# Behavioural Risk Factors of Noncommunicable Diseases among Nepalese Urban Poor: A Descriptive Study from a Slum Area of Kathmandu

Authors: Natalia Oli; Abhinav Vaidya; Gobardhan Thapa
Journal: Epidemiology Research International
 (2013)

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## Abstract

There has been a rapid rise in the burden of noncommunicable diseases in low-income countries like Nepal. Political and economical instability leading to internal migration give rise to haphazard urbanization in Nepal. This, coupled with negative effects of globalization, is largely responsible for changing lifestyle and developing risky behaviour among the urban poor that put them at high risk of developing noncommunicable diseases. A descriptive cross-sectional quantitative study was conducted from September to December 2012 in an urban slum of Kathmandu to explore the prevalence of four major behaviour risk factors namely physical inactivity, low fruit and vegetable consumption, and tobacco and alcohol use and to measure the burden of obesity and hypertension in the population. We used WHO NCDs Risk Factor steps 1 and 2 questionnaires in all the 689 households of the slum. The major behavioral risk factors for noncommunicable diseases were very common with at least a quarter of the population having the major risk factors. The results may serve to form a framework to future planning, policy-making, implementation, and evaluation of any measures undertaken to reduce these risk factors, especially as the government is planning to unveil the National Urban Health Policy soon.


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## Body

## 1. Introduction

It is well established that noncommunicable diseases (NCDs) are the leading cause of adult mortality and morbidity worldwide including the Southeast Asia region (SEAR) [1]. Four main NCDs, namely cardiovascular diseases (CVDs), diabetes, cancers, and chronic respiratory diseases, are mainly responsible for this high mortality and morbidity. Of the estimated 14.5 million total deaths in 2008 in SEAR, more than half (55%) of them were due to NCDs, mainly cardiovascular disease (25%) [1].From the beginning, NCDs and particularly CVDs were termed diseases of the rich in the developed countries [2]. However, over the past two decades, CVD deaths have been declining in the high-income countries but increasing significantly in the low- and middle-income countries (LMICs) [3]. One of the reasons for this increase is the rising life expectancy like the one being seen in South Asia region which leads to shifting disease burden towards NCDs [4]. Moreover, at the same time, the trend of urbanization is drastically increasing in this region causing changes in lifestyle of the people. For example, shifting lifestyle towards low physical activity and unhealthy diet leads to a rise in prevalence of obesity and NCDs among urban population [5]. For this reason, NCDs have also been called “diseases of urbanization”. Risk factors of NCDs such as high blood pressure are found more commonly among urban communities compared to rural in the developing countries [6].In 2008, half of the world’s population lived in urban areas and if this trend continues, 70 percent of the global population will be living in urban areas by 2050 [7]. No doubt that one of the negative impacts of this demographic transition is formation of slum and squatter settlements in the urban area. UN-HABITAT has estimated some 38 percent of the LMIC population lives in slums with 433 million of them living in Asia [8]. Slum communities are defined as a wide range of low-income settlements with inadequate living conditions and substandard facilities. The residents of slums areas generally own their land and house and have formal title papers. On the other hand, squatter settlements are those communities where people have settled on land without any legal right to be there. But as the patterns of living in both these settlements are similar, the term slum is often used to describe both. The urban poor have the most disadvantageous position among all the urban population. This is because they adopt urban lifestyle that put them at a higher risk of developing NCDs and at the same time, lack of knowledge and information regarding health, poor access to healthcare, and poor economic status worsen the situation among them [6].Nepal is not an exception in regard to urbanization. According to the national census 2011, about 17 percent of the total Nepalese population live in urban areas [9]. Most of the urban population is concentrated in the capital Kathmandu and its rapid growth has led to increasing pressure in Kathmandu. At present, there are 63 slum and squatter settlements in Kathmandu, and their population is estimated to be growing by 25 percent per year [10]. Most of the slums settlements are located in the centre of the city along the river banks of Bagamati, Bishnumati, and Manohara Rivers. People in slum areas have high levels of health risks and problems due to inadequate water supply, basic sanitation, and proper nutrition, amongst others.Facing the epidemiological transition like many LMICs, Nepal is also experiencing double burden of communicable and noncommunicable diseases [11]. CVDs are the most common cause of NCD admissions in Nepal (38%) according to a 2010 hospital-based study [12]. It is well known that up to 80% of heart disease, stroke, and type 2 diabetes and over a third of cancers could be prevented by eliminating common risk factors, mainly tobacco use, unhealthy diet, physical inactivity, and the harmful use of alcohol [13]. These unhealthy behaviours lead to metabolic changes such as raised blood pressure, obesity, raised blood sugar, and lipids. Moreover, coexistence of modifiable behavioural and metabolic risk factors in the same person increases the individual’s total risk of developing acute vascular events such as heart attacks and strokes [3].Studies and interventions in the Nepalese slums deal mainly with communicable diseases, environmental, and sanitation problems, while NCDs have remained largely neglected [10, 14–16]. Thus, very little is known about the spectrum and burden of NCD morbidity in the Nepalese slum population. Because people from slum do come to hospitals with late complications of NCDs, it is obvious that NCDs exist among urban poor as well [17, 18]. Reasons for this late presentation could be their low health seeking behaviour due to lack of health knowledge and information and poor purchasing ability [17].A number of studies have been conducted in Nepal using WHO steps risk factors questionnaire in different urban and rural areas [19]. These studies have however eluded slum and squatters areas. Hence, we undertook the present study to measure the prevalence of four major behaviour risk factors, namely physical inactivity, low fruit and vegetable consumption, and tobacco and alcohol use, in a slum population of Kathmandu. Additionally, we also aimed to find out the level of two major physical risk factors, obesity, and hypertension, in this study.


## 2. Methods

### 2.1. Study Site and Population

The study was of descriptive cross-sectional quantitative design. It was conducted in the Sinamangal squatter area in Kathmandu Metropolitan City along the banks of Bagmati River, from September to December 2012 (Figure1). The study area was purposively selected because it is a predefined study area for Family Health Advisory Programme for medical students under the Department of Community Medicine, Kathmandu Medical College. The study area is about one kilometre away from the medical college. As the squatter area has settlements scattered both upstream and downstream, the area of the squatter settlement falling in the Sinamangal area was first demarcated. It was then arbitrarily divided into four clusters.Figure 1
Map of the Sinamangal slum area, Kathmandu. The study area is shown as the shaded area (yellow) along the banks of the river (grey) across the middle of the map.The study population included household members aged 15–64 years living for at least 6 months in the defined squatter area and who consented to participate in the study. Because there was no official information about the number of households in the squatter area to construct a sampling frame, nonprobability convenient sampling technique was used.


