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Relationship between cigarette smoking and blood pressure in adults in Nepal: A population-based cross-sectional study

  • Renqiao Lan,

    Roles Conceptualization, Data curation, Formal analysis, Methodology, Writing – original draft, Writing – review & editing

    Affiliation School of Medicine, The University of Notre Dame Australia, Fremantle, Western Australia, Australia

  • Max K. Bulsara,

    Roles Formal analysis, Methodology, Software, Supervision, Validation, Writing – review & editing

    Affiliation Institute for Health Research, The University of Notre Dame Australia, Fremantle, Western Australia, Australia

  • Prakash Dev Pant,

    Roles Conceptualization, Data curation, Methodology, Resources, Supervision, Writing – review & editing

    Affiliation Monitoring and Evaluation Consultant, Kathmandu, Nepal

  • Hilary Jane Wallace

    Roles Conceptualization, Data curation, Formal analysis, Funding acquisition, Methodology, Project administration, Resources, Supervision, Validation, Writing – original draft, Writing – review & editing

    hilary.wallace@uwa.edu.au

    Affiliations School of Medicine, The University of Notre Dame Australia, Fremantle, Western Australia, Australia, School of Population and Global Health, The University of Western Australia, Crawley, Western Australia, Australia

Relationship between cigarette smoking and blood pressure in adults in Nepal: A population-based cross-sectional study

  • Renqiao Lan, 
  • Max K. Bulsara, 
  • Prakash Dev Pant, 
  • Hilary Jane Wallace
PLOS
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Abstract

Smoking and hypertension are two major risk factors for cardiovascular disease, the leading cause of death in Nepal. The relationship between cigarette smoking and blood pressure (BP) in Nepal is unclear. This study analysed the data from the 2016 Nepal Demographic and Health Survey to explore the differences in systolic BP (SBP) and diastolic BP (DBP) between current daily cigarette smokers and non-smokers in Nepali adults aged 18 to 49 years. A total of 5518 women and 3420 men with valid BP measurements were included. Age, body mass index, wealth quintile (socio-economic status) and agricultural occupation (proxy for physical activity) were included as potential confounders in multivariable linear regression analysis. Women smokers were found to have significantly lower SBP (mean difference 2.8 mm, 95% CI 0.7–4.8 mm) and DBP (mean difference 2.2 mm, 95% CI 0.9–3.6 mm) than non-smokers after adjustment. There were no significant differences in BP between smokers and non-smokers in males, either before or after adjustment. The lower BP in female cigarette smokers in Nepal may be explained by the physiological effect of daily cigarette smoking per se in women, or unmeasured confounders associated with a traditional lifestyle that may lower BP (for example, diet and physical activity). In this nationally representative survey, daily cigarette smoking was not associated with increased BP in males or females in Nepal.

Introduction

Cardiovascular disease is the leading cause of death globally [1] and in Nepal [2]. Both cigarette smoking and hypertension (high blood pressure) are well-established risk factors for cardiovascular disease and are thought to act synergistically on disease development [36]. According to the Nepal Burden of Disease 2017 report, high blood pressure and smoking are the top two risk factors for death and are responsible for 14% and 13% of all deaths in Nepal respectively [2]. The aetiology of primary hypertension is complex and lifestyle risk factors such as obesity, physical inactivity, excessive alcohol consumption and high salt intake are proposed to be strongly and independently associated with its development [79].

The understanding of the role of cigarette smoking in hypertension development continues to be refined. The hemodynamic effects of cigarette smoking are mediated primarily by nicotine [10], which can increase blood pressure (BP) acutely and temporarily via stimulation of the sympathetic nervous system [1012]. However, with long-term exposure nicotine may have different effects [13]. For example, it is hypothesized that the nicotine metabolite, cotinine, may decrease BP via its vasodilatory effect [13]. Nicotine may also decrease BP via lowering body weight secondary to its effects of appetite suppression or increasing metabolism [14].

Epidemiological studies on the relationship between smoking and BP have produced mixed results. Some studies have found a positive association between current smoking and hypertension [1517], including in an urban Nepali population [18]. By contrast, BP has also been found to be the same or lower in many groups of smokers compared to non-smokers [17, 1923].

