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PLOS ONE logoLink to PLOS ONE
. 2021 Jul 30;16(7):e0253605. doi: 10.1371/journal.pone.0253605

Prevalence of non-communicable diseases risk factors and their determinants: Results from STEPS survey 2019, Nepal

Bihungum Bista 1,#, Meghnath Dhimal 1,*,#, Saroj Bhattarai 1, Tamanna Neupane 1, Yvonne Yiru Xu 2, Achyut Raj Pandey 1, Nick Townsend 3, Pradip Gyanwali 1, Anjani Kumar Jha 1
Editor: Brecht Devleesschauwer4
PMCID: PMC8323895  PMID: 34329300

Abstract

Background

The World Health Organization (WHO) recommends ongoing surveillance of non-communicable diseases (NCDs) and their risk factors, using the WHO STEPwise approach to surveillance (STEPS). The aim of this study was to assess the distribution and determinants of NCD risk factors in Nepal, a low-income country, in which two-thirds (66%) of annual deaths are attributable to NCDs.

Methods

A nationally representative NCD risk factors STEPS survey (instrument version 3.2), was conducted between February and May 2019, among 6,475 eligible participants of age 15–69 years sampled from all 7 provinces through multistage sampling process. Data collection involved assessment of behavioral and biochemical risk factors. Complex survey analysis was completed in STATA 15, along with Poisson regression modelling to examine associations between covariates and risk factor prevalence.

Results

The most prevalent risk factor was consumption of less than five servings of fruit and vegetables a day (97%; 95% CI: 94.3–98.0). Out of total participants, 17% (95% CI: 15.1–19.1) were current smoker, 6.8% (95% CI: 5.3–8.2) were consuming ≥60g/month alcohol per month and 7.4% (95% CI:5.7–10.1) were having low level of physical activity. Approximately, 24.3% (95% CI: 21.6–27.2) were overweight or obese (BMI≥25kg/m2) while 24.5% (95% CI: 22.4–26.7) and 5.8% (95% CI: 4.3–7.3) had raised blood pressure (BP) and raised blood glucose respectively. Similarly, the prevalence of raised total cholesterol was 11% (95% CI: 9.6–12.6). Sex and education level of participants were statistically associated with smoking, harmful alcohol use and raised BP. Participants of age 30–44 years and 45–69 years were found to have increased risk of overweight, raised BP, raised blood sugar and raised blood cholesterol. Similarly, participants in richest wealth quintile had higher odds of insufficient physical inactivity, overweight and raised blood cholesterol. On average, each participant had 2 NCD related risk factors (2.04, 95% CI: 2.02–2.08).

Conclusion

A large portion of the Nepalese population are living with a variety of NCD risk factors. These surveillance data should be used to support and monitor province specific NCD prevention and control interventions throughout Nepal, supported by a multi-sectoral national coordination mechanism.

Introduction

Non-communicable diseases (NCDs) are the leading causes of disease burden worldwide [1]. According to World Health Organization (WHO) estimates, NCDs are responsible for 71% of all deaths globally, with around 85% of premature deaths from NCDs occurring in low and middle income countries (LMICs) [2]. Behavioral risk factors including smoking, alcohol consumption, unhealthy diet and physical inactivity, along with biological risk factors such as raised blood pressure (BP), blood glucose and cholesterol level, along with overweight and obesity have been identified as the major underlying causes of NCDs [3]. In addition, the risk of progression of NCDs is reported to increase with the co-existence of multiple risk factors within the same individual, which is referred to as clustering [46].

Data from Nepal, a lower middle income country in South Asia, indicate an 8% increase in deaths caused by NCDs between 2014 and 2016 [7,8] with two-thirds (66%) of the 182,751 deaths recorded in Nepal in 2017, attributed to NCDs [1]. A 2019 population based nationwide cross-sectional study in Nepal also indicating the high burden of NCDs with a high prevalence of COPD, diabetes, chronic kidney disease, and coronary artery disease, which could pose a serious challenge to health systems in the near future. Apart from these diseases, diabetes mellitus is recognized to affect a notable proportion (8.5%) of the adult population in Nepal [9]. The 2013 STEPS Survey in Nepal also confirming the high prevalence of various risk factors including smoking (19%), low consumption of fruits and vegetables (99%), raised BP (26%), and abnormal lipids (23%) [10]. Likewise, a substantial proportion of the Nepalese population was found to be hypertensive (19.9%) with more than one fifth overweight or obese (21.4%) [11].

To combat NCDs at a population level, the Nepal government adopted a Multisectoral Action Plan for the Prevention and Control of Non-Communicable Diseases in 2014 [12], aligning with the NCD global monitoring framework [13]. One of the key activities identified and included in the multisectoral action plan was to have a periodic NCD STEPS survey to track progress on prevention and control of NCDs within the country. With recent transition to federal structure, Nepal also needs evidence on NCD risk factors at provincial level so as to facilitate decision making process in health sector. In this context, this study aimed to assess the epidemiological distribution and determinants of behavioral (tobacco, alcohol, diet, salt consumption, physical activity) and biological risk factors (overweight/obesity, raised BP, raised blood sugar and cholesterol levels) associated with major/selected NCDs in.

Methods

Study settings

Nepal is a landlocked country situated in Southern Asia between India and China. The country runs from a plain area in the south, known as Terai, to the mountainous area of the Himalayas in the north, with a hilly region in between the two. Administratively, Nepal is comprised of 7 provinces, 77 districts and 753 local bodies.

Study design and sampling techniques

It was a nationally-representative cross-sectional NCD risk factors survey, following the WHO STEPwise approach to surveillance (STEPS), an integrated surveillance tool through which countries can collect, analyse and disseminate core standardized information on NCDs [14]. Data for the survey was collected from the eligible adult population, aged between 15 and 69 years, between February and May 2019.

Sampling for the survey took into consideration the current federal structure of Nepal, such that findings could be generalized to the provincial levels. A multistage cluster sampling method was used to select 6,475 eligible participants across all 7 provinces in Nepal. A total of 259 wards were selected as the primary sampling units (PSU) at the first stage, maintaining 37 PSUs from each province. The household listing operation was carried out in 259 PSUs, in order to develop a sampling frame for selection of individual households at the second stage. From the prepared list of the households, 25 households per PSU were sampled using systematic random sampling, after determining the sampling interval by dividing the number of listed households by 25. From each of the selected households, one adult member of age 15–69 years was sampled randomly for participation in the survey using an android tablet. This household listing process provided greater rigor to the sampling process than for previous STEPS surveys. Further details on the sampling process can be found elsewhere [14].

Variable definition

For this study, current smoking, harmful use of alcohol, insufficient fruit and vegetable intake, insufficient physical activity are considered as a behavioral factor. Similarly, overweight and raised BP, are categorized as a physical factor. Raised blood sugar and raised blood cholesterol together are considered as biochemical factor. The operational definitions of the outcome variables (NCD risk factor) are presented in Table 1.

Table 1. Variables definition.

Variables Definitions
Current smoker Participants those who had smoked in the past 30 days were considered as current smoker for this survey.
Harmful use of alcohol Consumption of ≥60 gm of pure alcohol on an average day in the past 30 days was considered harmful use.
Insufficient fruits and vegetables intake Participants who ate less than five servings of fruits and vegetables per day were considered to have insufficient fruit and vegetable intake.
Insufficient Physical activity Participants who participated in less than the equivalent of 150 minutes of moderate intensity (600 METs) physical activity per week were categorized as having insufficient physical activity.
Overweight Participants with a BMI ≥ 25 kg/m2, had classified them as being overweight.
Raised BP Participants were classified as having raised BP if the average 2nd and 3rd measurement of systolic BP was ≥140 mmHg, or the average diastolic BP was ≥90 mmHg, or if they reported to be taking antihypertensive medication.
Raised blood sugar Participants with a fasting blood sugar ≥126 mg/dl, or those currently taking medications to lower blood sugar, were considered to have raised blood sugar.
Raised blood cholesterol Participants whose blood cholesterol was above 190 mg/dl, or those currently taking medications to lower blood cholesterol, were considered to have raised blood cholesterol

Data collection

We conducted face to face interviews using standardized questions from the STEPS Survey (version 3.2) [15]–an update on the 2013 STEPs survey. The survey collected information related to behavioral (tobacco use, alcohol use, physical activity, fruits and vegetables intake) (STEP I), physical (height, weight and BP) (STEP II) and biochemical measures (Blood sugar, sodium level measurement in urine) (STEP III). Measurement of height, weight (measured using SECA weighing machine, Germany), BP (measured using OMRON BP monitor), blood sugar (measured using Cardiocheck PA) and blood cholesterol (measured using Cardiocheck PA) were made as per the WHO STEPS manual. Details of the measurement process has been described in more detail elsewhere [14,16].

The survey also included questions related to tobacco policy, alcohol policy and programs. Furthermore, it included questions related to violence and injury, along with musculoskeletal pain. In addition, in this round of the STEPs survey dietary salt intake level was estimated via spot urine collection, along with that concentrations of blood glucose and total cholesterol was measured using CardioCheck, PA, as recommended by the WHO.

Statistical analysis

Analysis was performed with STATA version 15.1 using survey (svy) set command, defining clusters and sampling weight information. All estimates were weighted by sample weights and are presented with 95% confidence intervals (CI). Prevalence estimates were calculated using Taylor series linearization. Chi-square tests were used for bivariate analysis, to test associations between independent and dependent variables. Furthermore, Poisson regression was used to calculate the adjusted prevalence ratio (APR) between each NCD risk factors and sociodemographic covariates (age, sex, education, marital status, province, ecological belt and place of residence) included simultaneously [17]. For clustering analysis of NCD risk factors, the numbers of risk factors present within each participant were summed (from 0 to 5) and was analyzed against socio-demographic covariates through Poisson regression. The relationship between the number of risk factors and covariates was estimated through adjusted relative risk ratios (ARR), with the number of risk factors designated as the dependent variable.