### 2.2. Data Collection

Data was collected by third-year medical students of Kathmandu Medical College. The eighty medical students were divided in to four groups of 20 for each of the four clusters. The students were trained by the researchers in taking informed consent, administering questionnaire, interview techniques, and physical measurements. Each household was visited and the person available at that time was interviewed. If nobody was present in the household during the first visit, the student revisited it again at the time when the resident of the house would be available.If there was more than one eligible respondent in a household, a lottery method was used to select one respondent. The students were continuously supervised by the researchers during the data collection process.


### 2.3. Study Tools

#### 2.3.1. Questionnaire

Data was collected through face-to-face interviews using questions based on WHO NCD risk factor steps 1 and 2 questionnaires [20]. Biochemical measurements of step 3 were not performed because of logistic limitations. The questionnaire included questions regarding demographic characteristic of the respondents, tobacco and alcohol consumption, physical activity, and fruit and vegetable consumption.


#### 2.3.2. Physical Measurements

Height, weight, waist, and hip circumferences were measured. The instructions given in the step 2 were followed while taking the measurements. Weight was measured with Microlife BR-9201 weighing machine (Microlife AG Swiss Corporation, Widnau, Switzerland). Height, waist, and hip circumferences were measured with nonstretchable measuring tapes (Jonson Tapes Ltd., New Delhi, India). Aneroid sphygmomanometers (Doctor Aneroid Sphygmomanometer, Japan) were used for blood pressure recording. Blood pressure was recorded three times with a gap of 5 minutes between the recordings. Average of the three readings was taken as the final blood pressure measurement.


#### 2.3.3. Operational Definitions

The definitions used for the study were based on the WHO steps survey manual and are briefly described below [20]. Occupation and educational categories are also based on the WHO steps survey manual. Classification of ethnic group is based on the National Central Bureau of Statistics of Nepal [9]:(a)
current drinkers: respondents who consumed alcohol in the previous 30 days;(b)
one serving of vegetable: one cup of raw, leafy green vegetables (spinach, salad, etc.), one half cup of other vegetables, cooked or raw (tomatoes, pumpkin, beans etc.), or half cup of vegetable juice;(c)
one serving of fruit: one medium-sized piece of fruit (banana, apple, etc.) or half cup of raw, cooked or canned fruit, or a half cup of juice from a fruit (not artificially flavoured);(d)
physical activity: it included questions on number of days and time spent on vigorous and/or moderate activities at work, travel to and from places, and recreational activities. The responses were then converted to MET minutes/week. The respondents were labelled as having vigorous activity or moderate activity if they achieved certain MET minutes as given in the WHO steps manual. Someone was termed as having low physical activity (LBA) if s/he did not fulfil the criteria of having vigorous or moderate activity;(e)
hypertension: it included those who self-reported as having hypertension or those who had high blood pressure according to JNC-VII classification [21] during the time of survey;(f)
combined risk factors: the following risk factors were considered while counting the number of risk factors present:
(i)
current daily smoker,(ii)
less than 5 servings of fruits and vegetables per day,(iii)
low physical activity (<600 MET minutes),(iv)
overweight or obese (BMI ≥ 25 kg/m²),(v)
raised BP (SBP ≥ 140 and/or DBP ≥ 90 mmHg or currently on medication for raised BP).


### 2.4. Data Management and Analysis

The filled questionnaires were thoroughly checked by the researchers for any inconsistencies. Data coding and cleaning were done. Data was entered and analysed with SPSS version 20.0 (IBM, Armonk, New York, USA). Descriptive analysis was done; mean with standard deviations was calculated for continuous variables, and percentages were calculated for categorical variables.


### 2.5. Ethical Consideration

The study obtained approval from Ethical Review Committee of Kathmandu Medical College. All principles of Helsinki Declaration were ensured during the study. Informed consent was obtained from each participant after the objectives of study had been explained. Privacy and confidentiality were thoroughly maintained during the interview. Filled questionnaires were securely placed in the Department of Community Medicine. There were no physical risks as there was no intervention such as blood sampling done during the study. However, there was possible psychological risk due to questions related to income and personal behaviours, and the students were trained to sensitively handle them. Those who were found to have the risk factors under the study were counselled and/or referred to Kathmandu Medical College for the needful.


## 2.1. Study Site and Population

The study was of descriptive cross-sectional quantitative design. It was conducted in the Sinamangal squatter area in Kathmandu Metropolitan City along the banks of Bagmati River, from September to December 2012 (Figure1). The study area was purposively selected because it is a predefined study area for Family Health Advisory Programme for medical students under the Department of Community Medicine, Kathmandu Medical College. The study area is about one kilometre away from the medical college. As the squatter area has settlements scattered both upstream and downstream, the area of the squatter settlement falling in the Sinamangal area was first demarcated. It was then arbitrarily divided into four clusters.Figure 1
Map of the Sinamangal slum area, Kathmandu. The study area is shown as the shaded area (yellow) along the banks of the river (grey) across the middle of the map.The study population included household members aged 15–64 years living for at least 6 months in the defined squatter area and who consented to participate in the study. Because there was no official information about the number of households in the squatter area to construct a sampling frame, nonprobability convenient sampling technique was used.


## 2.2. Data Collection

Data was collected by third-year medical students of Kathmandu Medical College. The eighty medical students were divided in to four groups of 20 for each of the four clusters. The students were trained by the researchers in taking informed consent, administering questionnaire, interview techniques, and physical measurements. Each household was visited and the person available at that time was interviewed. If nobody was present in the household during the first visit, the student revisited it again at the time when the resident of the house would be available.If there was more than one eligible respondent in a household, a lottery method was used to select one respondent. The students were continuously supervised by the researchers during the data collection process.


## 2.3. Study Tools

### 2.3.1. Questionnaire

Data was collected through face-to-face interviews using questions based on WHO NCD risk factor steps 1 and 2 questionnaires [20]. Biochemical measurements of step 3 were not performed because of logistic limitations. The questionnaire included questions regarding demographic characteristic of the respondents, tobacco and alcohol consumption, physical activity, and fruit and vegetable consumption.


