, 27].

Table 2

Sample characteristics (weighted numbers and percentages unless stated otherwise).

Characteristics	Overall (n = 13 436)	Men(n = 5645)	Women(n = 7790)	p-value
Mean Age (SE, standard error)	40.7 (0.1)	42.59 (0.28)	39.30 (0.19)	< 0.001
Marital Status (%)
Unmarried	1569 (11.7)	872 (15.4)	698 (9.0)	< 0.001
Married	11 867 (88.3)	4 774 (84.6)	7092 (91.0)
Education Levels (%)
No education/preschool	5498 (40.9)	1474 (26.1)	4024 (51.7)	< 0.001
Primary	2281 (17.0)	1194 (21.2)	1087 (14.0)
Secondary	3709 (27.6)	1958 (34.7)	1751 (22.5)
Higher	1947 (14.5)	1020 (18.1)	928 (11.9)
Employment Status (%)
Unemployed	2777 (30.6)	557 (15.9)	2,220 (40.0)	< 0.001
Employed	6287 (69.4)	2956 (84.15)	3331 (60.0)
Wealth Index (%)
Poorest	2405 (17.9)	993 (17.6)	1412 (18.1)	< 0.001
Poorer	2613 (19.5)	1054 (18.7)	1559 (20.0)
Middle	2693 (20.0)	1091 (19.3)	1603 (20.6)
Richer	2936 (21.9)	1280 (22.8)	1656 (21.3)
Richest	2787 (20.8)	1228 (21.8)	1559 (20.0)
Urbanity (%)
Urban	8205 (61.1)	3475 (61.6)	4729 (60.7)	0.27
Rural	5231 (38.9)	2171 (38.6)	3061 (39.3)
Region (%)
Mountain	859 (6.4)	367 (6.5)	491 (6.3)	0.60
Hill	5922 (44.1)	2468 (43.7)	3454 (44.3)
Terai	6655 (49.5)	2811 (49.8)	3844 (49.4)
Established Risk Factors of Hypertension
Mean Systolic Blood Pressure (SE)	117.7 (0.2)	122.02 (0.43)	114.57 (0.38)	< 0.001
Mean Diastolic Blood Pressure (SE)	78.3 (0.1)	79.89 (0.32)	77.17 (0.26)	< 0.001
High Blood Pressure (Told by doctor, %)	1670 (12.4)	763 (13.52)	907 (11.64)	0.004
Medication for Blood Pressure (%)	578 (4.3)	260 (4.61)	318 (4.08)	0.25
Mean Body Mass Index (SE)	22.1 (0.0)	21.08 (0.72)	22.28 (0.10)	< 0.001
Exposure to Secondhand Smoking (%)	6308 (47.0)	2718 (48.2)	3589 (46.08)	0.003
Consumption of Caffeine (%)	1058 (8.0)	581 (10.3)	477 (6.12)	< 0.001

For the brevity, we have reported ‘measurement-only definition’ of hypertension/normal blood pressure, if not stated otherwise, especially when we assessed associations between hypertension/normal blood pressure and SES. However, similar analyses for ‘medical definition’ of hypertension have been provided as supplementary data (S1 File). Comparable analyses based on the new guideline of ACC/AHA 2017 have also been given as supplementary data. Two-sided P-values and 95% CIs are presented. The complex survey design effects were accounted in all performed analyses for reducing differences due to oversampling, variation in the probability of selection and non-response in the NDHS 2016. All analyses were performed using Stata 15 (StataCorp).

Results

General characteristics of study participants

Of 13,436 participants, 7,790 (58%) were women, and 5,645 (42%) were men, with a mean age of 40.7 (SE ±0.10) years (Table 2). More than half (61.1%) of the population lived in urban areas with no significant sex difference. About 40% of the population had no education, and men were more likely to be educated than women at each level of education (p <0.001). Men were also more likely to be wealthier than women were (p <0.0001).

A similar trend was found for employment status where men were about 24% higher in employment status than women were (p <0.001). Mean BMI was significantly higher among women (22.28 vs. 21.08; p< 0.001) compared with men. Men were more likely to be exposed to secondhand smoking (p <0.003) compared with women.

Prevalence of hypertension by sex and urbanity

Women were having lower prevalence of hypertension compared with men for both measured (16.0%, 95% CI: 14.8, 17.3 vs. 22.8%, 95% CI: 21.2, 24.5) and medical hypertension (21.7%, 95% CI: 20.4, 23.0 vs. 29.1%, 95% CI: 27.4, 30.8) and the differences were significant statistically in both measurements (p< 0.001) (Table 3). People living in urban areas were having higher prevalence of hypertension compared with people living in rural areas for both measured (19.5%, 95% CI: 18.7, 20.4 vs. 17.9%; 95% CI: 16.9, 19.0) and medical (26.2%, 95% CI: 25.2, 27.1 vs. 22.7%; 95% CI: 21.6, 23.8) hypertension and the differences were significant statistically (p< 0.001) only for medical hypertension. Comparable trends were observed for both measurements in normal blood pressure (p <0.001). According to the new ACC/AHA 2017 guideline, there was an overall 21% increase in the prevalence of hypertension, with the highest increase in the male population (23%). Similar trends of sex differences were observed in hypertension (p <0.001) by both guidelines; however, significant urban-rural differences (p >0.05) were not observed.

Table 3

Prevalence of hypertension by sex and urbanity in Nepal.