Ethical considerations

Ethical approval to conduct this survey was granted from the Ethical Review Board (ERB) of the Nepal Health Research Council (NHRC), Government of Nepal (Registration number 293/2018). Written informed consent was obtained from each participant before they enrolled in the survey. In case of minors (under 18 years old) both assent from the research participants and consent from their parents (legal guardian) was obtained, as per national ethical guidelines for health research in Nepal. We also took administrative approval from federal, provincial and local governments, as per the need. The confidentiality of all information gathered was maintained. Any waste generated during the laboratory procedures was properly disinfected using aseptic techniques before being safely disposed of. All blood and urine samples were discarded after completing biochemical measurements.

Results

Characteristics of participants

Out of 6,475 participants approached for participation, 5,593 individuals participated in the study, a response rate of 86%. Just over half of the participants (53%) were female. Forty five percent (45%) of participants were aged between 15 and 29 years, with 29% aged 30 to 44 years and 26% 45to 69. Around one fifth of participants were from Lumbini Province (21%), with 19% from province 2, with the lowest proportion coming from Karnali Province (6%) and Gandaki Province (8%). Over half (57%) were from the Terai belt. Two-fifths (40%) of the participants had not completed their primary level education and approximately 46% were working as a homemaker. Just under 78% of the participants were currently married (Table 2).

Table 2. Prevalence of NCD risk factors among socio-demographic characteristics.

Characteristics of participants Total Current smoker harmful use of alcohol Insufficient fruit/vegetable use Physical inactivity) Overweight (%) Raised BP Raised blood sugar Raised cholesterol level
N (%) n % (95% CI) n % (95% CI) N % (95% CI) n % (95% CI) n % (95% CI) n % (95% CI) N % (95% CI) n % (95% CI)
Age
 15–29 1466 (44.9) 1466 11.7 (8.8–15.5) 1466 5.3 (3.4–8.0) 1462 96.4 (92.8–98.2) 1441 7.8 (5.5–11.0) 1407 17.2 (13.9–21.1) 1441 12.9 (10.6–15.8) 1356 2.48 (1.4-.5) 1390 5.9 (4.3–8.0)
 30–44 2039 (28.8) 2039 17.6 (14.9–20.6) 2039 7.6 (5.8–9.8) 2029 96.6 (94.6–97.9) 1997 5.8 (3.9–8.3) 2020 32.8 (29.2–36.5) 2016 25.6 (22.6–28.9) 1876 6.7 (4.9–9.1) 1944 12 (9.8–14.6)
 45–69 2088 (26.3) 2088 25.8 (22.9–28.9) 2088 8.5 (6.6–10.9) 2076 97.0 (94.9–98.3) 2055 9.3 (6.9–12.3) 2072 27.4 (23.9–31.1) 2049 42.9 (39.5–46.3) 1959 10.2 (8.1–12.7) 2016 18.7 (16.4–21.3)
 P-Value <0.001 0.082 0.746 <0.001 <0.001 <0.001 <0.001 <0.001
Sex
 Female 3595 (53%) 3595 7.5 (6.2–8.9) 3595 1.75 (1.0–3.0) 3578 96.3 (93.2–98.0) 3529 7.3 (5.2–10.02) 3507 25.1 (22.2–28.3) 3540 19.7 (17.3–22.2) 3357 5.3 (4.1–6.8) 3443 14.0 (12.1–16.1)
 Male 1998 (47.0) 1998 27.9 (24.6–31.6) 1998 12.4 (10.0–15.4) 1989 97 (94.8–98.3) 1964 8.1 (5.5–11.6) 1992 23.7 (20.1–27.6) 1966 29.8 (26.6–33.1) 1834 6.3 (4.6–8.5) 1907 7.8 (6.2–9.7)
 P-Value <0.001 <0.001 0.405 0.222 0.454 <0.001 0.225 <0.001
Level of education*
None/less than primary 2792 (39.7) 2792 21.6 (18.9–24.6) 2792 6.9 (5.2–9.3) 2772 98.1 (95.9–99.1) 2732 6.9 (4.9–9.5) 2758 24.9 (21.9–28.3) 2741 31.8 (28.7–35.1) 2595 6.2 (4.8–8.1) 2666 14.9 (12.8–17.3)
Primary 1051 (20.1) 1051 16 (11.8–21.4) 1051 8.7 (6.1–12.2) 1049 96.8 (93.6–98.4) 1032 9.5 (6.4–14.0) 1033 24.6 (20.4–29.4) 1037 25.3 (21.2–29.9) 975 6.5 (4.6–9.2) 1007 10.42 (7.7–14.0)
Secondary 1088 (24.9) 1088 15.4 (12.2–19.1) 1088 6.8 (4.8–9.7) 1084 97.6 (95.3–98.8) 1074 6.9 (4.3–11.2) 1067 22.9 (18.8–27.5) 1077 18.3 (14.9–22.2) 1005 5.4 (3.2–8.9) 1041 6.20 (4.7–8.2)
More than secondary 661 (15.3) 661 9.8 (6.3–14.8) 661 3.78 (2.2–6.4) 661 91.2 (81.7–95.9) 654 8.2 (5.3–12.3) 640 25.2 (19.1–32.6) 650 14.7 (10.9–19.4) 615 4.1 (2.3–7.2) 635 10.1 (6.9–14.3)
P-Value <0.001 0.079 <0.001 <0.001 0.835 <0.001 0.475 <0.001
Residence
 Metropolitian 705 (8.9%) 705 12.5 (7.9–19.2) 705 5.3 (2.4–11.3) 704 87.8 (64.9–96.5) 699 6.4 (2.8–13.9) 694 33.5 (26.7–41.1) 679 25.2 (19.8–31.5) 648 10.5 (5.3–19.6) 668 9.7 (6.6–14.0)
 Municipality 2755 (53.8) 2755 17.21 (14.9–19.8) 2755 6.9 (5.3–9.1) 2734 96.9 (94.1–98.4) 2700 9.4 (6.6–13.1) 2702 27.0 (22.9–31.6) 2719 24.8 (21.9–28.0) 2570 6.1 (4.4–8.5) 2638 11.7 (9.8–13.9)
 Rural municipality 2133 (37.2) 2133 18.1 (14.6–22.3) 2133 6.9 (4.7–9.9) 2129 98.4 (97.2–99.0) 2094 5.4 (2.9–9.7) 2103 18.5 (14.9–22.5) 2108 23.8 (20.5–27.4) 1973 4.16 (2.7–6.1) 2044 10.5 (8.1–13.5)
 P-Value 0.339 0.809 <0.001 0.276 <0.001 0.855 0.052 0.583
Province
Province 1 804 (18.3) 804 10.4 (7.4–14.4) 804 5.7 (3.4–9.55) 802 96.4 (88.2–98.9) 799 3.6 (1.6–7.9) 790 25.5 (19.9–31.9) 795 26.61 (21.2–32.8) 743 4.40 (3.1–6.2) 765 14.8 (10.8–19.8)
Province 2 803 (19.5) 803 13.93 (10.7–17.9) 803 3.72 (2.3–5.92) 792 96.4 (89.5–98.8) 796 8.55 (3.8–17.9) 794 19.7 (14.5–26.3) 796 18.7 (14.0–24.4) 759 11.3 (7.4–16.9) 770 11.5 (8.2–15.9)
Bagmati 759 (16.2) 759 18.8 (14.3–24.4) 759 8.72 (5.0–14.8) 759 97.2 (94.1–98.7) 748 10.3 (6.8–15.3) 755 42.8 (35.4–50.5) 732 25.2 (20.1–31.1) 687 4.1 (2.3–7.2) 718 8.2 (6.2–10.7)
Gandaki province 793 (8.1) 793 18.9 (15.3–23.2) 793 8.5 (5.2–13.6) 791 98.9 (97.7–99.6) 778 10.12 (4.9–19.6) 787 35.4 (28.7–42.7) 786 29.9 (26.6–33.5) 757 3.2 (1.8–5.5) 765 12.9 (9.7–17.1)
Lumbini Province 797 (20.6) 797 17.6 (12.5–24.1) 797 7.8 (4.6–12.8) 792 94.4 (82.6–98.4) 789 7.3 (3.6–14.1) 783 19.6 (15.8–24.2) 780 28.2 (24.1–32.8) 748 6.4 (3.9–10.3) 766 11.6 (8.7–15.4)
Karnali province 808 (5.6) 808 21.6 (17.9–25.7) 808 8.8 (5.7–13.3) 806 96.9 (93.3–98.6) 791 4.2 (1.9–8.8) 788 11.4 (8.21–15.6) 802 21.4 (17.2–26.3) 763 0.7 (0.4–1.4) 770 4.7 (3.2–6.84)
Sudurpaschim province 829 (11.8) 829 26.4 (21.8–31.5) 829 7.0 (4.5–10.7) 825 98.8 (97.7–99.4) 792 9.4 (4.4–18.9) 802 11.5 (8.7–15.3) 815 20.9 (16.9–25.7) 734 3.9 (1.5–9.7) 796 9.6 (6.6–13.8)
P-Value <0.001 0.248 0.504 0.122 <0.001 0.021 <0.001 0.023
Ecological belt
 Mountain 661 (10.8) 661 27.4 (22.4–33.0) 12.7 (7.7–20.2) 99.3 (98.4–99.7) 7.85 (5.1–11.9) 23.8 (17.5–31.5) 24.8 (18.6–32.2) 1.01 (0.4–2.7) 5.7 (3.8–8.4)
 Hill 2606 (31.8%) 2606 16.6 (13.8–19.8) 7.4 (5.7–9.61) 97.9 (96.6–98.8) 6.3 (4.6–8.7) 31.5 (26.1–37.5) 27.1 (24–30.4) 3.0 (2.0–4.5 10.4 (8.1–13.3)
 Terai 2326 (57.5) 2326 15.5 (12.9–18.5) 5.3 (3.7–7.5) 95.4 (91.1–97.7) 6.7 (4.9–8.8) 20.6 (17.7–23.8) 22.9 (20.1–26.1) 8.2 (6.2–10.5) 12.5 (10.5–14.8)
 P-Value <0.001 <0.001 0.011 0.299 0.001 0.217 <0.001 0.009
Wealth Quintile
Poorest 1653 (20.0) 1653 23.24 (19.78–27.11) 1653 9.11 (6.59–12.46) 1641 98.61 (96.98–99.37) 1612 4.23 (2.55–6.95) 1619 16.95 (13.67–20.83) 1630 26.85 (23.27–30.75) 1533 2.67 (1.61–4.41) 1589 6.98 (5.30–9.15)
Second quintile 1062 (20) 1062 17.1 (13.9–20.9) 1062 6.4 (4.7–8.7) 1054 99.5 (98.8–99.8) 1049 5.9 (3.8–9.3) 1043 21.5 (17.9–25.5) 1042 22.4 (19.0–26.3) 998 4.2 (2.7–6.5) 1020 10.9 (7.7–15.3)
Third quintile 949 (20.1) 949 15.7 (12.6–19.3) 949 7.4 (4.8–11.3) 947 97.9 (95.8–99.0) 930 7.0 (4.1–11.7) 928 22.8 (18.2–28.2) 929 24.7 (20.1–29.9) 890 6.5 (3.9–10.5) 905 11.3 (8.5–14.8)
Fourth quintile 878 (20.1) 878 15.8 (12.2–20.2) 878 4.7 (2.9–7.6) 876 95.9 (92.4–97.8) 868 7.6 (5.3–10.9) 867 23.9 (19.3–29.1) 869 24.5 (20.2–29.5) 803 6.8 (4.2–11.1) 833 12.8 (9.9–16.4)
Richest quintile 1051 (19.9) 1051 13.7 (10.6–17.8) 1051 6.3 (4.0–9.6) 1049 91.2 (83.1–95.7) 1034 13.3 (9.2–18.9) 1042 36.8 (30.5–43.6) 1036 23.9 (19.8–28.5) 967 8.7 (6.4–11.8) 1003 13.5 (10.5–17.3)
 P-Value 0.002 0.177 <0.001 0.001 <0.001 <0.001 0.008 0.032
Occupation*
 Employed 1707 (32.9) 1707 25.3 (21.7–29.3) 1707 10.9 (8.2–14.5) 1700 96.5 (93.8–98.0) 1685 8.7 (6.0–12.6) 1689 27.5 (23.6–31.9) 1687 31.6 (28.1–35.3) 1566 6.5 (5.1–8.3) 1625 11.2 (8.5–14.4)
 Student 402 (14.3) 402 3.6 (1.7–7.3) 402 1.66 (0.6–4.5) 400 95.1 (88.2–98.1) 396 6.3 (3.6–10.8) 393 12.3 (7.6–19.3) 393 6.6 (3.8–11.2) 374 1.7 (.73–4.1) 386 3.8 (2.1–6.9)
 Homemaker 3142 (45.5) 3142 15.2 (12.9–17.7) 3142 5.3 (3.69–7.45) 3131 97.4 (95.7–98.4) 3080 6.4 (4.6–8.8) 3076 25.8 (22.6–29.3) 3090 24.9 (21.9–28.0) 2927 6.3 (4.5–8.8) 3009 13.3 (11.5–15.4)
 Unemployed 273 (6.1) 273 20.9 (12.5–32.8) 273 8.4 (4.6–15.0) 267 95.4 (89.7–98.0) 263 13.3 (7.1–23.4) 272 24.0 (14.2–37.6) 269 23.4 (17.3–30.9) 256 5.4 (2.49–1.4) 261 10.6 (6.41–17.1)
 Others 63 (0.9) 63 11.4 (5.1–23.5) 63 2.3 (0.80–6.5) 63 98.7 (92.6–99.8) 63 12.22 (4.7–28.2) 63 27.7 (14.3–46.7) 61 34.3 (20.7–51.1) 62 16.9 (8.0–32.1) 63 16.3 (7.5–31.9)
 P-Value <0.001 <0.001 0.048 <0.001 0.002 <0.001 0.002 <0.001
Marital status*
 Unmarried 538 (19.5) 538 10.0 (6.6–15.0) 538 4.0 (1.8–8.7) 534 96.5 (92.4–98.4) 531 8.7 (5.4–13.5) 531 13.79 (9.5–19.6) 530 12.7 (9.0–17.7) 496 1.71 (0.7–4.1) 509 4.43 (2.6–7.4)
 Currently married 4752 (77.8) 4752 18.4 (16.4–20.5) 4752 7.4 (6.1–9.1) 4735 96.6 (94.4–97.9) 4666 7.2 (5.3–9.6) 4668 27.4 (24.5–30.5) 4675 26.9 (24.7–29.2) 4412 6.7 (5.3–8.5) 4552 12.3 (10.6–14.2)
 Separated/Divorced/Widowed 302 (2.7) 302 32.3 (24.8–40.9) 302 8.20 (3.7–17.1) 297 98.0 (93.7–99.4) 295 13.2 (7.9–21.3) 299 15.7 (10.7–22.3) 300 40.6 (33.2–48.5) 282 6.6 (3.8–11.5) 288 22.9 (17.0–30.1)
 P-Value <0.001 0.155 0.793 <0.001 <0.001 <0.001 <0.001 <0.001
Total 5593 17.1 (15.2–19.2) 5593 6.8 (5.5–8.4) 5567 96.6 (94.3–98.0) 5493 7.7 (5.7–10.1) 5499 24.4 (21.7–27.4) 5506 24.5 (22.4–26.7) 5191 5.8 (4.5–7.3) 5350 11.1 (9.7–12.7)