The nationally representative 2016 Nepal Demographic and Health Survey (NDHS) report found that 17% of women and 23% of men (aged 15 years and over) were hypertensive, and the cigarette smoking rate was 5.5% among women and 26.9% in men (aged 15–49 years) [24]. Cigarette smoking is the most common form of tobacco smoking by men and women in Nepal [24]. Although demographic factors and overweight/obesity were found to be associated with hypertension in adults in this survey [2527], the relationship between cigarette smoking and BP needs further study. Given the high burden of cardiovascular disease, it is important to have a better understanding of the relationship between its two major risk factors in Nepal’s unique sociodemographic context and add to the evidence available from this specific population to inform mechanistic studies. The aim of this study is to explore the relationship between cigarette smoking and BP (systolic and diastolic) in the Nepali adult population aged 18–49 years, using data from the 2016 Nepal Demographic and Health Survey.

Methods

Study design, setting, and participants

Data were obtained from the 2016 Nepal Demographic and Health Survey (NDHS), a nationally representative cross-sectional household survey, funded by the US Agency for International Development (USAID) [24]. The survey was conducted from June 19, 2016, to January 31, 2017, and the sampling frame was a modified version of the Nepal Central Bureau of Statistics 2011 National Population and Housing Census [24].

Households were selected in two stages in rural areas and three stages in urban areas [24]. In urban areas, wards (smallest units of local government in Nepal) were the primary sampling units (PSU), from which one enumeration area (EA) was selected. Households were subsequently selected from EAs [24]. In rural areas, wards were the PSU from which households were selected directly [24]. Only households containing a woman aged 15–49 years the night before survey administration were eligible for interview. All women aged 15–49 years who were permanent residents of the selected household or visitors who stayed the night in the household the night before the survey were eligible to be interviewed. All men aged 15–49 years from every second household who were permanent residents of the selected household or visitors who stayed the night in the households the night before the survey were eligible to be interviewed. Full details of the NDHS sampling design are discussed elsewhere [24]. Blood pressure measurements were recorded in women and men only in the subsample of households selected for the male survey [24]. Daily cigarette smoking was recorded for all participants aged 15–49 years who were interviewed.

Participants included in this study were men and women aged 18–49 years who were interviewed and with a valid BP measurement. Participants taking BP lowering medication were excluded from the study. Of the 12862 total women aged 15–49 years in the survey, 6452 women had BP measured. After inclusion and exclusion criteria were applied (62 women with technically invalid BP readings, 796 women under 18 years, and 92 women on BP lowering medication) there were 5518 women for analysis. Of the 4063 total men aged 15–49 in the survey, 4040 men had BP measured. After the exclusions were applied (22 men with technically invalid BP readings, 543 men under 18 years, and 71 men on BP lowering medication) there were 3420 men for analysis.

Variables

Systolic BP (SBP) and diastolic BP (DBP) were the primary outcome variables. Blood pressure was measured three times in each participant at a minimum of five-minute intervals using the UA-767F/FAC blood pressure monitor (A&D Medical, Japan). The first measurement was discarded and the average of the second and third measurements was recorded as the final reading and recorded as a continuous variable (mm Hg) according to the standard DHS biomarker collection protocol [24].

Current cigarette smokers were defined as those who smoked cigarettes daily (manufactured or hand-rolled). Cigarette smokers were further categorized according to number of cigarettes smoked per day (up to 9, 10 or more). For men, this number was the average daily number of cigarettes in the past week, and for women, the number of cigarettes in the last 24 hours.

Demographic variables used to describe the participants were age (years), body mass index (BMI) (weight (kg)/height (m)2), education (Y/N), economic status (poorest wealth quintile; Y/N), social group (marginalised group, non-marginalised group), agricultural occupation (Y/N), place of residence (urban, rural), Province (1–7) and ecological zone (mountains, hills, Terai [plains]). For wealth quintile we used the NDHS household wealth index, derived from detailed information on dwelling and household characteristics, access to a variety of consumer goods and services, and assets [24]. Classification of participants as marginalised or non-marginalised was based on an ethnic grouping which is reflective of the social hierarchy in Nepal [28]. The marginalised group comprised Terai Dalit, Hill Dalit, Hill Janajati, Terai Janajati, Muslim and other Terai castes. Participants not in these groups were classified as non-marginalised.

In exploring the association between cigarette smoking and BP, age, body mass index, socioeconomic status and physical activity were considered potential confounders. Age was classified into the following sub-groups: 18–24 years, 25–34 years, 35–44 years, 45–49 years. Body mass index (BMI) was categorised according to the World Health Organization general population BMI classification into underweight (<18.5 kg/m2), normal (18.5 to 24.9 kg/m2), overweight (25.0 to 29.9 kg/m2) and obese (≥30.0 kg/m2). Socioeconomic status [29] was assessed through economic status (wealth quintile) and social group (marginalised ethnic group: Y/N). Physical activity was accounted for, in part, through the proxy variable of agricultural occupation (Y/N), with agricultural occupation representing higher physical activity [30, 31].