Classification of Blood Pressure	Overall n = 13 436 (%) [95% CI]	Malen = 5645 (%) [95% CI]	Femalen = 7790 (%) [95% CI]	p value	Urbann = 8,205 (%) [95% CI]	Ruraln = 5,231 (%)[95% CI]	p value
JNC7 Guideline
Hypertension (measured)	2538 (18.9) [17.7, 20.1]	1289 (22.8) [21.2, 24.5]	1249 (16.0)[14.8, 17.3]	< 0.001	1600 (19.5)[18.7, 20.4]	938 (17.9)[16.9, 19.0]	0.22
Hypertension (medical)	3333 (24.8) [23.6, 26.0]	1645 (29.1) [27.4, 30.8]	1688 (21.7)[20.4, 23.0]	< 0.001	2147 (26.2)[25.2, 27.1]	1186 (22.7)[21.6, 23.8]	0.007
Normal Blood Pressure (measured)	7233 (53.8) [52.1, 55.6]	2581 (45.7) [43.5–48.0]	4652 (59.7)[57.9, 61.5]	< 0.001	4340 (52.9)[51.8, 54.0]	2893 (55.3)[54.0, 56.7]	0.22
Normal Blood Pressure (medical)	6888 (51.3) [49.7, 52.9]	2449 (43.4) [41.2, 45.6]	4439 (57.0)[55.3, 58.7]	< 0.001	4113 (50.1)[49.1, 51.2]	2775 (53.1)[51.7, 54.4]	0.11
ACC/AHA 2017 Guideline
Hypertension (measured)	5728 (42.6)[40.9, 44.4]	2772 (49.1)[47.8, 50.4]	2956 (38.0)[36.9, 39.0]	< 0.001	3582 (43.7)[42.6, 44.7]	2146 (41.0)[39.7, 42.4]	0.18
Hypertension (medical)	6136 (45.7)[44.1, 47.3]	2950 (52.3)[51.0, 53.6]	3186 (40.9)[39.8, 42.0]	< 0.001	3848 (46.9)[45.8, 48.0]	2288 (43.7)[42.4, 45.1]	0.08
Normal Blood Pressure (measured)	7093 (52.8)[52.0, 53.6]	2524 (44.7)[43.4, 46.0]	4569 (58.7)[57.6, 59.7]	< 0.001	4251 (51.8)[50.7, 52.9]	2842 (54.3)[53.0, 55.7]	0.20
Normal Blood Pressure (medical)	6763 (50.3)[49.5, 51.2]	2397 (42.5)[41.2, 43.8]	4365 (56.0)[54.9, 57.1]	< 0.001	4034 (49.2)[48.1, 50.3]	2729 (52.2)[50.8, 53.5]	0.10

Socioeconomic status and hypertension by sex and urbanity

Figs 1 and 2 explained the odds of blood pressure outcomes by education and wealth quintiles. The likelihood of being hypertensive (measured) was significantly higher in the higher education group compared with the lowest or no education group for men (OR 1.89 95% CI: 1.36, 2.61) and for women (OR 1.20 95% CI: 0.79, 1.83). People in the richest group were more likely to be hypertensive (measured) compared with people in the poorest group for men (OR 1.66 95% CI: 1.26, 2.19) and women (OR 1.60 95% CI: 1.20, 2.12). The overall associations between SES and hypertension were positive and statistically significant, modified by urbanity. However, the association between education and hypertension, not wealth and hypertension, was modified by gender (S1 File). Similar trends and associations between hypertension and SES were observed for ACC/AHA 2017 guidelines, and the effect of SES was modified by gender and urbanity (S2 File). Similarly, people with higher SES were less likely to have normal blood pressure compared with people in low SES (Figs 1 and 2, S1 File).

Fig 1

Association of (a) hypertension and (b) normal blood pressure (measured) with education levels by sex in Nepal. a) Hypertension and Education Levels b) Normal Blood Pressure and Education Levels. Odds ratios are adjusted for age, urbanity and marital status, and stratified by sex. Measurement-only outcomes are defined based on cut-off points: hypertension: SBP ≥140mmHg or DBP ≥90mmHg; normal blood pressure: SBP ≤ 120mmHg and DBP ≤ 80 mmHg.

Fig 2

Association of (a) hypertension and (b) normal blood pressure (measured) with wealth quintiles by sex in Nepal. a) Hypertension and Wealth Quintiles b) Normal Blood Pressure and Wealth Quintiles. Odds ratios are adjusted for age, urbanity and marital status, and stratified by sex. Measurement-only outcomes are defined based on cut-off points: hypertension: SBP ≥140mmHg or DBP ≥90mmHg; normal blood pressure: SBP ≤ 120mmHg and DBP ≤ 80 mmHg.

Mediation effect of BMI on SES and hypertension

Table 4 shows a reduction in estimates of SES after adjusting for mediator variable-BMI in the logistic regression models. At least a 10% change in the regression coefficients due to adjusting for mediator indicates its mediating effect. For the levels of education, the adjusted odds of hypertension (measured) significantly decreased, at least 10% throughout the models, and particularly BMI attenuated the association and level of significance for each primary, secondary and higher education category with hypertension (measured) in the models I, II and III. Table 4 also suggests that further adjustment for mediator-BMI in models reduced the effect size and level of significance in wealth quintiles and hypertension (measured). In other words, the further inclusion of BMI in the models has reduced the regression coefficients of hypertension-at least 10% for wealth quintiles and reduced statistical significance (Table 4: Model I(a) vs. Model I(b); Model II(a) vs. Model II(b); Model III(a) vs. Model III (b)). BMI, therefore, may play a mediating role in the associations between SES and hypertension (measured) for both men and women. Similar analyses were also performed for hypertension (medical) [S1 File].

Table 4

Mediation effect (by 10% change in coefficients after adjusting for the mediator) of BMI on SES and hypertension by sex in Nepal.

Predictor	Model I- Overall (n = 13,436)		Model II-Men (n = 5,646)		Model III-Women (n = 7,790)
	aRegression Coefficients without mediator (95% CI)	bMediator Adjusted Regression Coefficients (95% CI)	aRegression Coefficients without mediator (95% CI)	bMediator Adjusted Regression Coefficients (95% CI)	aRegression Coefficients without mediator(95% CI)	bMediator Adjusted Regression Coefficients(95% CI)
Hypertension (measured) by Education Levels (Ref. No education/preschool)
Primary	0.33(0.15, 0.52)***	0.19(-0.01, 0.38)	0.53(0.27, 0.79) ***	0.40(0.13,0.66) **	0.29(0.06,0.51) **	0.11(-0.13, 0.35)
Secondary	0.46(0.27, 0.65)***	0.24(0.05, 0.43)*	0.73(0.46, 1.00) ***	0.48(0.19,0.77) ***	0.24(-0.02.0.51)	0.03(-0.23,0.30)
Higher	0.39(0.14, 0.65) **	0.11(-0.16, 0.38)	0.69(0.36, 1.01) ***	0.34(-0.01, 0.69) *	0.13(-0.28,0.55)	-0.10(-0.53,0.33)
Hypertension (measured) by Wealth Quintiles (Ref. Poorest)
Poorer	0.20(0.02,0.39) *	0.17(-0.01, 0.35)	0.27(0.03,0.50) *	0.22(-0.02,0.45)	0.15(-0.10,0.40)	0.12(-0.13,0.37)
Middle	-0.01(-0.24,0.21)	-0.07(-0.29,0.15)	-0.06(-0.33,0.22)	-0.13(-0.41,0.15)	0.03(-0.25,0.31)	-0,01(-0.29,0.26)
Richer	0.04(-0.18,0.26)	-0.14(-0.35,0.08)	0.09(-0.21,0.38)	-0.09(-0.39,0.20)	-0.01(-0.29,0.28)	-0.20(-0.48,0.08)
Richest	0.49(0.26, 0.73) ***	0.04(-0.20,0.29)	0.57(0.28,0.86) ***	0.16(-0.17,0.49)	0.42(0.13,0.70) **	-0.10(-0.41,0.21)