### 2.3.2. Physical Measurements

Height, weight, waist, and hip circumferences were measured. The instructions given in the step 2 were followed while taking the measurements. Weight was measured with Microlife BR-9201 weighing machine (Microlife AG Swiss Corporation, Widnau, Switzerland). Height, waist, and hip circumferences were measured with nonstretchable measuring tapes (Jonson Tapes Ltd., New Delhi, India). Aneroid sphygmomanometers (Doctor Aneroid Sphygmomanometer, Japan) were used for blood pressure recording. Blood pressure was recorded three times with a gap of 5 minutes between the recordings. Average of the three readings was taken as the final blood pressure measurement.


### 2.3.3. Operational Definitions

The definitions used for the study were based on the WHO steps survey manual and are briefly described below [20]. Occupation and educational categories are also based on the WHO steps survey manual. Classification of ethnic group is based on the National Central Bureau of Statistics of Nepal [9]:(a)
current drinkers: respondents who consumed alcohol in the previous 30 days;(b)
one serving of vegetable: one cup of raw, leafy green vegetables (spinach, salad, etc.), one half cup of other vegetables, cooked or raw (tomatoes, pumpkin, beans etc.), or half cup of vegetable juice;(c)
one serving of fruit: one medium-sized piece of fruit (banana, apple, etc.) or half cup of raw, cooked or canned fruit, or a half cup of juice from a fruit (not artificially flavoured);(d)
physical activity: it included questions on number of days and time spent on vigorous and/or moderate activities at work, travel to and from places, and recreational activities. The responses were then converted to MET minutes/week. The respondents were labelled as having vigorous activity or moderate activity if they achieved certain MET minutes as given in the WHO steps manual. Someone was termed as having low physical activity (LBA) if s/he did not fulfil the criteria of having vigorous or moderate activity;(e)
hypertension: it included those who self-reported as having hypertension or those who had high blood pressure according to JNC-VII classification [21] during the time of survey;(f)
combined risk factors: the following risk factors were considered while counting the number of risk factors present:
(i)
current daily smoker,(ii)
less than 5 servings of fruits and vegetables per day,(iii)
low physical activity (<600 MET minutes),(iv)
overweight or obese (BMI ≥ 25 kg/m²),(v)
raised BP (SBP ≥ 140 and/or DBP ≥ 90 mmHg or currently on medication for raised BP).


## 2.3.1. Questionnaire

Data was collected through face-to-face interviews using questions based on WHO NCD risk factor steps 1 and 2 questionnaires [20]. Biochemical measurements of step 3 were not performed because of logistic limitations. The questionnaire included questions regarding demographic characteristic of the respondents, tobacco and alcohol consumption, physical activity, and fruit and vegetable consumption.


## 2.3.2. Physical Measurements

Height, weight, waist, and hip circumferences were measured. The instructions given in the step 2 were followed while taking the measurements. Weight was measured with Microlife BR-9201 weighing machine (Microlife AG Swiss Corporation, Widnau, Switzerland). Height, waist, and hip circumferences were measured with nonstretchable measuring tapes (Jonson Tapes Ltd., New Delhi, India). Aneroid sphygmomanometers (Doctor Aneroid Sphygmomanometer, Japan) were used for blood pressure recording. Blood pressure was recorded three times with a gap of 5 minutes between the recordings. Average of the three readings was taken as the final blood pressure measurement.


## 2.3.3. Operational Definitions

The definitions used for the study were based on the WHO steps survey manual and are briefly described below [20]. Occupation and educational categories are also based on the WHO steps survey manual. Classification of ethnic group is based on the National Central Bureau of Statistics of Nepal [9]:(a)
current drinkers: respondents who consumed alcohol in the previous 30 days;(b)
one serving of vegetable: one cup of raw, leafy green vegetables (spinach, salad, etc.), one half cup of other vegetables, cooked or raw (tomatoes, pumpkin, beans etc.), or half cup of vegetable juice;(c)
one serving of fruit: one medium-sized piece of fruit (banana, apple, etc.) or half cup of raw, cooked or canned fruit, or a half cup of juice from a fruit (not artificially flavoured);(d)
physical activity: it included questions on number of days and time spent on vigorous and/or moderate activities at work, travel to and from places, and recreational activities. The responses were then converted to MET minutes/week. The respondents were labelled as having vigorous activity or moderate activity if they achieved certain MET minutes as given in the WHO steps manual. Someone was termed as having low physical activity (LBA) if s/he did not fulfil the criteria of having vigorous or moderate activity;(e)
hypertension: it included those who self-reported as having hypertension or those who had high blood pressure according to JNC-VII classification [21] during the time of survey;(f)
combined risk factors: the following risk factors were considered while counting the number of risk factors present:
(i)
current daily smoker,(ii)
less than 5 servings of fruits and vegetables per day,(iii)
low physical activity (<600 MET minutes),(iv)
overweight or obese (BMI ≥ 25 kg/m²),(v)
raised BP (SBP ≥ 140 and/or DBP ≥ 90 mmHg or currently on medication for raised BP).


## 2.4. Data Management and Analysis

The filled questionnaires were thoroughly checked by the researchers for any inconsistencies. Data coding and cleaning were done. Data was entered and analysed with SPSS version 20.0 (IBM, Armonk, New York, USA). Descriptive analysis was done; mean with standard deviations was calculated for continuous variables, and percentages were calculated for categorical variables.


## 2.5. Ethical Consideration

The study obtained approval from Ethical Review Committee of Kathmandu Medical College. All principles of Helsinki Declaration were ensured during the study. Informed consent was obtained from each participant after the objectives of study had been explained. Privacy and confidentiality were thoroughly maintained during the interview. Filled questionnaires were securely placed in the Department of Community Medicine. There were no physical risks as there was no intervention such as blood sampling done during the study. However, there was possible psychological risk due to questions related to income and personal behaviours, and the students were trained to sensitively handle them. Those who were found to have the risk factors under the study were counselled and/or referred to Kathmandu Medical College for the needful.


## 3. Results and Discussion

Altogether 689 households were identified in the defined study area and we included one person from each household; thus, we had a sample size of 689. There were no non-responders. Based on the response given by the respondents about the number of adults aged 15–64 years in his/her house, we calculated that there were 2356 adults of age 15–64 residing in the area. Thus, our study covered 29.24% of the adults aged 15–64 years living in the area.