Statistical analysis

The data were analysed using IBM SPSS Ver 26.0 software (IBM Corp., Armonk, N.Y., USA). Data were weighted using sampling weights in accordance with DHS guidelines [32]. All analyses used the Complex Sample Analysis method to account for the multi-stage sample design [32]. Data from men and women were analysed separately. There were no missing data.

The relationship between smoking and BP was assessed with linear regression. The dependent variables in linear regression were the continuous variables SBP and DBP. The potential confounders were treated as categorical variables: age, BMI, wealth quintile, social group, and agricultural work. SBP and DBP were adjusted for age (through linear regression) after stratification into the four age groups (18–24 years, 25–34 years, 35–44 years, 45–49 years). All potential confounders which showed a significant association with BP (in either sex) in age-adjusted linear regression were included in the final multivariable linear regression models. The outcomes are presented as mean SBP and DBP with 95% confidence intervals.

Tests for interactions were also carried out, fitting a smoking X BMI interaction term in the models for men and women, with cigarette smoking fitted as a 2-category variable (Y/N) and BMI as a 4-category variable (underweight, normal, overweight, and obese).

Power analysis

Using the OpenEpi2 Sample Size calculator for power analysis (comparing two means) [33] and data from the 2016 NDHS (sample size, smoking prevalence and standard deviation), there was 80% statistical power to detect a 2.5 mm Hg mean difference in systolic BP between cigarette smokers and non-smokers in women, and a 1.8 mm Hg mean difference in men.

Ethics approval

The 2016 NDHS survey protocol was approved by the Nepal Health Research Council (NHRC) and the ICF Institutional Review Board prior to administration. Written informed consent was obtained from individual respondents prior to the interviews during the NDHS data collection. Access to the NDHS 2016 dataset for this project was granted by the DHS Program before the study was carried out. The study was also approved by the Human Research Ethics Committee of the University of Notre Dame Australia, Fremantle (Ref. 2020-066F).

Results

The characteristics of smokers in our sample (Table 1) showed several differences between men and women and to non-smokers. A smaller proportion of women (4.8%) smoked cigarettes daily than men (18.9%), and the same proportion (27%) of women and men smokers were moderate to heavy smokers (10 cigarettes or more per day [17]). While both men and women cigarette smokers had lower BMI than non-smokers, the mean difference in BMI was larger in women (2.1 units vs. 0.8 units in women and men respectively). A higher proportion of women smokers (41.8%) than men smokers (25.5%) were in the poorest wealth quintile, and a much higher proportion of women smokers (86.2%) had no formal education compared to men who smoked (16.8%) or to women who did not smoke (34.7%). Women smokers were, on average, older (mean 39.7 years) than non-smokers (mean 30.5 years) and men who smoked (mean 33.9 years). Women smokers were more often engaged in agricultural work (64.9%) than non-smokers (46.7%) and men who smoked (30.8%). The proportion of women smokers and men smokers in marginalised social groups was the same (68.8%).

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Table 1. Characteristics of participants by smoking status.

https://doi.org/10.1371/journal.pgph.0000045.t001

After adjustment for age, mean SBP and DBP were strongly associated with BMI category in both men and women, with significantly higher mean BP in overweight (4–6 mm) and obese (7–10 mm), and lower BP in underweight (3–6 mm), compared to the normal BMI group (Table 2). Men and women in the richest wealth quintile had significantly higher mean BP than those in middle wealth quintile (except for SBP in women), but this was a smaller effect (approximately 2–3 mm) than BMI. Men who were not engaged in agricultural work, but not women, had significantly higher BP than those who were, by approximately 2 mm. Mean BP was similar in the marginalised and non-marginalised social groups.

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Table 2. Age-adjusted mean SBP and DBP by confounding variables.

https://doi.org/10.1371/journal.pgph.0000045.t002

After age-adjustment, women smokers overall had significantly lower mean SBP (mean difference 3.9 mm; 95% CI 1.7–6.0 mm) and lower mean DBP (mean difference 3.4 mm, 95% CI 1.9–4.8 mm) than non-smokers (Table 3). There were no significant differences in BP between smokers and non-smokers in men, either before or after age-adjustment.