aCoefficients adjusted for age, sex, marital status, urbanity, and second-hand smoking; bCoefficients further adjusted for mediator-BMI. Regression coefficients; 95% confidence intervals in brackets

* p<0.05

** p<0.01

*** p<0.001.

Mediation Analysis: Body Mass Index

The average BMI, according to the global cut-offs of BMI, was about 22, which indicates about 18% of the respondents were obese/overweight. However, the prevalence of obesity/overweight, according to the South Asia-specific cut-offs of BMI, was about 37%. The likelihood of being overweight/obese increased with an increasing level of SES, which also modified by sex and urbanity (S1 and S2 Files). Hence, we formally tested the mediation effect of BMI on hypertension (measured) and SES as well as presented the path coefficients (95% CI), and indirect effects of SES through BMI with bias-corrected 95% CI (Fig 3). The indirect effect of education on hypertension through BMI was statistically significant (Coef. 0.48; 95% bias-corrected CI: 0.41, 0.56). The total direct effect of education levels was Coef. 0.82 (95% CI: 0.48, 1.17). Thus, we may interpret that BMI mediated about 37% of the effect of education on hypertension. Similarly, the indirect effect of wealth quintiles on hypertension through BMI (Coef. 0.71; 95% bias-corrected CI: 0.61, 0.82) was significant. The direct effect was Coef. 0.09 (95% CI: -0.41, 0.58), and BMI mediated 89% of the total effect of wealth quintiles. BMI played a similar mediating role in the associations between SES and hypertension by ACC/AHA 2017 guidelines (S2 File).

Fig 3

Mediating role of BMI in the association between SES and hypertension (measured) in Nepal. Path coefficients (95% CI) and indirect effect of SES on hypertension through BMI with bias-corrected 95% confidence intervals are reported. * p<0.05, ** p<0.01, *** p<0.001. CI = Confidence Interval, BMI = Body Mass Index, DV = Dependent Variable, IV = Independent Variable, MV = Mediating Variable, SES = Socioeconomic Status.

Sensitivity analyses

We conducted sensitivity analyses that assessed the associations between SES and hypertension (measured and medical) adjusted for potential confounders according to the new guidelines of ACC/AHA 2017, which were stratified by sex and urbanity (S2 File). These analyses produced estimates and trends that are very similar to those for primary analyses, which reinforce our findings that increasing SES is associated with an increased likelihood of having hypertension, which modified by sex and urbanity.

Our sensitivity analyses constructed nested logistic regression models for the associations between hypertension (measured and medical) and SES that progressively adjusted for age, sex, urbanity, marital status, exposure to second-hand smoke, and BMI (Table 4, S1 File). The estimates and trends reinforce our primary findings.

For the secondary outcome, we examined the associations between SES and BMI using two different approaches. Firstly, we conducted sex and urbanity stratified analyses for SES using both global and South Asia specific categories of BMI (S1 and S2 Files). Secondly, we tested BMI as a continuous variable in association with SES due to the low prevalence of obesity (S1 File). These results supported that the likelihood of being overweight/obese increased with an increasing level of SES, which also modified by sex and urbanity.

We also conducted mediation analysis for hypertension by new guidelines of ACC/AHA 2017, which reinforce our argument that the association between SES and hypertension is mediated by BMI (S2 File).

Discussion

Our study, including 13,436 people from a nationally representative survey, finds that people with increasing levels of SES (education and wealth) are at an increased risk of having hypertension in Nepal, with the association (education) moderated by gender. These associations also modified by urbanity. Our novel finding is that BMI mediated the associations between SES and hypertension in the context of LMICs, particularly in Nepal. We found these results were comparable for both the JNC7 and the ACC/AHA 2017 guidelines.

Established evidence suggests that risk factors for cardiovascular disease (CVD), including hypertension, are highly prevalent in low SES groups in developed countries [12, 26]. In contrast to this evidence, our study shows that the prevalence of hypertension was greater among people with higher SES groups, which is consistent with recent studies conducted in LMICs, particularly in South Asia [15, 27–30]. Substantial differences between men and women were observed only in the association between education and hypertension, which is consistent with previous studies in developed countries [15, 31–34]. However, a recent study claimed that the evidence of CVD risk among higher SES group in low-income countries is limited to particular countries and argued that the risk of CVD in low-income countries is higher among people with lower levels of education [35]. The study, however, did not investigate whether the risk of hypertension would be the same as the risk of CVD that warrant further research [35]. Moreover, we believe that this argument is merely applicable in the south Asian settings particularly in Nepal, due to recent economic and demographic transition [7, 15, 21].

In line with these studies, our study observed that increasing levels of education and wealth quintiles have a positive association with higher likelihood of BMI both in men and women. Hence, we formally tested the mediating roles of BMI in the association between higher SES and hypertension and demonstrated that BMI attenuates the observed associations. In other words, BMI may help to explain broader SES differentials in hypertension, particularly by education and wealth quintiles. Evidence from higher-income countries also supported that BMI mediates the association between education and the risk of cardiovascular diseases [36].

Our observed results have several policy implications. The comprehensive understanding of the mechanisms of socioeconomic differentials in hypertension may help to take adequate measures for the prevention of risk of CVD in resource-poor settings. Findings related to SES by sex and rural-urban differences in hypertension will also guide to take gender-sensitive policy measures in reducing CVD and its modifiable risk factors.