* 1 case from education, 6 cases from occupation and 1 case from marital status was excluded.

Smoking

Current smoking behavior was observed in 17% of the participants (95% CI: 15.2–19.2), with the prevalence being highest amongst men (28%; 95% CI: 24.6–31.6) and in the 45 to 69 years age group (26%; 95% CI: 22.9–28.9). The prevalence of current smoking was also higher among uneducated/less educated participants (22%; 95% CI: 18.9–24.6). There were a higher proportion of smokers found in Sudurpaschim Province (26%; 95% CI: 21.8–31.5) and Karnali Province (22%; 95% CI: 17.9–25.7) than in other provinces. Similarly, the proportion of smokers was higher in the mountain belt (27%; 95% CI: 22.4–33.0) and among the lowest quintile of affluence (poorest) (23%; 95% CI: 19.8–27.1). Conversely, the prevalence of smoking was found to be higher among employed (25%; 95% CI: 21.7–29.3) and married participants (32.3%; 95% CI: 24.8–40.9) (Table 2).

Alcohol use

Harmful use of alcohol was observed in around 7% (95% CI: 5.5–8.4) of participants with a higher prevalence amongst males (12%; 95% CI: 10.00–15.39). Participants from the mountain belt (13%; 95% CI: 7.7–20.2) had higher prevalence compared to Terai residents (5%; 95% CI: 3.7–7.5). A higher prevalence was also observed among participants who had primary education (9%; 95% CI: 6.1–12.2) and among employed people (11%; 95% CI: 8.2–14.5) (Table 2).

Insufficient fruit/Vegetable intake

An insufficient intake of fruits and vegetables was found among almost all participants (97%), although a slightly higher prevalence was found among those with none/less than primary education (98%; 95% CI: 95.9–99.1). Those from rural municipalities (98%; 95% CI: 97.2–99.0), the mountain belt (99%; 95% CI: 98.4–99.7) and those with the poorest economic status (99%; 95% CI: 98.8–99.8) had the highest prevalence (Table 2).

Physical inactivity

Around 8% (95% CI: 5.7–10.1) of the participants were physically inactive with a higher prevalence among those 45 to 69 years of age (9%; 95% CI: 6.9–12.3). Participants with a primary education had a higher prevalence of physical inactivity (10%; 95% CI: 6.4–14.0) compared to participants with a secondary or higher level of education. Participants in the richest quintile (13%; 95% CI: 9.2–18.9), those who were unemployed (13%; 95% CI: 7.1–23.4) and those that were married (13%; 95% CI: 7.9–21.3) had a higher proportion of physical inactivity as compared to their counterparts (Table 2).

Overweight

The prevalence of overweight was 24% (95% CI: 21.7–27.4) across all participants, a higher prevalence was found among participants in the 30 to 44 years age group (33%; 95% I: 29.2–36.5). Metropolitan city residents (34%; 95% CI: 26.7–41.1) and hill residents (32%; 95% CI: 26.1–37.5) had a higher prevalence of overweight compared to residents in rural municipality (18%; 95% CI: 14.9–22.5) and Terai belt (21%; 95% CI: 17.7–23.8]. The highest prevalence of overweight was found in Bagmati Province (43%; 95% CI: 35.4–50.5) followed by Gandaki Province (35%; 95% CI: 28.7–42.7). Similarly, the prevalence was highest amongst those in the richest quintile (37%; 95% CI: 30.5–43.6) and currently married participants (27%; 95% CI: 24.5, 30.5).