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Table 3. Unadjusted and age-adjusted mean SBP and DBP by smoking status.

https://doi.org/10.1371/journal.pgph.0000045.t003

Mean BP levels after adjustment for age, BMI, wealth quintile (socio-economic status) and agricultural occupation (proxy for physical activity) are shown stratified by age, BMI and smoking status in Table 4. In both men and women, SBP and DBP increased significantly with increasing age and BMI categories. In men, the mean increase in BP in overweight compared to normal weight was 6–7 mm, and 7–10 mm in obese. In women, the mean increase in BP in overweight compared to normal weight was approximately 5 mm, and 8–10 mm in obese. Women smokers had significantly lower SBP (mean difference 2.8 mm, 95% CI 0.7–4.8 mm) and lower DBP (mean difference 2.2 mm, 95% CI 0.9–3.6 mm) than non-smokers after adjustment. There were no significant differences in BP between smokers and non-smokers in males, either before or after adjustment. Tests for interaction between BMI and the smoking-SBP relationship were not significant in men or women.

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Table 4. Mean SBP and DBP: Unadjusted and adjusted for age, BMI, wealth quintile and agricultural occupation.

https://doi.org/10.1371/journal.pgph.0000045.t004

Discussion

This study used data from a nationally representative survey to examine the relationship between daily cigarette smoking and BP in Nepali adults aged 18–49 years. After adjustment for age, BMI and physical activity, no positive association was observed between cigarettes smoking and BP for men or for women. Indeed, women who smoked cigarettes had significantly lower BP than non-smoking women, by, on average, 2.8 mm and 2.2 mm for SBP and DBP respectively.

While smoking is a major risk factor for cardiovascular disease the association with increased BP is still unclear [34]. Our findings for men and women are consistent with many epidemiological studies showing that BP is either lower or the same in smokers as in non-smokers, the average difference being about 2–8 mm Hg for systolic pressure and 1–5 mm Hg for diastolic [35]. Globally, national surveys and longitudinal studies over the last twenty years have found different patterns for the smoking association with BP related to geography, sex and race. The Health Survey for England [17] found no significant difference in BP of male smokers aged 16–44 years compared to non-smokers, but in other national studies of men, notably in China and Japan, lower BP was found [20, 36]. A longitudinal study in the U.S. with a 30-year follow-up did not find a significant increase in SBP or DBP over time in male or female smokers, and white women smokers had lower DBP [34]. Studies in the UK, Sweden, Israel, and China [17, 22, 37, 38] have also found a lower BP in women current smokers compared to non-smokers, consistent with our findings, and a longitudinal study risk of incident hypertension in women conducted in the U.S. found cigarette smoking does not significantly increase the risk of incident hypertension in women smoking up to 15 cigarettes per day [15]. A meta-analysis of over 20 population-based studies conducted in many geographic locations concluded that there was no causal association between smoking heaviness in current smokers of either sex and SBP or DBP [39].

A systematic review of hypertension in low- and middle-income countries found geographic differences in the relationship between smoking and BP with lower proportions of hypertension among smokers compared to non-smokers in Europe and Central Asia, Latin America and Caribbean, and Middle East and North Africa regions, but higher proportions of hypertension amongst smokers compared to non-smokers in East Asia and Pacific, South Asia, and Sub-Saharan Africa regions [40]. Individual studies in Nepal have produced inconsistent findings, and many have not included adjustments for age and BMI [4147]. One study in periurban Kathmandu found an association between current cigarette smoking and higher BP after adjustment [18]. Other studies conducted in semi-urban and rural settings in Nepal [4850], and a nationwide survey [51], found current smoking was not significantly associated with BP in multivariable analyses. A systematic review and meta-analysis of 12 studies undertaken in Nepal in the last 20 years [52] estimated smokers have 1.43 times the odds (95% CI 1.14–1.79) of hypertension based on unadjusted odds ratios which did not control for confounders. Other recent systematic reviews and meta-analyses of the prevalence of hypertension in Nepal [53, 54] did not examine the effect of cigarette smoking. Two studies using data from the same 2016 NDHS survey as the present study to examine risk factors for hypertension [55, 56] used cigarette smoking in the 30 minutes prior to the BP measurement (Y/N) as their smoking variable and hence measured the short-term impact of nicotine on elevating BP [11], rather than the chronic effect of cigarette smoking on BP. The mixed results between studies demonstrate that methodological differences, different populations, and additional unmeasured confounders (e.g., lifestyle, diet, cultural characteristics, physical activity) may influence the observed relationship between smoking and BP.