Our study demonstrated that the prevalence of obesity/overweight, according to the South Asia-specific cut-offs of BMI, was at unhealthy levels, and the risk of being obese/overweight was increased by the increasing levels of SES (education and wealth).” Thus, the identification of BMI as a mediator of the higher SES and hypertension association emphasizes on this modifiable risk factor as a potential target for interventions to reduce CVD and related risk factors such as hypertension and elevated blood pressure in higher SES groups in LMICs. This study provides further evidence allied to the emergence of SES gradients in CVD and related risk factors. Although few recent studies found SES gradients in CVD risk in LMICs setting, this research contributes to previous work by bridging the fields of socioeconomic differentials in CVD risk and formally testing established theoretical models. The veracity of our findings is contingent on replication with longitudinal data and more comprehensive assessments of SES.

To the best of our knowledge, this is the first study found mediating roles of the modifiable risk factors of CVD in the SES and hypertension association using a nationally representative sample in a resource-poor setting. Our study also first time assessed the association between SES and hypertension according to standard hypertension JNC7 guideline and a new guideline recommended by the ACC/ AHA 2017. We observed sex and rural-urban differences in blood pressure outcomes by sex and urbanity stratified analysis. For instance, recent studies also emphasized to investigate the SES gradient along with sex and rural-urban differences in blood pressure outcomes in Nepal [7, 8, 11, 37].

Along with these novel contributions and methodological strengths, some limitations may also be considered with the interpretation of the results. We were not able to assess the causality of the associations between SES and hypertension due to the cross-sectional nature of the data. Our measurement of SES omits an indicator of employment status, which should be assessed in detail in further research. Finally, blood pressure measurement error may occur due to the quality of medical staff training in various regions of Nepal, even though an automatic device for BP measurement had been used.

Conclusions

In conclusion, higher SES was positively associated with the higher likelihood of having hypertension in Nepal according to both JNC7 and ACC/AHA 2017 guidelines. All of the observed trends were more pronounced in men than in women, and there was evidence of differences in these trends between residents in rural and urban areas. The association between higher SES and hypertension was mediated by BMI, which may help to explain broader socioeconomic differentials in CVD and related risk factors, particularly in terms of education and wealth. Our study suggests that the mediating factor of BMI should be tackled to diminish the risk of CVD in people with higher SES in LMICs.

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12 Aug 2019

PONE-D-19-16119 Socioeconomic Differentials in Hypertension based on JNC7 and ACC/AHA 2017 Guidelines Mediated by Body Mass Index: Evidence from Nepal Demographic and Health Survey PLOS ONE Dear Mr Rana, Thank you for submitting your manuscript to PLOS ONE. After careful consideration, we feel that it has merit but does not fully meet PLOS ONE’s publication criteria as it currently stands. Therefore, we invite you to submit a revised version of the manuscript that addresses the points raised during the review process.

Based on reviewer comments and my read of your paper, I believe there are several areas where your paper can be strengthen.  First, you need streamline your paper to engage Reviewer 2’s concerns about overlapping confidence intervals and the conclusions drawn for your two measures.  Depending on the outcome (hypertension or blood pressure), the within and between group confidence intervals overlap considerably across education and wealth for men and women.  What does this tell us about gender and health in Nepal?  Furthermore, what explains the within group divergence for a particular level of socioeconomic status by gender?

Second, more information is needed about your mediation methods.  Reviewer 2 draws attention to how your analysis diverges from usual methods of mediation and that Figure 3 is missing from the manuscript.  I concur and would recommend that you conduct a more standard mediation model where the direct and indirect effects are estimated, with BMI as the mediator, for each gender and reported in a table or figure.

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Reviewer #1: The authors have written a very interesting manuscript. The aim of the manuscript is to assess the associations between SES

and hypertension in Nepal and the extent to which these associations vary by sex and urbanity. The authors used a robust methodology to estimate the mediating role of body mass index. I do not have much comments about the results, however, I have a few minor comments and only one major concern.

1. The authors could fortify their results by providing the empirical strategy they used for the estimation. This is very important for other researchers who would like replicate their work.

2. I think the authors could recommend some policy implications of their results.

3. The figures in the manuscript are blare.

Reviewer #2: The methodology used by the authors seems to be commonly used for this type of analysis but it is not properly used and described. The results that they report contain important flaws from a methodological point of view. I explain my concerns with more detail in the attached file.

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27 Oct 2019

26th October 2019

Bryan L. Sykes, Ph.D.

Academic Editor

PLOS ONE

RE: PONE-D-19-16119 ‘Socioeconomic differentials in hypertension based on JNC7 and ACC/AHA 2017 guidelines mediated by body mass index: Evidence from Nepal demographic and health survey.’

Dear Professor Sykes,

We thank you for the opportunity to resubmit our manuscript and for the considered comments of the reviewers. We have addressed each of these point by point below, and a revised version of the manuscript is submitted for your consideration.

We believe we have addressed all the reviewers’ comments, and where changes have been made, have shown these as marked yellow changes. We hope the manuscript is now considered acceptable for publication in your journal.

Yours sincerely,

Juwel Rana, MPH

Corresponding author

Editor’s Concern

Based on reviewer comments and my read of your paper, I believe there are several areas where your paper can be strengthen. First, you need streamline your paper to engage Reviewer 2’s concerns about overlapping confidence intervals and the conclusions drawn for your two measures. Depending on the outcome (hypertension or blood pressure), the within and between group confidence intervals overlap considerably across education and wealth for men and women. What does this tell us about gender and health in Nepal? Furthermore, what explains the within group divergence for a particular level of socioeconomic status by gender?

Authors’ Response: We appreciate your succinct summarization of reviewers’ comments. We have addressed all of your concerns regarding the manuscript.

Second, more information is needed about your mediation methods. Reviewer 2 draws attention to how your analysis diverges from usual methods of mediation and that Figure 3 is missing from the manuscript. I concur and would recommend that you conduct a more standard mediation model where the direct and indirect effects are estimated, with BMI as the mediator, for each gender and reported in a table or figure.

Authors’ Response: We apologize that the missing of figure 3 about mediation analysis introduced confusion of reviewer 2. We have now provided the missing figure 3, indirect, and direct effects estimate of SES with details of the mediation analysis process.

Journal requirements:

When submitting your revision, we need you to address these additional requirements.

1. Please ensure that your manuscript meets PLOS ONE's style requirements, including those for file naming. The PLOS ONE style templates can be found at

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Authors’ Response: Thank you for your guidance. We have formatted the title, headings, subheadings, and authors’ affiliation according to the journal requirements.