Raised BP

Around 24% (95% CI: 22.4–26.7) of the participants had raised BP with a higher prevalence among men (30%; 95% CI: 26.6–33.1) and participants in the 45 to 69 years age group (43%; 95% CI: 39.5–46.3). Raised BP was most prevalent in Gandaki (30%; 95% CI: 26.6, 33.5) and Lumbini Provinces (28%; 95% CI: 24.1–32.8) as compared to other provinces. Similarly, a higher prevalence was observed among participants having none/less than primary level of education (32%; 95% CI: 28.7–35.1) and those that were married (41%; 95% CI: 33.2–48.5).

Raised blood sugar

The prevalence of raised blood sugar was 5.8% for the total sample (95% CI: 4.5–7.3). Around 10% of participants aged 4 to 69 years (10.2%; 95% CI: 8.1–12.7) and 9% of participants in the richest quintile (95% CI: 6.4–11.8) had raised blood sugar. The highest regional prevalence was observed among participants of province 2 (11%; 95% CI: 7.4–16.9) with the lowest in Karnali Province (1%; 95% CI: 0.36–1.4). Likewise, metropolitan (10%; 95% CI: 5.3–19.6) and Terai residents (8%; 95% CI: 6.2–10.7) had higher prevalence of raised blood sugar compared to rural municipality (4%; 95% CI: 2.7–6.1) and hilly areas (3%; 95% CI: 2.0–4.5) (Table 2).

Raised cholesterol level

Raised cholesterol level was found among 11% of the participants (95% CI: 9.7–12.7), with this highest among participants 45 to 69 years of age (19%; 95% CI: 16.4–21.3) and among females (14%; 95% CI: 12.11–16.17). Compared to other levels of education, participants with ‘none/less than primary education’ (15%; 95% CI: 12.7–17.3) had the highest prevalence of raised total cholesterol. Whilst a higher prevalence was found in Province 1 (15%; 95% CI: (10.81–19.84), Terai residents (12%; 95% CI: 10.5, 14.8), richest quintile (14%; 95% CI: 10.5–17.3) and married participants (23%; 95% CI: 17.0–30.1) compared to their counterparts (Table 2).

Prevalence ratios demonstrated a significantly higher prevalence of smoking among males (APR: 4.49, 95% CI: 3.70–5.46) compared to females, once adjusting for other covariates. Smoking was significantly lower among participants having more than a secondary level education (APR: 0.56, 95% CI: 0.39–0.81) compared to participants with no, or less than primary level of education. Similarly, a lower prevalence was found within Province 1 (APR: 0.42, 95% CI: 0.29–0.60) residents and participants of hilly region (APR: 0.69, 95% CI: 0.54–0.90) as compared to reference categories of Sudurpaschim Province and those from the mountain region, respectively (Table 3).

Table 3. Adjusted prevalence ratio of sociodemographic characteristics with NCD risk factors.

Smoking (APR with 95% CI) Harmful use of alcohol (APR with 95% CI) Insufficient fruit/vegetable intake (APR with 95% CI) Physical inactivity (APR with 95% CI) Overweight (APR with 95% CI) Raised BP (APR with 95% CI) Raised Sugar (APR with 95% CI) Raised blood cholesterol (APR with 95% CI)
Age
 15–29 Ref Ref Ref Ref Ref Ref Ref Ref
 30–44 1.1 (0.83–1.44) 0.94 (0.61–1.46) 0.99 (0.97–1.02) 0.72 (0.46–1.12) 1.46 (1.18–1.80) *** 1.61 (1.30–1.98)*** 2.33 (1.30 -.19) ** 1.69 (1.15–2.48) **
 45–69 1.39 (1.03–1.87)* 0.94 (0.59–1.50) 0.99 (0.96–1.01) 1.14 (0.76–1.69) 1.26 (1.04–1.53) * 2.52 (2.06 -.09)*** 3.88 (2.05 -.36)*** 2.58 (1.75 -.78)***
Sex
 Female Ref Ref Ref Ref Ref Ref Ref Ref
 Male 4.49 (3.70–46)*** 9.09 (5.38 -.35)*** 0.98 (0.97–1.00) 0.91 (0.60–1.39) 0.95 (0.81–1.12) 1.5 (1.27 -.77)*** 1.04 (0.67–1.61) 0.51 (0.38 -.67)***
Education level
 None/less than primary Ref Ref Ref Ref Ref Ref Ref Ref
 Primary 0.8 (0.63–1.02) 1.15 (0.81–1.64) 0.98 (0.96–1.00) 1.39 (0.90–2.14) 0.99 (0.82–1.19) 0.95 (0.82–1.10) 1.68 (1.16–2.43)** 0.89 (0.65–1.20)
 Secondary 0.8 (0.63–1.02) 0.85 (0.54–1.34) 1 (0.97–1.02) 0.91 (0.57–1.46) 0.93 (0.75–1.15) 0.79 (0.63–1.00)* 1.65 (1.03–2.65)* 0.66 (0.47–0.91)*
 more than secondary 0.56 (0.39–0.81)** 0.5 (0.28–0.90)* 0.94 (0.88–1.00)* 0.83 (0.45–1.54) 0.91 (0.71–1.19) 0.66 (0.48–0.92)* 1.29 (0.64–2.62) 1.06 (0.67–1.69)
Residence
 Rural municipality Ref Ref Ref Ref Ref Ref Ref Ref
 (Sub)Metropolitan 0.79 (0.53–1.17) 0.85 (0.40–1.84) 0.93 (0.81–1.07) 0.54 (0.21–1.37) 1.22 (0.93–1.59) 1.08 (0.85–1.36) 1.46 (0.72–2.97) 0.73 (0.50–1.07)
 Municipality 1.02 (0.82–1.26) 0.88 (0.56–1.39) 1.01 (0.99–1.03) 0.78 (0.40–1.52) 0.78 (0.63–0.96) * 0.91 (0.77–1.09) 0.79 (0.48–1.31) 1 (0.75–1.32)
Province
 Sudurpaschim Ref Ref Ref Ref Ref Ref Ref Ref
 Province 1 0.42 (0.29–60)*** 1.01 (0.55–1.87) 0.98 (0.94–1.03) 0.3 (0.10–0.83)* 1.98 (1.38–2.84)*** 1.23 (0.92–1.63) 0.68 (0.25–1.84) 1.16 (0.74–1.82)
 Province 2 0.53 (0.38 -.75)*** 0.75 (0.39–1.44) 1 (0.96–1.04) 0.6 (0.21–1.76) 1.51 (1.03–2.21)* 0.82 (0.59–1.15) 1.45 (0.50–4.25) 0.76 (0.47–1.21)
 Bagmati Province 0.67 (0.51–0.89)** 1.1 (0.56–2.16) 1 (0.97–1.03) 0.84 (0.38–1.84) 2.52 (1.82–3.49)*** 0.95 (0.69–1.31) 0.79 (0.29–2.14) 0.63 (0.39–1.02)
 Gandaki Province 0.79 (0.60–1.04) 1.37 (0.70–2.68) 1 (0.98–1.03) 0.89 (0.33–2.41) 2.36 (1.71–3.26)*** 1.3 (1.00–1.68)* 0.67 (0.22–2.01) 1.02 (0.66–1.60)
 Lumbini Province 0.72 (0.52–0.98)* 1.57 (0.87–2.86) 0.97 (0.92–1.03) 0.56 (0.22–1.42) 1.59 (1.13–2.23)** 1.34 (1.03–1.74)* 1 (0.34–2.94) 0.9 (0.58–1.40)
 Karnali Province 0.9 (0.67–1.21) 1.31 (0.74–2.32) 0.97 (0.94–1.00*) 0.51 (0.17–1.49) 0.94 (0.61–1.43) 1.02 (0.76–1.37) 0.26 (0.08 -.81)* 0.53 (0.31 -.94)*
Ecological
 Mountain Ref Ref Ref Ref Ref Ref Ref Ref
 Hill 0.69 (0.54 -.90)** 0.53 (0.30–0.95)* 1.01 (0.98–1.03) 2.32 (0.88–6.09) 1.03 (0.80–1.32) 0.94 (0.69–1.27) 2.46 (0.81–7.49) 1.53 (0.94–2.47)
 Terai 0.7 (0.53–0.92)** 0.38 (0.20–0.70)** 0.98 (0.96–1.01) 2.76 (0.95–8.05) 0.7 (0.53–0.94)* 0.77 (0.57–1.04) 4.25 (1.35–13.36)* 1.61 (0.94–2.77)
Wealth Quintile
 Poorest quintile Ref Ref Ref Ref Ref Ref Ref Ref
 Second quintile 0.88 (0.71–1.08) 0.84 (0.53–1.34) 1.02 (1.00–1.03)* 1.27 (0.72–2.27) 1.26 (1.02–1.56)* 0.88 (0.74–1.06) 1.2 (0.65–2.19) 1.49 (1.08–2.07)*
 Third quintile 0.84 (0.66–1.08) 1.16 (0.71–1.90) 1.01 (0.99–1.03) 1.31 (0.65–2.66) 1.42 (1.07–1.88)* 1.06 (0.83–1.37) 1.4 (0.70–2.80) 1.54 (1.01–2.37)*
 Fourth quintile 0.88 (0.66–1.17) 0.7 (0.37–1.30) 1 (0.97–1.02) 1.5 (0.82–2.74) 1.51 (1.18–1.94)** 1.06 (0.86–1.30) 1.2 (0.58–2.47) 1.86 (1.25–2.75)**
 Richest quintile 0.87 (0.65–1.17) 1.08 (0.59–1.97) 0.96 (0.92–1.00) 2.74 (1.42–5.27)** 1.94 (1.52–2.50)*** 1.07 (0.85–1.35) 1.5 (0.72–3.16) 2.09 (1.38–3.19)***
Occupation
 Employed Ref Ref Ref Ref Ref Ref Ref Ref
 Student 0.2 (0.09 -.45)*** 0.24 (0.08–0.68)** 0.97 (0.92–1.02) 0.59 (0.26–1.33) 0.74 (0.43–1.25) 0.3 (0.17 -.56)*** 1.06 (0.25–4.55) 0.57 (0.28–1.18)
 Homemaker 1.08 (0.88–1.32) 1.17 (0.78–1.75) 1 (0.99–1.02) 0.8 (0.59–1.10) 1.03 (0.87–1.21) 0.87 (0.72–1.06) 1.09 (0.66–1.79) 0.84 (0.58–1.20)
 Unemployed 0.9 (0.57–1.42) 0.91 (0.49–1.70) 0.99 (0.94–1.03) 1.65 (0.88–3.08) 0.83 (0.59–1.16) 0.71 (0.51–0.98)* 1.19 (0.55–2.56) 0.85 (0.54–1.34)
 Others 0.39 (0.19–0.81)* 0.25 (0.09–0.70)** 1.03 (0.99–1.07) 1.14 (0.44–2.94) 0.85 (0.51–1.43) 0.86 (0.58–1.28) 2.25 (1.16 -.37)* 1.2 (0.60–2.41)
Marital status
 Unmarried Ref Ref Ref Ref Ref Ref Ref Ref
 Currently married 0.85 (0.58–1.25) 1.13 (0.53–2.40) 0.98 (0.94–1.02) 0.72 (0.41–1.25) 1.28 (0.88–1.85) 0.73 (0.49–1.09) 1.92 (0.54–6.78) 1.13 (0.59–2.15)
 Separated/Divorced/Widowed 1.68 (0.98–2.90) 1.77 (0.66–4.70) 0.99 (0.94–1.03) 1.26 (0.56–2.85) 0.75 (0.45–1.24) 0.89 (0.57–1.40) 1.55 (0.37–6.41) 1.4 (0.68–2.91)