### 3.1. Demographic Characteristics

The demographic characteristics of the study population are described in Table1. Half of the respondents were aged less than 35 years. About 60% were women. About 40% had no formal education. About one third of the respondents were self-employed, while a quarter of the women were housewives. Thirteen percent of the respondents were unemployed though they were able to work. Majority (72.3%) of them had been staying there for more than five years. More than one third of the respondents had a monthly income of less than NRs. 5000 (1 US Dollar = approximately 85 Nepalese Rupees). Most of them had come to live in the squatter because of economic reasons, in search of a job, because of natural calamities like flood in their native places, or due to marriage. They had migrated mainly from other parts of Kathmandu district (22%), or from other nearby districts such as Kavrepalanchok (6%) and Sindhupalchowk (3%), or from Eastern Nepal districts such as Sindhuli (10%), Jhapa (7%), and Morang (5%).Table 1
Demographic characteristics of the study population (N=689). Classification of ethnic group is based on the National Central Bureau of Statistics [9]. Primary and secondary schooling indicate education up to grades 4 and 10, respectively. Other ethnic group includes Terai Dalit, disadvantaged Janajatis (Terai), disadvantaged non-Dalit Terai caste groups, and religious minorities. Other occupations include nonpaid, retired, and unemployed ones (unable to work).
Male
Female
Total
N
%
N
%
N
%
Age (years)
15–24
75
26.5
117
28.8
192
27.9
25–34
56
19.8
109
26.8
165
23.9
35–44
61
21.6
87
21.4
148
21.5
45–54
48
17.0
53
13.1
101
14.7
55–64
29
10.2
23
5.7
52
7.5
≥65
14
4.9
17
4.2
31
4.5
Ethnicity
Hill Dalit
33
11.7
66
16.3
99
14.4
Disadvantaged Janajatis (Hill)
113
39.9
185
45.6
298
43.3
Relatively advantaged Janajatis
34
12.0
30
7.4
64
9.3
Upper caste groups
89
31.4
112
27.6
201
29.2
Others
14
49
13
3.2
27
3.9
Highest education
No formal education
71
25.1
209
51.5
280
40.6
Less than primary
27
9.5
19
4.7
46
6.7
Primary completed
109
38.5
106
26.1
215
31.2
Secondary completed
58
20.5
55
13.5
113
16.4
High school or more
18
6.4
17
4.2
35
5.1
Occupation
Employed (government/nongovernment)
59
20.8
32
7.9
90
13.1
Self-employed
137
48.4
109
26.8
246
35.7
Student
21
7.4
38
9.4
59
8.6
Housewife
0
0.0
159
39.2
163
23.7
Unemployed (able to work)
46
16.3
45
11.1
89
12.9
Others
20
7.1
23
5.7
42
6.1
Migration (years)
<1
28
9.9
38
9.4
66
9.6
1–5
43
15.2
82
20.2
125
18.1
5–10
98
34.6
139
34.2
237
34.4
>10
114
40.3
147
36.2
261
37.9
Income status (NRs) (n=391)
1st quartile (≤5000)
55
27.9
91
46.9
146
37.3
2nd quartile (5001–7000)
32
16.2
35
18.0
67
17.1
3rd quartile (7001–10000)
51
25.9
42
21.6
93
23.8
4th quartile (≥10000)
59
29.9
26
13.4
85
21.7
NRs: Nepalese rupees.


### 3.2. Behavioural Risk Factors

The urban poor remain neglected part of urban population in terms of exploring their burden of behaviour risk factors and prevalence of NCDs among them. During the present study, it was found that the four important behavioural risk factors of NCDs—tobacco consumption, harmful use of alcohol, low fruit and vegetable consumption, and physical inactivity—are rampant in the slum population. Moreover, hypertension and obesity are also prevalent in the study population.


#### 3.2.1. Tobacco Consumption

Age-sex distribution of smoking is presented in Figure2. Prevalence of smoking in men, women, and both sexes is 51.9%, 24.1%, and 35.6%. The overall (both sexes) prevalence of smoking in the present study is higher than the national data (23.8%) [19]. The smoking rate in the population is higher than that in the urban population of Myanmar (22.0%) [22] and the Chennai (20.2%) [23] and Patna (12.54%) [24] slums of India but lower than that in the Mumbai slum (66.7%) [18]. But compared to the urban poor from Kerala, India, there is not a large difference (37.3%) [25].Figure 2
Prevalence (%) of current smokers in the study population according to the age group (n=689). The categories are based on the WHO NCD risk factor steps survey manual [20]. Current smokers included those who responded “yes” to “Do you smoke?”. Dark and light grey bars are for men and women, respectively, while the black bars represent both sexes.By gender-wise stratification also, more men and women in our study smoke compared to the of the Nepal’s national data (31.2% and 15.5%) [19], the urban population of Myanmar (44.8 and 7.8%) [22], and the urban slum of Patna (21.15% and 5.91%) [24]. Comparatively, fewer women (12.2%) and more men (62.6%) in the Kerala slum smoked [25].Regarding smokeless tobacco, the overall consumption rate in our study is 24.8% (men: 38.5%, women: 15.3%). Age-sex distribution of smokeless tobacco is shown in Figure3. Compared to the national figures (18.6% for both sexes, 31.2% for men, and 4.6% for women), prevalence of consumption of smokeless tobacco is also higher in the study population [19]. The figures are however less compared to data from India and Myanmar. There are twice more consumers (55.4%) in the Mumbai slum [18] than in our study. Similarly, the prevalence in the urban Myanmar is also higher with 51.4% of the males and 16.1% of females consuming smokeless tobacco [22].Figure 3
Prevalence (%) of current Smokeless tobacco in the study population according to the age group (n=689). The categories are based on the WHO NCD risk factor steps survey manual [20]. Smokeless tobacco included products such as gutka, khaini, and surti. Dark and light grey bars are for men and women respectively, while the black bars represent both sexes.It is already an established fact that tobacco consumption is now universally more common among lower socioeconomic groups. For example, prevalence of tobacco chewing among women labourers in Dharan, Nepal (22%), was twice as much as among service class women (10%) [26].


#### 3.2.2. Alcohol Consumption

Alcohol consumption in the slum is very high with 38.5% of the study population (58% of men and 24.9% of women) being current drinkers, that is, who had alcohol containing drink in the last 30 days. Among the current drinkers, one third consumes it daily (men: 34.6%, women: 31.1%). Comparatively, less people (31.2%) in the Mumbai slum drank [18]. Age-sex distribution of current drinkers is shown in Figure 4. Prevalences of alcohol consumption in the males and females of the study area were higher than the national averages for men (39.3%) and women (16.5%) [19]. Among males, the current drinking rate is higher than that in their counterparts from Patna (16.65%) [24] and Kerala (45.4%) [25]. The highest prevalence of current drinking was seen in the 35–44 years age group (72.1%). Similarly, more females in the Sinamangal slum drink more than the women of Patna slum (3.35%) [24] and in Kerala slum (1.0%) [25].Figure 4
Prevalence (%) of current drinkers in the study population according to the age group (n=689). The categories are based on the WHO NCD risk factor steps survey manual [20]. Current drinkers refer to the ones that consumed alcohol within the previous month. Dark and light grey bars are for men and women, respectively, while the black bars represent both sexes.