The strength of our study compared to other studies conducted in Nepal is that we have used data from a nationally representative survey, rather than a specific geographical location, to examine the association between current cigarette smoking and BP. In addition, we used smoking variables that reflect daily smoking patterns rather than smoking in the 30 minutes before BP measurement. We adjusted for age, BMI, socioeconomic status and social differences, but we were unable to adjust for physical activity directly, dietary intake of fruit, vegetables and salt, or alcohol consumption, as these potential confounders were not collected in the 2016 NDHS. To account for physical activity, we used a proxy variable which provided a limited adjustment for this variable. Alcohol intake is strongly associated with smoking in Nepal [49] and may affect the smoking-BP relationship. However, in the study of Primatesta et al. [17] alcohol consumption did not alter smoking effects on BP for men or women. We also did not exclude individuals who had smoked or consumed alcohol or caffeine within 30 minutes before the BP readings, or who were users of smokeless tobacco, which may be a source of confounding. Other limitations include a lack of statistical power to undertake sub-group analysis by level of smoking. The use of office BP measurements in the NDHS rather than 24-hour ambulatory BP did not enable detection of BP changes throughout the day, including patterns such as the white-coat effect and masked hypertension [57]. In addition, cigarette smoking might act preferentially on central BP, rather than brachial BP, in the development of hypertensive target organ disease [12]. Since this is a cross-sectional study, we could not establish causality due to the lack of temporal relationship between smoking and BP.

Overall, we showed that BP is either lower or the same in daily cigarette smokers as in non-smokers in the age group 18–49 years. Our finding that women cigarette smokers, but not men smokers, had a significantly lower BP than non-smokers may reflect either, (1) the physiological effect of cigarette smoking per se in women, or (2) unmeasured dietary, lifestyle or health factors associated with low education, poverty, and agricultural work that were characteristics of the women smokers in the study and which may independently lower BP. Possible unmeasured factors include the consumption of a traditional diet (i.e., high in fiber and vegetables, low in fat) and less sedentary behaviour as part of a more traditional lifestyle [58]. While smoking was associated with a lower BMI in women, BMI was adjusted for in the final model.

Conclusions

This study describes the association between cigarette smoking and blood pressure in adults in Nepal aged 18–49 years. Our finding that daily cigarette smoking was not associated with increased BP in men or women in this population contributes to the understanding of the relationship between these two major risk factors for cardiovascular disease in populations that share characteristics with Nepal. However, the results of this study should not be used to influence the public health campaigns on smoking cessation as cigarette smoking is a strong independent risk factor for cardiovascular disease. Future research employing longitudinal studies, the use of ambulatory BP monitoring, adjusting for additional confounders, and studies on arterial stiffness and central blood pressure [12] may provide further insight into of the effect of smoking on BP.

Acknowledgments

We acknowledge the contribution of the Demographic and Health Survey (DHS) program for providing access to the 2016 Nepal dataset.