2. Please upload a copy of Figure 3, to which you refer in your text on page 18. If the figure is no longer to be included as part of the submission, please remove all reference to it within the text.

Authors’ Response: We apologize for this unintentional error, which raised some questions related to mediation analysis. We believe the figure will make more sense about the mediation analysis. We have now uploaded the Figure 3.

Reviewer #1: The authors have written a very interesting manuscript. The aim of the manuscript is to assess the associations between SES and hypertension in Nepal and the extent to which these associations vary by sex and urbanity. The authors used a robust methodology to estimate the mediating role of body mass index. I do not have much comments about the results, however, I have a few minor comments and only one major concern.

1. The authors could fortify their results by providing the empirical strategy they used for the estimation. This is very important for other researchers who would like replicate their work.

Authors’ Response: Thank you very much for your appreciation. We have now included more details on the methods and material section along with the particular references for the empirical strategy that we followed.

2. I think the authors could recommend some policy implications of their results.

Authors’ Response: We have already discussed some policy implications of our results on Page 20-21.

3. The figures in the manuscript are blare.

Authors’ Response: The quality of figures was distorted due to the conversion of JPG to TIFF. We have now replaced those with a clear version of the figures.

Reviewer #2:

The main objective of this research is to evaluate if there is a positive association between socioeconomic status (SES) and the probability of having hypertension in Nepal. They use the 2016 Nepal Demographic and Health Survey to estimate logit models using binary dependent variable based on two definitions of blood pressure. One dependent variable is only based on the blood pressure exams taken during the interview. The second dependent variable identifies high-blood pressure individuals based on the blood pressure exams taken during the interview or on self-reported diagnosis of high pressure or medicine prescription. In addition, the authors include in the models an obesity-related variable to evaluate if the body mass index could be viewed as a mediator variable between SES and the likelihood of having high blood pressure. The authors conclude that higher SES was positively associated with the higher likelihood of having hypertension, and that such association between higher SES and hypertension was mediated by the body mass index (BMI).

The methodology used by the authors seems to be commonly used for this type of analysis, but it is not properly used and described. More importantly, conclusions do not agree with the statistical result. I explain below my concerns with more detail.

Main Comments

1. The authors conclude that higher SES is positively associated with a higher likelihood of having hypertension. However, this conclusion is not reliable because of two reasons:

(a) Most of the confidence intervals (CIs) of the odds ratios (ORs) related to education or wealth overlap with each other. In particular, the confidence interval for the highest level of SES (education or wealth) is the widest. Therefore, it is possible that ORs are not significantly different across SES levels. If there is some significant difference based on the results, it would be for any SES with respect to the lowest SES where in which it is more likely to observe under-nutrition (and probably a very low prevalence rate of obesity). These are just two examples to illustrate extreme CI overlap: (i) S1 Table, medical, women: primary education CI [1.24, 1.94], higher education CI [1.18, 2.25] (ii) S2 Table, medical, overall: poorer CI [1.08, 1.52], richer CI [1.02, 1.59].

Authors’ Response (a): Thank you for raising this point. Firstly, we have now presented the results of the associations between SES and hypertension (measurement-only definition) addressing the reviewer’s concern. Our comparators were ‘no education/preschool’ (Ref. Category) for other levels of education and ‘poorest wealth quintile’ (Ref. Category) for other categories of wealth quintiles, which indicate that we do not need to be cautious about the overlap of CI across SES levels. Therefore, our conclusion about positive associations between higher SES and hypertension is reliable.

Both Editor and reviewer’s concern about the between-group confidence intervals overlap across SES is more reasonable to investigate further because we did not formally test effect modification/interaction by sex in the earlier version. However, we have now tested effect modification of sex by interaction term along with previous sex-stratified analysis, which clarifies overlaps of confidence intervals across SES levels between men and women. Our effect modification test confirms that the effect of higher levels of education on hypertension was different between men and women, but the effect of wealth on hypertension was not different between men and women. Therefore, we do not have methodological limitations to conclude that higher SES is positively associated with a higher likelihood of having hypertension, which is different by sex across education but not across wealth quintiles.

We also would like to note that there has been an entrenched belief, especially in medicine, that overlapping 95%CIs is statistical insignificance, p>0.05. However, there have now been a number of explanations that prove that this belief is incorrect. If two statistics have non-overlapping confidence intervals, they are necessarily significantly different, but if they have overlapping confidence intervals, it is not necessarily true that they are not significantly different.

Several writers have also pointed out that considerable overlap can be compatible with a significant difference, p=0.05 (i-v).

i. Andrea Knezevic A. Overlapping Confidence Intervals and Statistical Significance. The Cornell Statistical Consulting Unit, StatNews # 73, 2008.

ii. Cumming G, Finch S. Inference by eye: confidence intervals and how to read pictures of data. American Psychologist 2005; 60:170–180. DOI: 10.1037/0003-066X.60.2.170.

iii. Schenker N, Gentleman JF. On judging the significance of differences by examining the overlap between confidence intervals. The American Statistician 2001; 55:182–186. DOI: 10.1198/000313001317097960.

iv. Austin PC, Hux JE. A brief note on overlapping confidence intervals. Journal of Vascular Surgery 2002; 36:194–195. DOI: 10.1067/mva.2002.125015.

v. Cumming G. Inference by eye: Reading the overlap of independent confidence intervals. Statist. Med. 2009; 28:205–220

(b) It is not clear how the medical high blood pressure is defined. It seems that adults that do not have high pressure based on the blood pressure readings could still be considered to have high pressure if they reported a past diagnosis of hypertension or they report that are taking some prescribed medicine for high blood pressure. Since access to health services could be correlated with higher SES, it is possible that this medical definition of high blood pressure biases the estimated relationship between SES and the probability of having high blood pressure. In fact, OR point estimates using the medical definition of high blood pressure are systematically larger than those calculated using the measured blood pressure definition, whereas their corresponding CI are always to the right of those calculated with the measured blood pressure (e.g., see Table Sl-S4). This limitation of using the medical definition should be clearly stated in the manuscript.

Authors’ Response (b): We appreciate reviewer’s concern regarding the medical definition of hypertension used in this study based on published literature. Following the reviewer’s suggestions, we have now reassessed the association between SES and hypertension based on ‘measurement-only’ definition and presented throughout the manuscript to avoid biases and limitations of using medical definition suspected by the reviewer.