#### 3.2.3. Fruits and Vegetable Consumption

The mean fruit consumption was less than two days per week with about 20% taking fruits less than once a week (Table2). Comparatively, vegetable consumption was better with average six days per week. A higher level of fruit and vegetable intake has been observed among the urban slum dwellers in Chennai where more than half population consumed vegetables 5 days and fruits 3 days per week [27]. The mean number of servings of fruit and/or vegetables per day in the present study was low—one serving per day for fruits and two servings per day for vegetables (Table 2). Myanmar urban slum population had better fruit and vegetable consumption [22].Table 2
Fruit and vegetable consumption in the study population (N=689). One serving of fruit means one medium-sized piece of fruit (banana, apple, etc.) or half cup of raw, cooked or canned fruit, or a half cup of juice from a fruit (not artificially flavoured). One serving of vegetable is one cup of raw, leafy green vegetables, (spinach, salad, etc.), one half cup of other vegetables, cooked or raw (tomatoes, pumpkin, beans, etc.), or half cup of vegetable juice.
Male
Female
Total
N
%
N
%
N
%
Fruit consumption
Days/week
0
60
21.2
79
19.5
139
20.2
1–3
181
64.0
272
67.0
453
65.7
4–6
29
10.2
42
10.3
71
10.3
7
13
4.6
13
3.2
26
3.8
Mean (SD)
1.96 (1.76)
1.94 (1.66)
1.95 (1.70)
Servings/day
0
60
21.2
79
19.5
139
20.2
1-2
204
72.1
312
76.8
516
74.9
≥3
19
6.7
15
3.7
34
4.9
Mean (SD)
1.08 (0.81)
1.03 (0.72)
1.05 (0.76)
Vegetable consumption
Days/week
0
4
1.4
9
2.2
13
1.9
1–3
26
9.2
38
9.4
64
9.3
4–6
82
29.0
104
25.4
186
27.0
7
171
60.4
255
62.8
426
61.8
Mean (SD)
6.01 (1.59)
5.95 (1.72)
5.98 (1.67)
Servings/day
0
4
1.4
9
2.2
13
1.9
1-2
250
88.3
354
87.2
604
87.7
≥3
29
10.2
43
10.6
72
10.4
Mean (SD)
1.80 (0.84)
1.73 (0.97)
1.76 (0.92)
Combined fruit and vegetable
Servings
<5
256
90.5
379
93.3
635
92.2
≥5
27
9.5
27
6.7
54
7.8
Mean (SD)
2.88 (1.32)
2.76 (1.33)
2.81 (1.33)
SD: standard deviation.More than five servings of fruit and vegetables are recommended for healthy living, but 90.5% men and 93.3% women consume less than the recommended five servings of fruit and vegetables daily in the study area. The situation in the slum is worse compared to the average Nepalese data according to which 60.5% of men and 63.5% of women have inadequate fruit and vegetable consumption [19]. Our study population fared worse in comparison to other studies in the similar settings as well [22, 25, 27]. Low consumption of fruits and vegetables in the urban poor can be due to lack of health knowledge, poor economic status of urban poor, and preference for junk food that put them at high risk of developing NCDs.


#### 3.2.4. High Salt Containing Food Consumption

About 14% of the respondents are taking high salt containing food such as chips, instant noodles, and pickles every day. While a quarter of the respondents are taking such food less than once a week or 1-2 times in a week, about 17% almost never had them.


#### 3.2.5. Level of Physical Inactivity

One third of the study population in the slum have low physical activity. This contrasts with the lower prevalence of physical inactivity across Nepal (5.9%) [19] or the Kerala slum (4.5%) [25] but may be considered less compared to the very high physical inactivity Chennai figure of 95% [23]. The data is however similar to the one from the Patna slum (33.64%) [24].Prevalence of low physical activity in the Sinamangal slum is higher in the females (39.4%) than in the males (21.9%). In fact, the women here are more inactive than those from Patna and Kerala slum settlements (37.65% and 4.9%, resp.) [24, 25]. At the same time, fewer males in the present study were physically inactive in contrast with 30.55% of males in the Patna slum [24]. Furthermore, low physical activity increased drastically from the average of 20% in the younger age group to around 60% in those aged 65 or more (Table 3). Those employed either in the government or nongovernmental organizations had less prevalence of low physical activity (10%). Whereas both men and women belonging to the lowest income group had similar prevalence of low physical activity, the women with high income tended to become less physically active compared to men of the same income group.Table 3
Prevalence (%) of low physical activity in the study population according to demographic variables (N=689). The physical activity section included questions on number of days and time spent on vigorous and/or moderate activities at work, travel to and from places, and recreational activities. The responses were then converted to MET minutes/week. The respondents were labelled as having vigorous activity or moderate activity if they achieved certain MET minutes as given in the WHO steps manual [20]. Someone was termed as having low physical activity (LBA) if s/he did not fulfil the criteria of having vigorous or moderate activity.
Male
Female
Total
N
%
N
%
N
%
Age (years)
15–24
15
20.0
47
40.2
62
32.3
25–34
12
21.4
41
37.6
53
32.1
35–44
12
19.7
28
32.3
40
27.0
45–54
9
18.8
18
34.0
27
26.7
55–64
6
20.7
15
65.2
21
40.2
≥65
8
57.1
11
64.7
19
61.3
Ethnicity
Hill Dalit
6
18.2
25
37.9
31
31.3
Disadvantaged Janajatis (Hill)
29
25.7
78
42.2
107
35.9
Relatively advantaged Janajatis
5
14.7
9
30.0
14
21.9
Upper caste groups
18
20.2
45
40.2
63
31.3
Others
4
28.6
3
23.1
7
25.9
Highest education
No formal education
22
31.0
83
39.7
105
37.5
Less than primary
4
14.8
4
21.1
8
17.4
Primary completed
20
18.3
50
47.2
70
32.6
Secondary completed
13
22.4
16
29.1
29
25.7
High school or more
3
16.7
7
41.2
10
28.6
Occupation
Employed (government/nongovernment)
3
5.2
6
18.8
9
10.0
Self-employed
26
19.0
36
33.0
62
25.2
Student
5
23.8
12
31.6
17
28.8
Housewife
—
—
64
40.3
64
40.3
Unemployed (able to work)
15
34.1
27
60.0
42
47.2
Others
13
68.4
15
65.2
28
66.7
Income status (NRs) (n=391)
1st quartile (≤5000)
13
23.6
20
22.0
33
22.6
2nd quartile (5001–7000)
4
12.5
12
34.3
16
23.9
3rd quartile (7001–10000)
6
11.8
19
45.2
25
26.9
4th quartile (≥10000)
7
11.9
11
42.3
18
21.2
Total
62
21.9
160
39.4
222
32.2
NRs: Nepalese rupees.