References

  1. 1. World Health Organization. Global Health Estimates 2020: Deaths by cause, age, sex, by country and by region, 2000–2019. Geneva; 2020.
  2. 2. Nepal Health Research Council (NHRC), Ministry of Health and Population (MoHP), Monitoring Evaluation and Operational Research (MEOR). Nepal Burden of Disease 2017: A Country Report based on the Global Burden of Disease 2017 Study. Kathmandu, Nepal; 2019.
  3. 3. Njolstad I, Arnesen E, Lund-Larsen PG. Smoking, serum lipids, blood pressure, and sex differences in myocardial infarction. A 12-year follow-up of the Finnmark Study. Circulation. 1996;93(3):450–6. pmid:8565161
  4. 4. Prescott E, Hippe M, Schnohr P, Hein HO, Vestbo J. Smoking and risk of myocardial infarction in women and men: longitudinal population study. BMJ. 1998;316(7137):1043–7. pmid:9552903
  5. 5. Lewington S, Clarke R, Qizilbash N, Peto R, Collins R, Prospective Studies C. Age-specific relevance of usual blood pressure to vascular mortality: a meta-analysis of individual data for one million adults in 61 prospective studies. The Lancet. 2002;360(9349):1903–13. pmid:12493255
  6. 6. De Cesaris R, Ranieri G, Filitti V, Bonfantino MV, Andriani A. Cardiovascular effects of cigarette smoking. Cardiology. 1992;81(4–5):233–7. pmid:1301248
  7. 7. Forman JP, Stampfer MJ, Curhan GC. Diet and lifestyle risk factors associated with incident hypertension in women. JAMA. 2009;302(4):401–11. pmid:19622819
  8. 8. Sonne-Holm S, Sorensen TI, Jensen G, Schnohr P. Independent effects of weight change and attained body weight on prevalence of arterial hypertension in obese and non-obese men. BMJ. 1989;299(6702):767–70. pmid:2508915
  9. 9. Carnethon MR, Evans NS, Church TS, Lewis CE, Schreiner PJ, Jacobs DR Jr., et al. Joint associations of physical activity and aerobic fitness on the development of incident hypertension: coronary artery risk development in young adults. Hypertension. 2010;56(1):49–55. pmid:20516395
  10. 10. Benowitz NL. Cigarette smoking and cardiovascular disease: pathophysiology and implications for treatment. Progress in Cardiovascular Diseases. 2003;46(1):91–111. pmid:12920702
  11. 11. Benowitz NL. Clinical pharmacology of nicotine. Annu Rev Med. 1986;37:21–32. pmid:3518606
  12. 12. Virdis A, Giannarelli C, Neves MF, Taddei S, Ghiadoni L. Cigarette smoking and hypertension. Curr Pharm Des. 2010;16(23):2518–25. pmid:20550499
  13. 13. Benowitz NL, Sharp DS. Inverse relation between serum cotinine concentration and blood pressure in cigarette smokers. Circulation. 1989;80(5):1309–12. pmid:2805267
  14. 14. Chiolero A, Faeh D, Paccaud F, Cornuz J. Consequences of smoking for body weight, body fat distribution, and insulin resistance. Am J Clin Nutr. 2008;87(4):801–9. pmid:18400700
  15. 15. Bowman TS, Gaziano JM, Buring JE, Sesso HD. A prospective study of cigarette smoking and risk of incident hypertension in women. J Am Coll Cardiol. 2007;50(21):2085–92. pmid:18021879
  16. 16. Groppelli A, Giorgi DM, Omboni S, Parati G, Mancia G. Persistent blood pressure increase induced by heavy smoking. J Hypertens. 1992;10(5):495–9. pmid:1317911
  17. 17. Primatesta P, Falaschetti E, Gupta S, Marmot MG, Poulter NR. Association between smoking and blood pressure: evidence from the health survey for England. Hypertension. 2001;37(2):187–93. pmid:11230269
  18. 18. Dhungana RR, Pandey AR, Bista B, Joshi S, Devkota S. Prevalence and associated factors of hypertension: A community-based cross-sectional study in municipalities of Kathmandu, Nepal. Int J Hypertens. 2016;2016:1656938. pmid:27293880
  19. 19. Kim BJ, Han JM, Kang JG, Kim BS, Kang JH. Association between cotinine-verified smoking status and hypertension in 167,868 Korean adults. Blood Press. 2017;26(5):303–10. pmid:28643526
  20. 20. Li G, Wang H, Wang K, Wang W, Dong F, Qian Y, et al. The association between smoking and blood pressure in men: a cross-sectional study. BMC Public Health. 2017;17(1):797. pmid:29017534
  21. 21. Rhee MY, Na SH, Kim YK, Lee MM, Kim HY. Acute effects of cigarette smoking on arterial stiffness and blood pressure in male smokers with hypertension. Am J Hypertens. 2007;20(6):637–41. pmid:17531920