* p<0.05;

** p<0.01;

*** p<0.001.

Likewise, alcohol intake was significantly higher among men (APR: 9.09, 95% CI: 5.38–15.35) and lower among participants having more than a secondary level of education (APR: 0.5, 95% CI: 0.28–0.9) and those residing in the Terai region (APR: 0.38, 95% CI: 0.20–0.70) (Table 3).

Insufficient intake of fruits and vegetables was significantly less prevalent among participants having more than a secondary level education (APR: 0.94, 95% CI: 0.88–1.00) and among participants of Karnali Province (APR: 0.97, 95% CI: 0.94–1.00) than Sudurpaschim residents. A higher prevalence was observed among participants in the second poorest quintile (APR: 1.02, 95% CI: 1.0–1.03) compared to those in the poorest quintile. Similarly, low physical activity was significantly lower among participants of Province 1 (APR: 0.3, 95% CI: 0.10–0.83) and higher among richest participants (APR: 2.74, 95% CI: 1.42–5.27) (Table 3).

Being overweight was significantly higher among participants aged 30 to 44 years (APR: 1.46, 95% CI: 1.18–1.80) compared to those aged 15 to 29 years. A higher prevalence was observed among participants of Bagmati Province (APR: 2.5, 95% CI: 1.82–3.49) and Gandaki Province (APR: 2.36, 95% CI: 1.71–3.26) and lower among Terai residents (APR: 0.70, 95% CI: 0.53–0.94) than participants of mountain area. Similarly, raised BP (APR: 2.52, 95% CI: 2.06–3.09) and raised blood sugar (APR: 3.9, 95% CI: 2.05–7.36) was significantly higher among participants within the 45 to 49 years age group. A higher prevalence of raised BP was found amongst males (APR: 1.5, 95% CI: 1.27–1.77) and participants of Gandaki Province (APR: 1.3, 95% CI: 1.0–1.7) and Lumbini Province (APR: 1.3, 95% CI: 1.03–1.74). Whilst raised blood sugar was significantly higher among Terai residents (APR: 4.3, 95% CI: 1.35–13.36) (Table 3).

A significantly higher prevalence of raised blood cholesterol was observed among participants within the 45 to 69 years age group (APR: 2.58, 95% CI: 1.75–3.78). This was lower among men (APR: 0.51, 95% CI: 0.38–0.67) and participants from the Karnali Province (APR: 0.53, 95% CI: 0.31–0.94) compared to females and Sudurpaschim residents, respectively. A higher prevalence was found among those in the most affluent quintile (APR: 2.09, 95% CI: 1.38–3.19) (Table 3).

Age, sex, education, residence, province and wealth were significantly associated with clustering of risk factors. Males (ARR: 1.2, 95% CI: 1.1–1.3) and those in the fourth wealth quintile (ARR: 1.17, 95% CI: 1.07–1.28) had a significantly higher number of risk factors compared to females and the poorest participants. Similarly, participants who had more than a secondary level education (ARR: 0.86, 95% CI: 0.78–0.95) and those who resided in Karnali Province (ARR: 0.9, 95% CI: 0.8–0.9) had fewer risk factors (Table 4).

Table 4. Clustering of NCD risk factors and its multivariable analysis.

Age Mean number of existing risk factors (95% CI) Adjusted relative risk ARR (95% CI)
 15–29 years 1.81 (1.75–1.86) Ref
 30–44 years 2.00 (1.96–2.05) 1.14 (1.06–1.22)***
 45–69 years 1.95 (1.91–1.98) 1.31 (1.23–1.39)***
Sex
 Female 1.95 (1.91–1.98) Ref
 Male 2.41 (2.36–2.46) 1.21 (1.14–1.29)***
Education level
 None/less than primary 2.15 (2.11–2.20) Ref
 Primary 2.11 (2.04–2.18) 0.99 (0.94–1.04)
 Secondary 2.09 (2.02–2.16) 0.94 (0.92–1.01)
 more than secondary level 1.95 (1.86–2.04) 0.86 (0.78–0.95)**
Residence
 Rural municipality 1.99 (1.94–2.03) Ref
 Sub/metropolitan 2.30 (2.21–2.39) 0.94 (0.81–1.09)
 Municipality 2.16 (2.11–2.21) 0.95 (0.89–1.01)
Province
 Sudurpaschim 2.09 (2.01–2.8) Ref
 Province 1 2.15 (1.91–2.07) 0.91 (0.82–1.02)
 Province 2 1.99 (1.91–2.08) 0.89 (0.78–1.01)
 Bagmati Province 2.29 (2.20–2.37) 0.98 (0.90–1.07)
 Gandaki Province 2.29 (2.21–2.38) 1.07 (0.97–1.18)
 Lumbini Province 2.07 (1.99–2.15) 0.95 (0.86–1.04)
 Karnali Province 1.90 (1.83–1.97) 0.88 (0.80–0.96)**
Ecological
 Mountain 2.08 (1.99–2.15) Ref
 Hill 2.13 (2.09–2.17) 0.99 (0.92–1.06)
 Terai 2.10 (2.05–2.15) 0.93 (0.85–1.01)
Wealth Quintile
 Poorest 1.95 (1.91–2.00) Ref
 Second quintile 2.05 (1.99–2.12) 1.02 (0.96–1.09)
 Third quintile 2.11 (2.04–2.18) 1.06 (0.98–1.14)
 Fourth quintile 2.20 (2.12–2.29) 1.1 (1.02–1.18)*
 Richest 2.33 (2.25–2.41) 1.17 (1.07–1.28***)
Occupation
 Employed 2.28 (2.21–2.33) Ref
 Student 2.22 (2.10–2.35) 0.75 (0.68–0.84)***
 Homemaker 1.99 (1.96–2.04) 0.98 (0.91–1.04)
 Unemployed 2.13 (1.99–2.27) 0.95 (0.86–1.05)
 Others 2.5 (2.21–2.79) 0.92 (0.77–1.09)
Marital status
 Unmarried 2.13 (2.03–2.23) Ref
 Currently married 2.09 (2.07–2.13) 0.93 (0.84–1.03)
 Separated/Divorced/Widowed 2.29 (2.17–2.42) 0.99 (0.88–1.12)
Total 2.04 (2.02–2.08) -

* p<0.05;

** p<0.01;

*** p<0.001.

Discussion

Smoking

The prevalence of current smoking (17.1%) is relatively stable from the previous round of the STEPS survey (19%) and this findings is similar to that of Bangladesh’s GATS 2017 survey [10,18,19]. However, compared to India’s smoking prevalence (10.7%), the prevalence in Nepal is higher [20]. This relatively stable smoking rate from 2013 onward, could be the result of implementation and monitoring of the comprehensive tobacco control law that was introduced in 2011 [21]. Furthermore, an increase in literacy rate of the population, increased awareness about the health consequences of smoking, effective implementation and monitoring of tobacco control law provisions such as pictorial health warning, tobacco industry litigation, may have played a crucial role in keeping the smoking prevalence stable, or to curb the increasing trend.