#### 3.2.6. Blood Pressure Status

A respondent who was either a known hypertensive or was found to have high blood pressure during the survey was called having “hypertension.” Twenty-eight percent of the respondents are thus found to have hypertension (Table4). This prevalence is higher than the national average (21.5%) [19]. It is also high compared to the Indian slums of Chennai (22.8%) [23] and Patna (16.36%) [24]. On the other hand, the Kerala slum has a slightly higher (31%) prevalence of high blood pressure [25].Table 4
Prevalence of hypertension in the study population according to demographic variables (N=689). Hypertensives included those who self-reported as having hypertension or those who had high blood pressure according to JNC-VII classification [21] during the time of survey.
Male
Female
Total
N
%
N
%
N
%
Age (years)
15–24
11
14.7
7
6.0
18
9.4
25–34
12
21.4
17
15.6
29
17.6
35–44
26
42.6
34
39.1
60
40.5
45–54
24
50.0
18
34.0
42
41.6
55–64
18
62.1
11
47.8
29
55.8
≥65
8
57.1
7
41.2
15
48.4
Ethnicity
Hill Dalit
14
42.4
12
18.2
26
26.3
Disadvantaged Janajatis (Hill)
39
34.5
47
25.4
86
28.9
Relatively advantaged Janajatis
13
38.2
12
40.0
25
39.1
Upper caste groups
30
33.7
20
17.9
50
24.9
Others
3
21.4
3
23.1
6
22.2
Highest education
No formal education
34
47.9
61
29.2
95
33.9
Less than primary
7
25.9
2
10.5
9
19.6
Primary completed
34
31.2
20
18.9
54
25.1
Secondary completed
16
27.6
8
14.5
24
21.2
High school or more
8
44.4
3
17.6
11
31.4
Occupation
Employed (government/nongovernment)
20
34.5
3
9.4
23
25.6
Self-employed
49
35.8
30
27.5
79
32.1
Student
3
14.3
3
7.9
6
10.2
Housewife
—
—
37
23.3
37
23.3
Unemployed (able to work)
16
36.4
12
26.7
28
31.5
Others
11
57.9
9
39.1
20
47.6
Migration (years)
<1
5
17.9
4
10.5
9
13.6
1–5
20
46.5
16
19.5
36
28.8
5–10
32
32.7
31
22.5
63
26.6
>10
42
36.8
43
29.3
85
32.6
Income status (NRs) (n=391)
1st quartile (≤5000)
15
27.3
18
19.8
33
22.6
2nd quartile (5001–7000)
13
40.6
10
28.6
23
34.3
3rd quartile (7001–10000)
21
41.2
10
23.8
31
33.3
4th quartile (≥10000)
21
35.6
8
30.8
29
34.1
Total
99
35.0
94
23.2
193
28.0
NRs: Nepalese rupees.Prevalence of hypertension according to various demographic variables is given in Table4. The males have a higher prevalence of hypertension (35.0%) than the females (23.2%). In fact, hypertension prevalence in the males of the slum is higher than the national average of 24.5% [19] and the urban males of Dharan town in the Eastern Nepal (22.7%) [28]. Furthermore, the prevalence rates are even higher than some of the slums of India such as Patna (18.79%) [24] and Kerala (31.0%) [25] but lower than the very high figure of Mumbai (71.2%) [18]. Though women in the slum had less hypertension compared to men, it was still high in comparison to the Nepalese average (18.1%) [19] and the Patna slum (14.48%) [24].Blood pressure status of the population according to extent of awareness, treatment, and control is presented in Figure5. About 63% of the study population had their blood pressure measured in the previous 12 months while 34.7% of the males and 29.8% of the females never had their blood pressure checked. Among the latter group, 49% of the males (i.e., 17% of the total male respondents) and 24.8% of the females (i.e., 7.4% of the total female respondents) were found to have high blood pressure during the survey—that is, undiagnosed hypertension. This prevalence of undiagnosed hypertension among both sexes in our study was about 20% which is higher than that in the Chennai slum study (15.4%) [23]. Likewise, our male population also had slightly higher prevalence of undiagnosed hypertension compared to the Dharan study (17% versus 14.4%) [28].Figure 5
Blood pressure status of the study population according to different sub-categories of awareness, treatment and control (N=689). Hypertensives included those who self-reported as having hypertension or those who had high blood pressure according to JNC-VII classification [21] during the time of survey. Black bars are for men and grey bars are for women respectively.In our study, 17.6% males and 15.8% females were known hypertensives—among whom 50% males (i.e., 8.8% of total male respondents) and 42.4% (i.e., 6.7% of total female respondents) were untreated—that is, diagnosed but untreated hypertension. Among the hypertensive population who were receiving treatment, 39.8% of the males and 40.7% of the females still had high blood pressure during the survey—that is, 3.5% of the total male respondents and 3.7% of the total female respondents had undertreated hypertension. The remaining hypertensives had their blood pressure under control—that is, 5.3% of the total male respondents or 30.1% of the male hypertensives, and 5.4% of the total female respondents or 34.2% of the female hypertensives had their blood pressure under control with or without drugs. The control rate among the known male hypertensive population was less in our study compared to the study in the Dharan (30.1% versus 58.5%) [28].