  22. 22. Green MS, Jucha E, Luz Y. Blood pressure in smokers and nonsmokers: Epidemiologic findings. American Heart Journal. 1986;111(5):932–40. pmid:3706114
  23. 23. Imamura H, Tanaka K, Hirae C, Futagami T, Yoshimura Y, Uchida K, et al. Relationship of cigarette smoking to blood pressure and serum lipids and lipoproteins in men. Clin Exp Pharmacol Physiol. 1996;23(5):397–402. pmid:8713678
  24. 24. Ministry of Health Nepal (MOHN), New ERA, ICF. Nepal Demographic and Health Survey 2016. Kathmandu, Nepal; 2017. Available from: https://dhsprogram.com/publications/publication-fr336-dhs-final-reports.cfm.
  25. 25. Gupta RD, Talukdar A, Haider SS, Haider MR. Prevalence and associated factors of hypertension subtypes among the adult population in Nepal: Evidence from Demographic and Health Survey data. Osong Public Health Res Perspect. 2019;10(6):327–36. pmid:31897361
  26. 26. Hasan M, Sutradhar I, Akter T, Das Gupta R, Joshi H, Haider MR, et al. Prevalence and determinants of hypertension among adult population in Nepal: Data from Nepal Demographic and Health Survey 2016. PLoS One. 2018;13(5):e0198028. pmid:29852006
  27. 27. Kibria GMA, Swasey K, Sharmeen A, Sakib MN, Burrowes V. Prevalence and associated factors of pre-hypertension and hypertension in Nepal: Analysis of the Nepal Demographic and Health Survey 2016. Health Sci Rep. 2018;1(10):e83. pmid:30623039
  28. 28. Devkota B, Maskey J, Pandey AR, Karki D, Godwin P, Gartoulla P, et al. Determinants of home delivery in Nepal—A disaggregated analysis of marginalised and non-marginalised women from the 2016 Nepal Demographic and Health Survey. PLoS One. 2020;15(1):e0228440. pmid:31999784
  29. 29. Busingye D, Arabshahi S, Subasinghe AK, Evans RG, Riddell MA, Thrift AG. Do the socioeconomic and hypertension gradients in rural populations of low- and middle-income countries differ by geographical region? A systematic review and meta-analysis. Int J Epidemiol. 2014;43(5):1563–77. pmid:24867304
  30. 30. Patel S, Ram U, Ram F, Patel SK. Socioeconomic and demographic predictors of high blood pressure, diabetes, asthma and heart disease among adults engaged in various occupations: evidence from India. J Biosoc Sci. 2020;52(5):629–49. pmid:31647045
  31. 31. Sorensen TB, Matsuzaki M, Gregson J, Kinra S, Kadiyala S, Shankar B, et al. Is agricultural engagement associated with lower incidence or prevalence of cardiovascular diseases and cardiovascular disease risk factors? A systematic review of observational studies from low- and middle-income countries. PLoS One. 2020;15(3):e0230744. pmid:32231387
  32. 32. Croft TN, Aileen MJM, C. K., Allen EA. Guide to DHS Statistics2018. Available from: https://dhsprogram.com/data/Guide-to-DHS-Statistics/index.cfm.
  33. 33. Dean AG, Sullivan KM, Soe MM. OpenEpi: Open Source Epidemiologic Statistics for Public Health 2013. Available from: https://www.openepi.com/SampleSize/SSCohort.htm.
  34. 34. Luehrs RE, Zhang D, Pierce GL, Jacobs DR Jr., Kalhan R, Whitaker KM. Cigarette smoking and longitudinal associations with blood pressure: The CARDIA Study. J Am Heart Assoc. 2021;10(9):e019566. pmid:33902307
  35. 35. Pickering TG. Effects of stress and behavioral interventions in hypertension—the effects of smoking and nicotine replacement therapy on blood pressure. J Clin Hypertens (Greenwich). 2001;3(5):319–21. pmid:11588411
  36. 36. Okubo Y, Suwazono Y, Kobayashi E, Nogawa K. An association between smoking habits and blood pressure in normotensive Japanese men: a 5-year follow-up study. Drug Alcohol Depend. 2004;73(2):167–74. pmid:14725956
  37. 37. Janzon E, Hedblad B, Berglund G, Engstrom G. Changes in blood pressure and body weight following smoking cessation in women. J Intern Med. 2004;255(2):266–72. pmid:14746564
  38. 38. Wang M, Li W, Zhou R, Wang S, Zheng H, Jiang J, et al. The paradox association between smoking and blood pressure among half million Chinese people. Int J Environ Res Public Health. 2020;17(8). pmid:32325946
  39. 39. Linneberg A, Jacobsen RK, Skaaby T, Taylor AE, Fluharty ME, Jeppesen JL, et al. Effect of smoking on blood pressure and resting heart rate: A Mendelian randomization meta-analysis in the CARTA Consortium. Circ Cardiovasc Genet. 2015;8(6):832–41. pmid:26538566