2. Given the limitation of the medical definition of high blood pressure, the exercise of mediation presented in Table 4 should be done with the measurement-only definition. It is not clear why the authors only present the results using the medical definition. In addition, Table 4 presents results as exponentiated coefficients whereas the OR estimates are reported for other models. I suggest to present the results of Table 4 using the OR estimates. Since other regressors are included exponentiated coefficients are not equal to the OR.

Authors’ Response: According to the reviewer’s recommendation, we have performed a mediation analysis with the ‘measurement-only’ definition of hypertension. Table 4 presents regression coefficients (exponentiated coefficients) because we can initially observe the mediation effect of BMI on the basis of a rule thumb-comparing at least 10% change in coefficients for models with and without adjusting for the mediator variable. We cannot calculate/compare a 10% change in terms of OR that would produce misleading results. Thus, we had to report regression coefficients rather than OR.

3. The mediator analysis is poorly explained. The authors say: "The second step was the "indirect effect "approach, which formally examined the statistical significance of an indirect effect using the product of coefficients approach. For assessing the indirect effect of BMI on these binary outcomes, we used the binary-mediation package in Stata" The results of this analysis are poorly reported (e.g., Figure 3 is not in the manuscript). Furthermore, to the best of my knowledge, there are at least three different methods in Stata to conduct mediation analysis. I suggest that the authors explain this procedure clearly.

Authors’ Response: We apologize for this unintentional error, as Figure 3 was not uploaded in the previous version. Following the editor and reviewer’s suggestions, we have now reported the results (Figure 3 is attached) of direct and indirect effect with bias-corrected confidence intervals. We acknowledge that there are multiple methods of mediation analysis. However, we used generalized structural equation modeling (GSEM) technique. We are happy to upload STATA code as supplementary material if the editor asks for. The procedure is explained in detail on the materials and methods section in our manuscript as well as in the following articles:

i. https://stats.idre.ucla.edu/stata/faq/how-can-i-do-mediation-analysis-with-the-sem-command/

ii. https://stats.idre.ucla.edu/stata/faq/how-can-i-do-mediation-analysis-with-a-categorical-iv-in-stata/

iii. StataCorp. 2019. Stata: Release 16. Structural Equation Modeling Reference Manual, Example 42

iv. Baron RM, Kenny DA. The Moderator-Mediator Variable Distinction in Social Psychological Research. Conceptual, Strategic, and Statistical Considerations. J Pers Soc Psychol. 1986;51: 1173–82. doi:10.1037/0022-3514.51.6.1173

v. Preacher KJ, Hayes AF. Asymptotic and resampling strategies for assessing and comparing indirect effects in multiple mediator models. Behav Res Methods. 2008;40: 879–891. doi:10.3758/BRM.40.3.879

vi. Hosmer DW, Lemeshow S, Sturdivant RX. Applied Logistic Regression: Third Edition. Applied Logistic Regression: Third Edition. 2013. doi:10.1002/9781118548387

4. According to Nepal DHS, 2016-Final Report, rates of hypertension are higher among tobacco users. Therefore, the mediation analysis should include this variable.

Authors’ Response: This is a good point raised by the reviewer, and we have already considered secondhand smoking in the mediation analysis. There are several reasons for using secondhand smoking over tobacco users. First, since tobacco can be used in several forms (cigarette smoking, chewing with battle leaf and water pipes, also referred to as Hookah, Shisha, Narghile, Argileh) in South Asian countries, including Nepal, comprehensive data on ‘tobacco use’ was unavailable. Secondly, the small sample size of current tobacco smoking that will reduce the power of the analysis.

5. According to the Nepal DHS, 2016-Final Report l "The average of the second and third measurements was used to classify the respondent with respect to hypertension, according to internationally recommended categories (WHO 1999; NIH 1997) l'. It is not clear why the authors use the average of the three measurements. Would the results be different if they use the variable created and suggested by the Nepal DHS? See Tables 14.3.1 and 14.32 to find the definitions of hypertension available in Nepal DHS. 2016.

Authors’ Response: Our research aimed to compare between JNC7 and ACC/AHA 2017 guidelines. Thus, we have classified hypertension according to the internationally recommended categories of JNC7 and ACC/AHA 2017, which recommended to take the average of the two or more measurements for reducing the bias of measurement error. The previously published literature also used the average of three measurements for ensuring higher accuracy (Harshfield E, Chowdhury R, Harhay MN, Bergquist H, Harhay MO. Association of hypertension and hyperglycaemia with socioeconomic contexts in resource-poor settings: The Bangladesh Demographic and Health Survey. Int J Epidemiol. 2015; 44: 1625–1636. doi:10.1093/ije/dyv087).

According to the reviewer’s suggestion, using the average of the two (second and third) measurements, the results (15.6% women and 22.1% men) were not much different from the Nepal DHS (17% women and 23% men). The existing variation of prevalence could be explained by the differences in age groups included in the analyses. For example, the Nepal DHS included men and women aged 15+ while we included men and women aged 18+ in our analysis.

Furthermore, our results (16% women and 22.8% men aged 18 years and older) based on the average of three measurements are almost identical to the Nepal DHS report (17% women and 23% men aged 15 and older).

6. It is not clear if sampling weights were included in the estimations of the logistic regressions. They should be used.

Authors’ Response: Thank you. Yes, we have used sampling weights, which is already mentioned on page 6, lines 125-127 and page 10, lines 211-213, and in Supplementary Fig 1.

7. About policy implications. The authors suggest that interventions should be done in the higher SES groups to modify hypertension or obesity. Based on my previous comments, the results that they report contain important flaws from a methodological point of view, so they do not support this policy recommendation.

Authors’ Response: Thank you for your critical remarks. We have already considered reviewer’s suggestion and presented results by measurement-only definition which give an almost identical conclusion. Moreover, the Nepal DHS Report 2016 also gave a similar conclusion based on their results (Nepal DHS Figure 14.3) that is identical to our findings. However, we are now cautious about our argument in the discussion, Page 19.

Minor Comments

1. The number of observations mentioned by the authors is smaller from the one reported in the Nepal DHS, 2016-Final Report (see Tables 14.3.1 and 14.3.2). Is it possible that the range of ages considered was only between 15-60 years old? In the case of BMI the number of observations reported in Nepal DHS seems to be different too (e.g. it was not measured for pregnant women). I suggest that the authors report the sample size for each estimated model.