#### 3.2.7. General and Abdominal Obesity

According to their body mass index, almost one quarter of the respondents (26.0% of males and 23.9% of females) were overweight, while 5.8% were obese (Table5). In the males, prevalence of overweight was the highest in the 35–44 years group (37.7%) while females had the highest percent (28.4%) of overweight in the 25–34 years group (Table 5). Ethnicity-wise, relatively advantaged Janajatis had the highest prevalence of overweight and obesity, while the prevalence was less in those who had studied more than secondary education and who were currently students. In terms of abdominal obesity, females had higher waist circumference across all demographic parameters than males (Table 5).Table 5
Prevalence (%) of overweight and obesity, increased waist circumference, and increased waist hip ratio according to demographic variables (n=689). Increased waist circumference is waist measurements of ≥80 cm for females and ≥90 cm for males; increased waist hip ratio is ≥0.85 for females and ≥0.90 for males. Income status data is only for 391 persons who disclosed their financial information.
Body mass index
Increased waist circumference
Increased waist hip ratio
Overweight  (25–29.9 kg/m2)
Obesity(≥30 kg/m2)
Male
Female
Male
Female
Male
Female
Male
Female
Age (years)
15–24
8.0
9.4
5.3
0.9
10.7
17.1
49.3
54.7
25–34
28.6
28.4
0.0
3.7
14.3
45.9
50.0
59.6
35–44
37.7
25.3
11.5
8.0
32.8
51.7
63.9
71.3
45–54
31.2
24.5
8.3
9.4
27.1
56.6
60.4
69.8
55–64
17.2
26.1
3.4
4.3
37.9
43.5
58.6
34.8
≥65
28.6
17.6
7.1
5.9
35.7
52.9
71.4
52.9
Ethnicity
Hill Dalit
21.2
16.9
6.1
3.0
18.2
40.9
39.4
48.5
Disadvantaged Janajatis (Hill)
26.5
23.6
6.2
4.3
21.2
43.2
58.4
65.9
Relatively advantaged Janajatis
32.4
30.8
8.8
13.3
29.4
43.3
70.6
60.0
Upper caste groups
20.2
25.3
4.5
4.5
23.6
34.8
53.9
58.9
Others
21.4
36.4
7.1
0
28.6
38.5
64.3
53.8
Highest education
No formal education
21.1
25.8
9.9
6.2
26.8
49.8
59.2
60.8
Less than primary
37.0
21.1
7.4
0
18.5
47.4
59.3
57.9
Primary completed
22.0
27.7
4.6
5.7
16.5
31.1
54.1
62.3
Secondary completed
15.5
12.5
3.4
0
22.4
29.1
56.9
56.4
High school or more
61.1
17.6
5.6
0
55.6
11.8
55.6
58.8
Occupation
Employed (government/nongovernment)
29.3
25.9
5.2
6.2
27.6
34.4
53.4
59.4
Self-employed
28.5
21.6
8.0
7.3
23.4
38.5
60.6
56.9
Student
—
6.1
0
0
9.5
10.5
33.3
50.0
Housewife
—
29.5
—
3.1
—
50.9
—
68.6
Unemployed (able to work)
22.7
23.1
6.8
6.7
29.5
37.8
59.1
53.3
Others
15.8
22.2
0
4.3
10.5
39.1
52.6
52.2
Migration
<1
14.3
17.1
3.6
0
17.9
26.3
25.0
50.0
1–5
25.6
25.6
9.3
8.5
23.3
41.5
72.1
61.0
5–10
28.6
21.4
5.1
5.7
21.4
38.8
58.2
63.3
>10
22.8
25.4
6.1
3.2
25.4
44.9
57.0
59.9
Income status (NRs)
1st quartile (≤5000)
31.3
19.0
2.1
6.3
20.8
43.0
68.8
60.8
2nd quartile (5001–7000)
29.0
33.3
6.5
13.3
27.6
39.4
65.5
66.7
3rd quartile (7001–10000)
38.8
26.3
10.2
0.0
28.9
53.8
62.2
76.9
4th quartile (≥10000)
32.8
33.3
8.6
8.3
29.3
43.5
60.3
65.2
Total
26.0
23.9
6.4
5.3
25.8
45.1
63.5
67.3
NRs: Nepalese rupees.Unlike the other slum studies from India, the problem of high BMI is found to be more in males than in females in our study population. For example, the prevalence of overweight women was higher than men in the urban Myanmar (13.5% males and 22% females) [22] and in Kerala slums (men—29.7%, women—41%) [25]. Even in the Mumbai slum settlement, though 42.1% males were overweight compared to 11.7% females, 20% of women were obese in contrast to 0.8% males [18].


#### 3.2.8. Combination of Risk Factors

About forty percent of the males and thirty percent of the females had 3–5 risk factors (Table6). This is much higher than the 12.5% and 5.8% in the national representative data of Nepal [19]. In our study, the upper caste groups had the least occurrence of “3–5 risk factors” in both males and females. Those who were self-employed or were unemployed despite being able to work and those without formal education had more risk factors. Similarly, those who had lived for a longer time in the slum area were found to have more risk factors compared to those who had recently migrated. There was no definite relationship between income and number of risk factors.Table 6
Summary of combined risk factors in the study population, shown in percentage (N=689). The following risk factors were considered while counting the number of risk factors present: current daily smoker, less than 5 servings of fruits and vegetables per day, low physical activity (<600 MET-minutes), overweight or obese (BMI ≥ 25 kg/m²), raised BP (SBP ≥ 140 and/or DBP ≥ 90 mmHg, or currently on medication fro raised BP [20].
Male
Female
Total
Number of risk factors
Number of risk factors
Number of risk factors
0
1-2
3–5
0
1-2
3–5
0
1-2
3–5
Age (years)
15–24
2.7
81.3
16.0
6.0
82.9
11.1
4.7
82.3
13.0
25–34
1.8
57.1
41.1
2.8
67.0
30.3
2.4
63.6
33.9
35–44
0.0
49.2
50.8
1.1
58.6
40.2
0.7
54.7
44.6
45–54
0.0
43.8
56.3
0.0
62.3
37.7
0.0
53.5
46.5
55–64
0.0
55.2
44.8
0.0
39.1
60.9
0.0
48.1
51.9
≥65
0.0
28.6
71.4
0.0
47.1
52.9
0.0
38.7
61.3
Ethnicity
Hill Dalit
0.0
54.5
45.5
1.5
65.2
33.3
1.0
61.6
37.4
Disadvantaged Janajatis (Hill)
0.0
57.1
42.9
2.7
63.2
34.1
1.7
60.4
37.9
Relatively advantaged Janajatis
5.9
50.0
44.1
3.3
56.7
40.0
4.7
53.1
42.2
Upper caste groups
1.1
65.2
33.7
2.7
75.9
21.4
2.0
71.1
26.9
Others
0.0
57.1
42.9
7.7
69.2
23.1
3.7
63.0
33.3
Highest education
No formal education
0.0
49.3
50.7
2.4
57.9
39.7
1.8
55.7
42.5
Less than primary
0.0
55.6
44.4
5.3
73.7
21.1
2.2
63.0
34.8
Primary completed
0.9
63.3
35.8
1.9
72.6
25.5
1.4
67.9
30.7
Secondary completed
3.4
58.6
37.9
5.5
81.8
12.7
4.4
69.9
25.7
High school or more
0.0
61.1
38.9
0.0
82.4
17.6
0.0
71.4
28.6
Occupation
Employed (gov/nongov)
0.0
70.7
29.3
9.4
71.9
18.8
3.3
71.1
25.6
Self-employed
0.0
53.3
46.7
0.9
63.3
35.8
0.4
57.7
41.9
Student
9.5
76.2
14.3
10.5
81.6
7.9
10.2
79.7
10.2
Housewife
—
—
—
1.9
71.7
26.4
1.9
71.7
26.4
Unemployed (able to work)
0.0
52.3
47.7
0.0
53.3
46.7
0.0
52.8
47.2
Others
0.0
42.1
57.9
0.0
43.5
56.5
0.0
42.9
57.1
Migration (years)
<1
3.6
78.6
17.9
0.0
73.7
26.3
1.5
75.8
22.7
1–5
2.3
44.2
53.5
3.7
73.2
23.2
3.2
63.2
33.6
5–10
1.0
57.1
41.8
3.6
65.5
30.9
2.5
62.0
35.4
>10
0.0
58.8
41.2
2.0
62.6
35.4
1.1
60.9
37.9
Income status (NRs) (n=391)
1st quartile (≤5000)
0.0
60.0
40.0
4.4
67.0
28.6
2.7
64.4
32.9
2ndquartile (5001–7000)
0.0
43.8
56.3
2.9
62.9
34.3
1.5
53.7
44.8
3rd quartile (7001–10000)
0.0
56.9
43.1
0.0
73.8
26.2
0.0
64.5
35.5
4th quartile (≥10000)
0.0
57.6
42.4
3.8
53.8
42.3
1.2
56.5
42.4
Total
1.1
58.0
41.0
2.7
66.7
30.5
2.0
63.1
34.8
NRs: Nepalese rupees.