Within our study we found a significantly higher smoking prevalence amongst males (25%) than females (7%), which aligns with the patterns of smoking observed in WHO SEARO member countries [22]. Studies have indicated that this could due to a range of factors including tobacco industry market strategies that portray smoking as more masculine and community tolerance of male smoking over female smoking [23,24]. Our findings also found an increasing prevalence in smoking with increasing age, a similar finding to that of the previous 2013 STEPS survey and other global data [10,22]. A possible explanation for increasing smoking prevalence with age may be due to increased levels of dependence with age, or lack of effective cessation programs which may lead to the accumulation of smokers with increasing age [25].

We found that participants residing in Province 1, Province 2, Bagmati Province, and Lumbini Province were less likely to smoke than those in Sudurpaschim Province, with this finding aligning with another national level survey [26]. This may be due to the comparatively high levels of literacy in those provinces compared to Karnali, Gandaki and Sudurpaschim. The role of education in smoking practices is further elucidated through the relationship of educational level and prevalence of smoking found within the current study. Participants with none/less than primary of education were more likely to smoke as compared to those with a higher education level (primary, secondary or higher secondary above) in our study. This finding was consistent with previous data from demographic and health surveys of nine countries, including Nepal [27]. Similarly, people residing in mountainous region were more likely to smoke than in any other regions of country and these findings, which aligns with previous survey findings [10]. The differences in prevalence of smoking based on province and ecological belt could indicate the need of contextualized targeted interventions for smoking control in Nepal. The current federal structure of the country, where planning process is devolved to provincial and local government to a large extent, could be an opportunity for implementation of locally contextualized interventions for control of smoking.

Alcohol intake

Prevalence of harmful alcohol intake has increased to 6.7% in the current study from the 2.2% reported in 2013 STEPS survey [10]. Alcohol intake and harmful alcohol intake was higher among males than females, which was also noted in the previous round of STEPS survey [10]. Regarding types of alcohol used, a significant proportion of females consumed home-brewed alcohol whilst males consumed alcohol from other sources i.e. industrially produced alcohol [28]. This difference in consumption of alcohol based on the sex of participants could be linked to social and cultural norms which define drinking alcohol by males as normal behavior, while in females, drinking alcohol is still considered as an anti-social act [24]. However, compared to previous rounds of the STEPS survey, a higher proportion of females are consuming alcohol, which could be a result of changing lifestyles and societal perceptions in alcohol consumption among females. In addition, findings revealed that there is a higher prevalence of the harmful use of alcohol among employed participants (10.96%) compared to other groups.

Our study revealed participants with higher education level (secondary, more than secondary) and Terai residents were less likely to consume harmful levels of alcohol. This finding is in line with a previous study conducted among 9,000 females, in which those of the mountain region and those having no education/formal education were more likely to drink alcohol [29]. This may be due to socio-cultural differences among different ecological belts of Nepal. In the majority of ethnic groups in the mountainous region there is a cultural acceptance of drinking alcohol, whereas in the Terai region drinking alcohol is considered an unreligious act [29].

Insufficient fruit and vegetable intake

The results of fruit and vegetables intake suggests that there is marginal improvement intake in comparison to previous round of STEPS survey [10]. Multivariable analysis found no significant association with achieving recommended levels of fruit and vegetable intake. However, this study has found participants with higher education level (more than secondary level of education) participants were more likely to consume adequate levels of fruits and vegetables when compared with less educated groups, similar to findings of a previous study [30]. In the context of Nepal, factors such as limited accessibility, availability and affordability of fruits and vegetables and social perceptions on the use of fruits and vegetables could have a role in the high prevalence of an insufficient intake of fruit and vegetables in the population. Individuals may also lack adequate information on the need to consume sufficient fruit and vegetables and the health consequences of insufficient intake. This issue could be further explored through qualitative research, which could provide more in-depth insights into the insufficient fruit and vegetable intake among the Nepalese population. Findings from such studies could also be useful in designing contextualized interventions intended to promote adequate intake of fruits and vegetables.

Physical inactivity

The current study reports a low prevalence of physical inactivity (7.4%) a finding that is in line with those of previous national and international surveys [31,32]. However, in comparison with the 2013 STEPS survey, physical inactivity has doubled [10]. Those in the richest quintile were found to have the highest prevalence of physical inactivity. This may be due to the adoption of a sedentary lifestyle associated with occupations among this group of people [33] along with better access to means of transportation, thereby reducing walking hours in a day.

Overweight

Almost of one quarter (24%) of people were overweight, a figure slightly higher than that reported in STEPS survey in 2013 (21%) [10]. This increment may be understood in relation to changes in physical inactivity level, which was about 3% in 2013 and has increased to 7.4%. Apart from sedentary lifestyle, urbanization accompanied with increased consumption of processed/junk foods may be a factor in the increased prevalence of overweight among Nepalese adults.

There is increasing prevalence of overweight with increasing age group, a finding in line with previous publications from the 2013 STEPS survey. Ageing may also be associated with limited mobility and limited engagement in labor intensive works which could result in overweight among participants of relatively higher age group. Those in the richest quintile have higher a prevalence of overweight compared to those in the poorest quintile which is similar to findings from a systematic review on the South Asian context [34]. A higher prevalence of overweight among females could be attributed to social and cultural factors which influence both dietary intake and physical activity [35]. However, this finding was not supported by multivariable analysis in the present study.

Raised BP

Within the present study one quarter (24.44%) of Nepalese had raised BP, which is consistent with previous round of STEPS survey, but slightly higher than reported in the 2016 NDHS survey (19.9%) [36]. This difference may be due to methodological variation i.e. differences in sampling design. Findings from both surveys indicate an increasing burden of raised BP in Nepal and demand sufficient efforts for prevention and control of this problem.

We found an increasing prevalence of raised BP with increasing age, which is similar to the previous STEPS survey and the Nepal Demographic and Health Survey (NDHS) 2016. We also found a sex difference in the prevalence of raised BP, which is consistent with other surveys’ findings [10,36]. Sex differences in raised BP may be due to both biological and behavioral factors [37]. Such as sex hormones, genetic makeup, and other biological sex features that are assumed to have a protective effect against raised BP in females [37,38]. An association was also found between education level raised BP, with a lower prevalence found amongst more educated participants. This result is consistent with the findings of previous STEPS and NDHS. Educated people are likely to have access to information about the raised BP and its consequences, which might ultimately help them to adopt preventative measures [39].

Raised blood sugar

WHO global estimates has shown that 8% of South Asian people have increased level of blood sugar level which is close to the estimates in this study (6%) [40]. The prevalence of raised blood sugar has doubled from 3% to 6% [10] which should also be interpreted considering the difference in techniques to measure blood sugar level. In previous rounds of the survey the wet method was adopted to measure blood sugar level, however, for this round of survey the dry method was used. The prevalence of raised blood sugar level increased with increasing age group, which is comparable to other national surveys. Increasing age is associated with combined effect of increasing adiposity, decreasing physical activity, medications, coexisting illness, and insulin secretary defects that effect blood sugar level [41]. Similarly, this study has reported differences in prevalence of raised blood sugar between provinces, place of residence (sub/metropolitan city, municipality or rural municipality), and ecological region (mountain, hill, Terai). Furthermore, blood sugar difference among ecological region is further validated by multivariable analysis, that has shown that residents from Terai are more likely to have raised blood sugar compared to those from the mountainous region. These differences may be attributed to variations in physical activity levels, dietary habits and urbanization level, with this findings similar to that from previous studies [42,43]. Some of the studies have put forward a biological explanation that increased content of the glucose transporter GLUT4 in the plasma membrane of skeletal muscle cells incubated under anoxia conditions (35,38), and in skeletal muscle cells exposed to prolonged hypoxia leads to the better glucose tolerance [44,45].

Raised cholesterol level

The prevalence of raised total cholesterol as found in the current study is quite low (11%) compared with previous rounds of the survey i.e 22.7%; this may be due to differences in measurement techniques. As with raised BP, we found an increase in prevalence of raised cholesterol level with increasing age group, with this finding similar to that of the 2013 survey. Reduction in the production of growth hormone with increasing age may be a causal factor contributing to the age-dependent rise in blood cholesterol [46]. Similarly, our finding that females have a higher prevalence of raised blood cholesterol level may be linked to increasing age and fluctuations in female sex hormone i.e. estrogen. Various studies have shown that estrogen helps to maintain levels of high-density lipoproteins (HDL) in adult females. However, at menopause many females experience a change in their cholesterol levels, with total cholesterol and low-density lipoproteins (LDL) levels rising and HDL falling [47]. In addition, a greater prevalence of raised total cholesterol in participants in the richest quintile, as found in the present study, may be due to the adoption of a more sedentary lifestyle, a lack of physical activity and stress related factors.

Clustering of risk factors

The current study reveals that Nepalese adults on average have the presence of two risk NCD risk factors. With the average number of NCD risk factors greater in males, the richest wealth quintile, and amongst older participants. Suggesting that increasing age is associated with increasing clustering of risk factors, a finding supported by research from other countries [48,49]. As Nepal has been experiencing a rapid increase in life expectancy and median age of population, it is likely that such problems could escalate in the coming years [1]. Greater clustering of risk factors in males compared to females may be due to risk-oriented behavior and sedentary lifestyle in male such as tobacco smoking, alcohol and physical inactivity.

Our finding that the prevalence of clustering of NCDs risk factors is higher among the richest, was also found in a previous study in Bhutan [50]. Similar to individual risk factors such as overweight/obesity and hypertension, the clustering of NCDs risk factors in the richest group can be linked with the adoption of a sedentary lifestyle.