Authors’ Response: We reported now the sample size for the all estimated models. However, we had mentioned in the previous version about the range of age (18 years and older) and the number of unweighted and weighted observations in Tables 2 and 3, Page 6 Lines 125-127; Figures 1 and 2, including in the Supplementary Fig 1.

2. The evidence that prevalence of hypertension is higher among high SES in low income countries is limited to specific countries and it has been questioned. Furthermore, in a recent study Rosengrent et al. (2019) conclude that "people with low levels of education in low-income and middle-income countries had a markedly higher risk of major cardiovascular events compared with those with higher levels of education. Cardiovascular disease in low-income countries is a problem predominantly among people with lower levels of education, whereas the situation in middle-income countries is more variable" I suggest that the authors consider this issue at least in the discussion section.

Authors’ Response: Thank you for this point and suggesting relevant literature. We already used the referred article in the earlier version (Ref. 28 Razak F and Subramanian S. Commentary: Socioeconomic status and hypertension in low- and middle-income countries: can we learn anything from existing studies? Int J Epidemiol 2014; 43(5): 1577-1581). We further acknowledge reviewer’s concern and added the following text on page 19, lines 356-361 to be cautious about our conclusion.

“However, a recent study claimed that the evidence of CVD risk among higher SES groups in low-income countries is limited to particular countries and argued that the risk of CVD in low-income countries is higher among people with lower levels of education.35 The study, however, did not investigate whether the risk of hypertension would be the same as the risk of CVD that warrant further research.35 Moreover, we believe that this argument is merely applicable in the south Asian settings particularly in Nepal, due to recent economic and demographic transition.7, 15, 21”

3. Improve quality of figures.

Authors’ Response: We acknowledge the poor quality of figures due to the conversion of JPG to TIFF. However, we have now replaced those with a clear version of the figures.

Submitted filename: Responses to Reviewers.docx

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18 Dec 2019

PONE-D-19-16119R1

Socioeconomic Differentials in Hypertension based on JNC7 and ACC/AHA 2017 Guidelines Mediated by Body Mass Index: Evidence from Nepal Demographic and Health Survey

PLOS ONE

Dear Mr Rana,

Thank you for submitting your manuscript to PLOS ONE. After careful consideration, we feel that it has merit but does not fully meet PLOS ONE’s publication criteria as it currently stands. Therefore, we invite you to submit a revised version of the manuscript that addresses the points raised during the review process.

This manuscript has improved greatly.  Thank you for taking seriously the reviewer comments and my suggestions from the last round of review.  There are several outstanding revisions in need of elaboration or correction before your paper can be accepted for publication.  Should you make the following set of revisions successfully, your revised submission will not be sent out for re-review, and I will accept your paper for publication upon resubmission.

First, Reviewer 2 draws your attention to an overarching conclusion espoused in the paper.  I concur with the reviewer’s assessment and request that you change the statement “Our study, including 13,436 people from a nationally representative survey, demonstrated that increasing levels of SES (education and wealth) were positively associated with an increased risk of having hypertension in Nepal, with an evidence of effect modification of gender in the 351 association between education and hypertension” “Our study, including 13,436 people from a nationally representative survey, finds that higher levels of education (compared to individuals with no education or at a pre-school educational level) are at an increased risk of having hypertension in Nepal, with the association moderated by gender.”

Second, Reviewer 2 raises a question that merits engagement in your discussion section.  Namely, how does BMI mediate hypertension in a population that has normal (or healthy) BMI levels?

We would appreciate receiving your revised manuscript by Feb 01 2020 11:59PM. When you are ready to submit your revision, log on to https://www.editorialmanager.com/pone/ and select the 'Submissions Needing Revision' folder to locate your manuscript file.

If you would like to make changes to your financial disclosure, please include your updated statement in your cover letter.

To enhance the reproducibility of your results, we recommend that if applicable you deposit your laboratory protocols in protocols.io, where a protocol can be assigned its own identifier (DOI) such that it can be cited independently in the future. For instructions see: http://journals.plos.org/plosone/s/submission-guidelines#loc-laboratory-protocols

Please include the following items when submitting your revised manuscript:

A rebuttal letter that responds to each point raised by the academic editor and reviewer(s). This letter should be uploaded as separate file and labeled 'Response to Reviewers'.

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Reviewer #1: All comments have been addressed

Reviewer #2: (No Response)

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Reviewer #1: Specific comments

Page 11: line 232 - 233; I think the reviewer should be cautious in interpreting their values since they claim there were too many missing values.

Table 2: sample characteristics. I suggest that the authors interpret the dummies from averages to frequencies.

Page 29: line 389 - 396 should go to the conclusions.

General

Your conclusion should address the summary, limitations and policy recommendations. Please do restructure your introduction.

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Submitted filename: PONE-D-19-16119R2.pdf

Click here for additional data file.

20 Dec 2019

20th December 2019

Bryan L. Sykes, Ph.D.

Academic Editor

PLOS ONE

RE: PONE-D-19-16119 ‘Socioeconomic differentials in hypertension based on JNC7 and ACC/AHA 2017 guidelines mediated by body mass index: Evidence from Nepal demographic and health survey.’

Dear Professor Sykes,

We thank you for the opportunity to resubmit our manuscript and for the considered comments of the reviewers. We have addressed each of these point by point below, and a revised version of the manuscript is submitted for your consideration.

We believe we have addressed all the reviewers’ comments, and where changes have been made, have shown these as marked yellow changes. We hope the manuscript is now considered acceptable for publication in your journal.

Yours sincerely,

Juwel Rana, MPH (double)

Corresponding author

Editor’s Concern

First, Reviewer 2 draws your attention to an overarching conclusion espoused in the paper. I concur with the reviewer’s assessment and request that you change the statement from “Our study, including 13,436 people from a nationally representative survey, demonstrated that increasing levels of SES (education and wealth) were positively associated with an increased risk of having hypertension in Nepal, with an evidence of effect modification of gender in the 351 association between education and hypertension” to “Our study, including 13,436 people from a nationally representative survey, finds that higher levels of education (compared to individuals with no education or at a pre-school educational level) are at an increased risk of having hypertension in Nepal, with the association moderated by gender.”