  40. 40. Sarki AM, Nduka CU, Stranges S, Kandala NB, Uthman OA. Prevalence of hypertension in low- and middle-income countries: A systematic review and meta-analysis. Medicine (Baltimore). 2015;94(50):e1959. pmid:26683910
  41. 41. Vaidya A, Pokharel PK, Karki P, Nagesh S. Exploring the iceberg of hypertension: a community based study in an eastern Nepal town. Kathmandu University Medical Journal. 2007;5(3):349–59.
  42. 42. Manandhar K, Koju R, Sinha NP, Humagain S. Prevalence and associated risk factors of hypertension among people aged 50 years and more in Banepa Municipality, Nepal. Kathmandu Univ Med J (KUMJ). 2012;10(39):35–8. pmid:23434959
  43. 43. Shrestha DB, Dhungel S. Prevalence and risk factors of hypertension in Hansposa VDC of Sunsari District, Nepal. Medical Journal of Shree Birendra Hospital. 2017;15(2):48–53.
  44. 44. Shrestha S, Devkota R. Prevalence of hypertension and its associated risk factors in a sub-urban area of central Nepal. International Journal of Community Medicine and Public Health. 2016:2477–86.
  45. 45. Maharjan B. Prevalence and Awareness of Hypertension among Adults and its Related Risk Factors. J Nepal Health Res Counc. 2017;15(3):242–6.
  46. 46. Kafle R, Sharma D, Paudel N, Sapkota S, Alurkar V. Prevalence and associated risk factors of hypertension in a rural community of Western Nepal: A cross sectional study. Journal of Advances in Internal Medicine. 2018;7(1):11–6.
  47. 47. Karki DK, Nepal S, Bhandari KR. Prevalence and associated risk factors of hypertension among adults in Palpa District, Nepal. Journal of Lumbini Medical College. 2019;7(2):107–12.
  48. 48. Chataut J, Khanal K, Manandhar K. Prevalence and associated factors of hypertension among adults in rural Nepal: A community based study. Kathmandu Univ Med J (KUMJ). 2015;13(52):346–50. pmid:27423286
  49. 49. Khanal MK, Dhungana RR, Bhandari P, Gurung Y, Paudel KN. Prevalence, associated factors, awareness, treatment, and control of hypertension: Findings from a cross sectional study conducted as a part of a community based intervention trial in Surkhet, Mid-western region of Nepal. PLoS One. 2017;12(10):e0185806. pmid:28982159
  50. 50. Gyawali B, Mishra SR, Ghimire S, Hansen MRH, Shah KJ, Subedee KC, et al. The burden and correlates of multiple cardiometabolic risk factors in a semi-urban population of Nepal: a community-based cross-sectional study. Sci Rep. 2019;9(1):15382. pmid:31653888
  51. 51. Koju R, Manandhar K, Risal A, Steiner TJ, Holen A, Linde M. Undertreated hypertension and its implications for public health in Nepal: Nationwide population-based survey. Kathmandu Univ Med J (KUMJ). 2015;13(49):3–7. pmid:26620741
  52. 52. Shrestha DB, Budhathoki P, Sedhai YR, Baniya A, Lamichhane S, Shahi M, et al. Prevalence, awareness, risk factors and control of hypertension in Nepal from 2000 to 2020: A systematic review and meta-analysis. Public Health in Practice. 2021;2:100119.
  53. 53. Dhungana RR, Pandey AR, Shrestha N. Trends in the prevalence, awareness, treatment, and control of hypertension in Nepal between 2000 and 2025: A systematic review and meta-analysis. Int J Hypertens. 2021;2021:6610649. pmid:33747559
  54. 54. Huang Y, Guo P, Karmacharya BM, Seeruttun SR, Xu DR, Hao Y. Prevalence of hypertension and prehypertension in Nepal: a systematic review and meta-analysis. Glob Health Res Policy. 2019;4:11. pmid:31165100
  55. 55. Agho KE, Osuagwu UL, Ezeh OK, Ghimire PR, Chitekwe S, Ogbo FA. Gender differences in factors associated with prehypertension and hypertension in Nepal: A nationwide survey. PLoS One. 2018;13(9):e0203278. pmid:30212519
  56. 56. Hasan MM, Tasnim F, Tariqujjaman M, Ahmed S, Cleary A, Mamun A. Examining the prevalence, correlates and inequalities of undiagnosed hypertension in Nepal: a population-based cross-sectional study. BMJ Open. 2020;10(10):e037592. pmid:33004393
  57. 57. Muntner P, Shimbo D, Carey RM, Charleston JB, Gaillard T, Misra S, et al. Measurement of blood pressure in humans: A scientific statement from the American Heart Association. Hypertension. 2019;73(5):e35–e66. pmid:30827125
  58. 58. Aryal KK, Neupane S, Mehata S, Vaidya A, Singh S, Paulin F, et al. Non communicable diseases risk factors: STEPS Survey Nepal 2013. Kathmandu, Nepal; 2015.