Policy implications and way forward

The policies and programs targeted to reduce NCD risk factors within the Nepal population should be designed as per the socio-demographic gradient of the country. Finally, the new multi-sectoral action plan for prevention and control of NCDs in Nepal should consider the federal context and trends of risk factors for effective prevention and control in Nepal.

Conclusion

The findings for this survey demonstrate that a large proportion of the Nepalese population is living with two or more NCD risk factors. In comparison to the 2013 STEPS survey, prevalence of most of the risk factors has increased, indicating a need for effective programs to counter this. One of the primary strategies to reduce the burden of NCD risk factors would be to prevent, or reduce, the burden of modifiable risk factors, which could also prove more cost effective than providing curative services to people with NCDs. However, interventions on modifiable risk factors demand collaborative efforts from multiple sectors so as to create an enabling environment for behavior change. The current federal structure in Nepal, in which the municipality takes responsibility for different sectors like education, infrastructure development, environment etc. together with health, can provide an opportunity for integrated interventions from different sectors, which could prove effective in reducing the burden of NCD risk factors in the country.

Supporting information

S1 File

(PDF)

S1 Dataset

(XLSX)

Acknowledgments

We would like to acknowledge the effort of all the individuals involved in this survey, express our deep sense of appreciation to the steering committee and technical working group (TWG) members. We are grateful to World Health Organization (WHO) for technical support to conduct this survey. In particular, we would like to express my sincere thanks to Dr. Manju Rani and Naveen Agarwal (HO/SEARO); Dr. Patricia Rarau and Dr. Stefan Savin (WHO HQ); Dr. Md. Khurshid Alam Hyder and Dr. Lonim Prasai Dixit (WHO Nepal); Ms. Yvonne Y. Xu, Ms. Preetika D. Banerjee and Ms. Surabhi Chaturvedi (WHO SEARO) for their valuable and remarkable contribution in the survey. We would also like to thank all NHRC staff who helped during conduction of this study.

Data Availability

All relevant data are within the manuscript and its Supporting information files.

Funding Statement

This survey was funded by the Government of Nepal and the World Health Organization.

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Decision Letter 0

Brecht Devleesschauwer

6 Apr 2021

PONE-D-21-06886

Prevalence of Noncommunicable Diseases Risk Factors and their Determinants: Results from STEPS survey 2019, Nepal

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Reviewer #1: Introduction

The rationale for reporting this study could be reframed better.

For ex. There is no link between the multi sectoral plan and current STEPS survey; linking these two might give better clarity to understand the need for present STEPS survey

Methods

Sampling methods adapted in previous STEPS surveys are different from the current survey. The change in context should get mentioned for the readers to compare and interpret these two surveys

As per methods details provided in the reference, the sample size was estimated to cover each province with equal number of participants (around 900 each). If it is so, why there are 20% participants from some province and 8% from some province. Please clarify.

As province and ecological belts were considered as independent variables across all NCD risk factors give a brief write up regarding the characteristics of these provinces and ecological belts which are relevant to understand the burden of NCD risk factors.

Though sample size and sampling methods were published elsewhere it is better to give a brief description on the sample size and selection of PSUs.

Under Ethical concern, for minors it require assent from the adolescents and consent from parents. Hence it needs to be clarified accordingly. Avoid the tern assent consent.

Results:

In this survey 15-29 years age group is predominant which is entirely different from the previous STEPS survey or Nepal population distribution based on age.

Please clarify how this over representation from this age group has happened.

Two decimals in results make the tables too crowded. It does not become reader friendly.

There is no display of gender disaggregated results within the province. But the discussion section presented in those lines.

Tobacco use and smokers are used inter changeably. Please use the term consistently

For fruits & vegetables intake, when everything is more than 97% sub group disaggregation does not help much (line no 221 to 223) could be avoided.

Table 2: As majority of the times the number in the sub group is going to remain same across all NCD risk factors it seems there is redundancy the way the n is represented. Instead of total number in the sub group it is preferred to give number with the particular risk factor present under the column n.

Discussion

Comparison of smoking with previous STEPS survey is incomplete.

The speculation for higher prevalence of smoking in Nepal is not justified well. (line No 268-274). Actually the prevalence in Nepal is more that should be explained by extent of lacunae in implementation of current tobacco legislative measures.

Adjusted prevalence ratio or adjusted relative risk has been calculated but it has been interpreted as if they were odds ratios. (line No 322-324, 334-335, 337,350, 353)

Introduction

The rationale for reporting this study could be reframed better.

For ex. There is no link between the multi sectoral plan and current STEPS survey; linking these two might give better clarity to understand the need for present STEPS survey

Methods

Sampling methods adapted in previous STEPS surveys are different from the current survey. The change in context should get mentioned for the readers to compare and interpret these two surveys

As per methods details provided in the reference, the sample size was estimated to cover each province with equal number of participants (around 900 each). If it is so, why there are 20% participants from some province and 8% from some province. Please clarify.

As province and ecological belts were considered as independent variables across all NCD risk factors give a brief write up regarding the characteristics of these provinces and ecological belts which are relevant to understand the burden of NCD risk factors.

Though sample size and sampling methods were published elsewhere it is better to give a brief description on the sample size and selection of PSUs.

Under Ethical concern, for minors it require assent from the adolescents and consent from parents. Hence it needs to be clarified accordingly. Avoid the tern assent consent.

Results:

In this survey 15-29 years age group is predominant which is entirely different from the previous STEPS survey or Nepal population distribution based on age.

Please clarify how this over representation from this age group has happened.

Two decimals in results make the tables too crowded. It does not become reader friendly.

There is no display of gender disaggregated results within the province. But the discussion section presented in those lines.

Tobacco use and smokers are used inter changeably. Please use the term consistently

For fruits & vegetables intake, when everything is more than 97% sub group disaggregation does not help much (line no 221 to 223) could be avoided.

Table 2: As majority of the times the number in the sub group is going to remain same across all NCD risk factors it seems there is redundancy the way the n is represented. Instead of total number in the sub group it is preferred to give number with the particular risk factor present under the column n.

Discussion

Comparison of smoking with previous STEPS survey is incomplete.

The speculation for higher prevalence of smoking in Nepal is not justified well. (line No 268-274). Actually the prevalence in Nepal is more that should be explained by extent of lacunae in implementation of current tobacco legislative measures.

Adjusted prevalence ratio or adjusted relative risk has been calculated but it has been interpreted as if they were odds ratios. (line No 322-324, 334-335, 337,350, 353)

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Attachment

Submitted filename: comments.docx

PLoS One. 2021 Jul 30;16(7):e0253605. doi: 10.1371/journal.pone.0253605.r002

Author response to Decision Letter 0


29 May 2021

28 May 2021

Prof. Brecht Devleesschauwer

Academic Editor

PLOS ONE

Re: PONE-D-21-06886 (R1) Prevalence of Noncommunicable Diseases Risk Factors and their Determinants: Results from STEPS survey 2019, Nepal

Dear Professor Devleesschauwer

Thank you very much for your email of 7 April 2021 and comments on our manuscript. We have carefully revised the manuscript in response to the extensive and insightful comments we received from you and reviewer.

In particular, the reviewer provided constructive comments with some corrections. In the revised manuscript version, all the suggested corrections are made. We have also revised the content of introduction, methodology, result and discussion section according to your and reviewer advice and have paid special attention to correcting all typological errors. Appended below is the list of all yours and reviewer comments along with our responses to each point.

We hope that this revised version will be suitable for publication in PLoS ONE.

Yours sincerely,

Meghnath Dhimal, PhD

Editors comments

1. The write-up of the manuscript is quite sloppy, with several typos and grammatical and punctuation errors. We therefore suggest you thoroughly copy-edit your manuscript for language usage, spelling, and grammar. If you do not know anyone who can help you do this, you may wish to consider employing a professional scientific editing service.

Upon resubmission, please provide the following:

- The name of the colleague or the details of the professional service that edited your manuscript

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**Thank you so much for your feedback and suggestions. We have carefully revised our manuscript and our manuscript is proof read by Professor Nick Townsend who is native speaker of English from United Kingdom. Based on his significant contribution in our manuscript, we have included him a co-author in our revised manuscript. Next as per request of WHO colleagues, we have transferred WHO affiliated co-authors in acknowledgements section.

2. Why was Poisson regression used to analyze the binary data, instead of logistic regression? Using Poisson regression for binary data underestimates variance, and hence leads to higher type 1 error.

** Thank you for your feedback. Firstly,to establish the comparability with previous round survey findings (https://journals.plos.org/plosone/article?id=10.1371/journal.pone.0134834) which has also used Poisson regression has motivated us to use the Poisson regression in our analysis. Secondly, the Adjusted Prevalence Ratios rather than odds ratios allowed us to compare the relative strengths of association in a manner that was not biased by whether a risk factors was rare or common. Finally, as the outcomes gets frequent, there is chances of underestimates of variance, to combat with under estimates of variance due to frequent occurrence of events/outcomes, the robust variance estimates Huber's sandwich estimator has been used while running (robust) Poisson regression in STATA i.e., vce (robust).

In your revision note, please include EACH of the reviewer and editor comments, provide your reply, and when relevant, include the modified/new text (or motivate why you decided not to modify the text). Note that failure to do so may result in a rejection of the manuscript.

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*Thank you so much for your suggestion and we have followed Journal’s author guidelines strictly.

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We will update your Data Availability statement on your behalf to reflect the information you provide.