Authors’ Response: We appreciate your succinct summarization of reviewers’ comments. Reviewer 2’s concern is about overlapping CIs in the associations. In our first round responses, we benignly addressed the issues of CI’s overlapping. However, the concept of overlapping CI’s is only relevant when the outcome measure is continuous and normally distributed. Our outcome is binary, and effect estimates are odds ratios, which are NOT normally distributed. Like all other non-normal estimates such as risk ratio or hazard ratio, the issues of overlapping CIs cannot be applied to the odds ratio. The lower limits and upper limits of odds ratio are exponential of the margin of error of beta coefficients. Hence, the conclusion drawn from the results is valid.

Moreover, despite gender-stratified analysis, we have formally tested effect modification of gender in the associations between SES and hypertension. It reaffirms our rationale related to CI’s overlapping. Therefore, we would like to keep the statement as it currently appears in the manuscript.

However, if the editor feels the statement needs to be changed, we are happy to change it, but the statement will be partially correct based on our results.

According to editor’s suggestions, we have modified the tone of our statement in page 19, lines 352-354: “Our study, including 13,436 people from a nationally representative survey, finds that people with increasing levels of SES (education and wealth) are at an increased risk of having hypertension in Nepal, with the association (education) moderated by gender.”

Second, Reviewer 2 raises a question that merits engagement in your discussion section. Namely, how does BMI mediate hypertension in a population that has normal (or healthy) BMI levels?

Authors’ Response: We agree with this point. Due to getting low prevalence of obesity, according to the global cut-offs of BMI, we counted and categorized BMI as well as obesity/overweight according to the South Asia-specific cut-offs for BMI in our earlier version and already showed on page 19, lines 341-346 and in supplementary tables 5 and 6.

However, we have provided further clarification in the result and discussion section now according to the suggestions of the editor and reviewer in the following pages and lines:

Result: Page 17, lines 306-310: “The average BMI, according to the global cut-offs of BMI, was about 22, which indicates about 18% of the respondents were obese/overweight. However, the prevalence of obesity/overweight, according to the South Asia-specific cut-offs of BMI, was about 37%. The likelihood of being overweight/obese increased with an increasing level of SES, which also modified by sex and urbanity (S5 Table, S6 Fig).”

Discussion: Page 21, lines 385-387: “Our study demonstrated that the prevalence of obesity/overweight, according to the South Asia-specific cut-offs of BMI, was at unhealthy levels, and the risk of being obese/overweight was increased by the increasing levels of SES (education and wealth).”

Reviewer #1: Specific comments

Page 11: line 232 - 233; I think the reviewer should be cautious in interpreting their values since they claim there were too many missing values.

Authors’ Response: We completely agree with this point. This was one of the reasons not considering employment as an indicator of SES in our study. Thus, we are cautious about our interpretation and we did not include ‘employment’ as one of the main independent variables.

Table 2: sample characteristics. I suggest that the authors interpret the dummies from averages to frequencies.

Page 29: line 389 - 396 should go to the conclusions.

General

Your conclusion should address the summary, limitations and policy recommendations. Please do restructure your introduction.

Authors’ Response: We think now we have included more details, which addressed these comments. Moreover, our conclusion seems inclusive addressing summary, limitations and policy recommendations.

Reviewer #2:

A) I appreciate the effort of the authors to respond to all my comments. In my opinion, the quality and the clarity of the paper have improved significantly. However, I have one concern about this conclusion:

“Our study, including 13,436 people from a nationally representative survey, demonstrated that increasing levels of SES (education and wealth) were positively associated with an increased risk of having hypertension in Nepal, with an evidence of effect modification of gender in the 351 association between education and hypertension”.

I disagree with this conclusion because the statistical results that they obtain do not constitute a demonstration or a proof that increasing levels of SES are positively associated with hypertension in Nepal.

In fact, the statistical results present evidence that adults with some level of formal education (primary or beyond) are more likely to have hypertension in Nepal. My conclusion is based on the statistical results of Figure 1 and 2 (and the corresponding estimated models from Table 4). These figures show that the confidence intervals (CIs) of the odd ratios (ORs) related to education or wealth overlap with each other. This is the same comment that I included in my previous review. The authors’ response was that “If two statistics have non‐overlapping confidence intervals, they are necessarily significantly different, but if they have overlapping confidence intervals, it is not necessarily true that they are not significantly different.” That is totally correct. Since there are overlapping CIs, the ORs could or could not be different. Therefore, we cannot conclude that we have demonstrated that the probability of having hypertension increases as the SES increases.

This is equivalent to the result of not rejecting a null hypothesis. In that case, we would never claim that the null is true (or not true). The most precise way to solve this concern is by testing the equality of the parameters estimated in the models presented in Table 4 (without the mediator). A Wald test or a Lagrange multiplier test can be used to evaluate if the null that the parameters associated with different SES are different is rejected or not. Once again, a clear conclusion from this article is that having primary education or more increases the probability of having hypertension. As I mentioned in my previous review, this result could be explained if people with the lowest SES are also those with under‐nutrition levels.

Authors’ Response: We have addressed this point and mentioned it in the editor’s concern.

B) Finally, the average BMI in Nepal is around 22 with a small standard deviation. This suggests that the prevalence rate of obesity is small. How could the BMI be an important mediator for hypertension in individuals when most of them have BMI levels that are considered healthy levels? It would be worth considering this issue in the discussion section.

Authors’ Response: We have addressed this point and mentioned it in the editor’s concern.

Submitted filename: Responses to Reviewers.docx

Click here for additional data file.

3 Jan 2020

Socioeconomic Differentials in Hypertension based on JNC7 and ACC/AHA 2017 Guidelines Mediated by Body Mass Index: Evidence from Nepal Demographic and Health Survey

PONE-D-19-16119R2

Dear Dr. Rana,

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With kind regards,

Bryan L. Sykes, Ph.D.

Academic Editor

PLOS ONE

Additional Editor Comments (optional):

Reviewers' comments:

17 Jan 2020

PONE-D-19-16119R2

Socioeconomic Differentials in Hypertension based on JNC7 and ACC/AHA 2017 Guidelines Mediated by Body Mass Index: Evidence from Nepal Demographic and Health Survey

Dear Dr. Rana:

I am pleased to inform you that your manuscript has been deemed suitable for publication in PLOS ONE. Congratulations! Your manuscript is now with our production department.

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on behalf of

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Academic Editor

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