**Thank you so much for your comments and suggestion. The data of this study is available from the WHO NCD Microdata Repository (https://extranet.who.int/ncdsmicrodata/index.php/catalog/771/data_dictionary).

3. We note you have included a table to which you do not refer in the text of your manuscript. Please ensure that you refer to Table 2 and 4 in your text; if accepted, production will need this reference to link the reader to the Table.

[Note: HTML markup is below. Please do not edit.]

**Thank you so much for your suggestion and we have cited all tables in our revised manuscript.

Reviewers Comment

Introduction

The rationale for reporting this study could be reframed better.

For ex. There is no link between the multi sectoral plan and current STEPS survey; linking these two might give better clarity to understand the need for present STEPS survey

** Thank you for your suggestion. We have added following text in our introduction section

In order to track progress on prevention and control of NCD risk factors over the years, the multi-sectoral NCD action plan has included NCD STEPS survey to be conducted in every five years and as follow up of NCD STEPS survey 2013, this survey was conducted.

Methods

Sampling methods adapted in previous STEPS surveys are different from the current survey. The change in context should get mentioned for the readers to compare and interpret these two surveys

** Thank you for your suggestion. Necessary changes has been made in the revised masncuript.

Sampling for the survey took into consideration the current federal structure of Nepal, such that findings could be generalized to the provincial levels. The household listing operation was carried out in 259 PSUs, in order to develop a sampling frame for selection of individual households at the second stage.

As per methods details provided in the reference, the sample size was estimated to cover each province with equal number of participants (around 900 each). If it is so, why there is 20% participants from some province and 8% from some province. Please clarify.

** All the percentage displayed in findings are adjusted using sample weighing and population weighting. So, province wise distribution of participants will be similar to like province population distribution.

As province and ecological belts were considered as independent variables across all NCD risk factors give a brief write up regarding the characteristics of these provinces and ecological belts which are relevant to understand the burden of NCD risk factors.

** Thank you for your suggestion. Necessary changes has been made in revised manuscript

Study settings: Nepal is a landlocked country situated in Southern Asia between India and China. The country runs from a plain area in the South, known as Terai, to the mountainous area of the Himalayas in the North, with a hilly region in between the two. Administratively, Nepal is comprised of 7 provinces, 77 districts and 753 local bodies.

Though sample size and sampling methods were published elsewhere it is better to give a brief description on the sample size and selection of PSUs.

** Thank you for your suggestion. Necessary changes have been made in method section of the revised manuscript.

A total of 259 wards were selected as the primary sampling units (PSU) at the first stage, maintaining 37 PSUs from every province. The household listing operation was carried out in 259 PSUs, in order to develop a sampling frame for selection of individual households at the second stage

Under Ethical concern, for minors it require assent from the adolescents and consent from parents. Hence it needs to be clarified accordingly. Avoid the tern assent consent.

** Thank you for your suggestion. Necessary changes has been made in the revised manuscript

Ethical approval to conduct this survey was granted from the Ethical Review Board (ERB) of the Nepal Health Research Council (NHRC), Government of Nepal (Registration number 293/2018). Written informed consent was obtained from each participant before they enrolled in the survey. In case of minors (under 18 years old) both assent from the research participants and consent from their parents (legal guardian) was obtained, as per national ethical guidelines for health research in Nepal. We also took administrative approval from federal, provincial and local governments, as per the need. The confidentiality of all information gathered was maintained. Any waste generated during the laboratory procedures was properly disinfected using aseptic techniques before being safely disposed of. All blood and urine samples were discarded after completing biochemical measurements.

Results:

In this survey 15-29 years age group is predominant which is entirely different from the previous STEPS survey or Nepal population distribution based on age.

Please clarify how this over representation from this age group has happened.

** There were some error in reporting the percentage, it has been corrected in the revised manuscript. Regarding percentage of 15-29 years age group, it is a weighted percentage i.e 44.9% which is quite near to previous round of survey i.e 46.5% (https://journals.plos.org/plosone/article?id=10.1371/journal.pone.0134834)

Two decimals in results make the tables too crowded. It does not become reader friendly.

There is no display of gender disaggregated results within the province. But the discussion section presented in those lines.

** Thank you. It has been corrected in the revised manuscript (especially on table 2). Regarding gender disaggregation within the province, that was a mistake in reporting in discussion. It has been revised.

We found that participants residing in Province 1, Province 2, Bagmati province, and Lumbini province were less likely to smoke than those in Sudurpaschim province, with this finding aligning with another national level survey

Tobacco use and smokers are used inter changeably. Please use the term consistently

** Thank you for your comments. It has been corrected in revised version of manuscript with a word smoker only.

For fruits & vegetables intake, when everything is more than 97% sub group disaggregation does not help much (line no 221 to 223) could be avoided.

** Thank you for your suggestion. Since every variable has been described individually as well as to maintain comparability with last round survey findings, we think it is wise to describe briefly.

Table 2: As majority of the times the number in the sub group is going to remain same across all NCD risk factors it seems there is redundancy the way the n is represented. Instead of total number in the sub group it is preferred to give number with the particular risk factor present under the column n.

** Thank you for your suggestion. As for every variable sample size (n) has varied due to missing of data, so, it has been reported separately for every variable.

Discussion

Comparison of smoking with previous STEPS survey is incomplete.

The speculation for higher prevalence of smoking in Nepal is not justified well. (line No 268-274). Actually, the prevalence in Nepal is more that should be explained by extent of lacunae in implementation of current tobacco legislative measures.

** Thank you for your suggestions. Necessary changes has been made in the revised manuscript.

Adjusted prevalence ratio or adjusted relative risk has been calculated but it has been interpreted as if they were odds ratios. (line No 322-324, 334-335, 337,350, 353).

** Thank you for your valuable suggestions. In the above-mentioned line number as well as in other line number, it has been corrected in the revised manuscript.

This study has the lot of scope to bring out the change or impact of previous strategic plan through comparison of previous STEPs survey with the current. In similar lines, if there is reduction or increase in the NCD risk factor level the reasons could have been explained better.

** Thank you for your valuable suggestions. As per suggestions, necessary amendment has been made in the revised manuscript with possible explanation wherever possible. But in case of some variables, comparison is not possible due to difference in methodological or instrumental difference (eg. Level of Cholesterol), that has been explained wherever necessary as a limitation of the study.

Attachment

Submitted filename: Rebuttal Letter MD rev.docx

Decision Letter 1

Brecht Devleesschauwer

9 Jun 2021

Prevalence of noncommunicable diseases risk factors and their determinants: Results from STEPS survey 2019, Nepal

PONE-D-21-06886R1

Dear Dr. Dhimal,

We’re pleased to inform you that your manuscript has been judged scientifically suitable for publication and will be formally accepted for publication once it meets all outstanding technical requirements.

Within one week, you’ll receive an e-mail detailing the required amendments. When these have been addressed, you’ll receive a formal acceptance letter and your manuscript will be scheduled for publication.

An invoice for payment will follow shortly after the formal acceptance. To ensure an efficient process, please log into Editorial Manager at http://www.editorialmanager.com/pone/, click the 'Update My Information' link at the top of the page, and double check that your user information is up-to-date. If you have any billing related questions, please contact our Author Billing department directly at authorbilling@plos.org.

If your institution or institutions have a press office, please notify them about your upcoming paper to help maximize its impact. If they’ll be preparing press materials, please inform our press team as soon as possible -- no later than 48 hours after receiving the formal acceptance. Your manuscript will remain under strict press embargo until 2 pm Eastern Time on the date of publication. For more information, please contact onepress@plos.org.

Kind regards,

Brecht Devleesschauwer

Academic Editor

PLOS ONE

Additional Editor Comments (optional):

Reviewers' comments:

Reviewer's Responses to Questions

Comments to the Author

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Reviewer #1: All comments have been addressed

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2. Is the manuscript technically sound, and do the data support the conclusions?

The manuscript must describe a technically sound piece of scientific research with data that supports the conclusions. Experiments must have been conducted rigorously, with appropriate controls, replication, and sample sizes. The conclusions must be drawn appropriately based on the data presented.

Reviewer #1: Yes

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3. Has the statistical analysis been performed appropriately and rigorously?

Reviewer #1: Yes

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Reviewer #1: Yes

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Reviewer #1: Yes

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Reviewer #1: The manuscript has been substantially revised in terms of language and Reviewers comments.

The survey methods and rationale section has been revised according to previous comments. Similarly, the findings are appropriately interpreted as adjusted Prevalence ratio

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Do you want your identity to be public for this peer review? For information about this choice, including consent withdrawal, please see our Privacy Policy.

Reviewer #1: No

Acceptance letter

Brecht Devleesschauwer

22 Jul 2021

PONE-D-21-06886R1

Prevalence of non-communicable diseases risk factors and their determinants: Results from STEPS survey 2019, Nepal

Dear Dr. Dhimal:

I'm 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.

If your institution or institutions have a press office, please let them know about your upcoming paper now to help maximize its impact. If they'll be preparing press materials, please inform our press team within the next 48 hours. Your manuscript will remain under strict press embargo until 2 pm Eastern Time on the date of publication. For more information please contact onepress@plos.org.

If we can help with anything else, please email us at plosone@plos.org.

Thank you for submitting your work to PLOS ONE and supporting open access.

Kind regards,

PLOS ONE Editorial Office Staff

on behalf of

Prof. Dr. Brecht Devleesschauwer

Academic Editor

PLOS ONE

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    Submitted filename: comments.docx

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    Submitted filename: Rebuttal Letter MD rev.docx

    Data Availability Statement

    All relevant data are within the manuscript and its Supporting information files.


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