### 3.3. Strengths and Limitations of the Study

Our study explored NCD risk factors in an urban slum for the first time in Nepal. Through this study, we intended to measure the status of NCD risk factors in this population group, so that appropriate recommendations could be made to improve any prevalent risk factors. We followed the WHO STEPwise approach to surveillance which is a standardized method for collecting, analysing, and disseminating data in WHO member countries. Data collection was done by trained medical students under continuous supervision of medical faculty. During the course of data collection, any needy respondent was referred to the hospital for further management ensuring that the study incorporated socio-ethical values.Our study had certain limitations and weaknesses. There were disproportionately more women respondents (60%) in our study. Although there is no actual demographic data to compare if the male : female ratio is different in the population, unintentional oversampling of women is possible as the data collection was done during daytime when many men may have gone for work. We tried to minimize this by visiting the households in the morning time before the men left for work. Also, we did stratified analysis for men and women to eliminate this selection bias.Certain biological risk factors such as blood glucose and cholesterol were not included in the study due to logistic limitations. Because the sample size is moderate, only one slum area was studied and nonprobability sampling was opted, the results may not be generalized to other slum populations.


## 3.1. Demographic Characteristics

The demographic characteristics of the study population are described in Table1. Half of the respondents were aged less than 35 years. About 60% were women. About 40% had no formal education. About one third of the respondents were self-employed, while a quarter of the women were housewives. Thirteen percent of the respondents were unemployed though they were able to work. Majority (72.3%) of them had been staying there for more than five years. More than one third of the respondents had a monthly income of less than NRs. 5000 (1 US Dollar = approximately 85 Nepalese Rupees). Most of them had come to live in the squatter because of economic reasons, in search of a job, because of natural calamities like flood in their native places, or due to marriage. They had migrated mainly from other parts of Kathmandu district (22%), or from other nearby districts such as Kavrepalanchok (6%) and Sindhupalchowk (3%), or from Eastern Nepal districts such as Sindhuli (10%), Jhapa (7%), and Morang (5%).Table 1
Demographic characteristics of the study population (N=689). Classification of ethnic group is based on the National Central Bureau of Statistics [9]. Primary and secondary schooling indicate education up to grades 4 and 10, respectively. Other ethnic group includes Terai Dalit, disadvantaged Janajatis (Terai), disadvantaged non-Dalit Terai caste groups, and religious minorities. Other occupations include nonpaid, retired, and unemployed ones (unable to work).
Male
Female
Total
N
%
N
%
N
%
Age (years)
15–24
75
26.5
117
28.8
192
27.9
25–34
56
19.8
109
26.8
165
23.9
35–44
61
21.6
87
21.4
148
21.5
45–54
48
17.0
53
13.1
101
14.7
55–64
29
10.2
23
5.7
52
7.5
≥65
14
4.9
17
4.2
31
4.5
Ethnicity
Hill Dalit
33
11.7
66
16.3
99
14.4
Disadvantaged Janajatis (Hill)
113
39.9
185
45.6
298
43.3
Relatively advantaged Janajatis
34
12.0
30
7.4
64
9.3
Upper caste groups
89
31.4
112
27.6
201
29.2
Others
14
49
13
3.2
27
3.9
Highest education
No formal education
71
25.1
209
51.5
280
40.6
Less than primary
27
9.5
19
4.7
46
6.7
Primary completed
109
38.5
106
26.1
215
31.2
Secondary completed
58
20.5
55
13.5
113
16.4
High school or more
18
6.4
17
4.2
35
5.1
Occupation
Employed (government/nongovernment)
59
20.8
32
7.9
90
13.1
Self-employed
137
48.4
109
26.8
246
35.7
Student
21
7.4
38
9.4
59
8.6
Housewife
0
0.0
159
39.2
163
23.7
Unemployed (able to work)
46
16.3
45
11.1
89
12.9
Others
20
7.1
23
5.7
42
6.1
Migration (years)
<1
28
9.9
38
9.4
66
9.6
1–5
43
15.2
82
20.2
125
18.1
5–10
98
34.6
139
34.2
237
34.4
>10
114
40.3
147
36.2
261
37.9
Income status (NRs) (n=391)
1st quartile (≤5000)
55
27.9
91
46.9
146
37.3
2nd quartile (5001–7000)
32
16.2
35
18.0
67
17.1
3rd quartile (7001–10000)
51
25.9
42
21.6
93
23.8
4th quartile (≥10000)
59
29.9
26
13.4
85
21.7
NRs: Nepalese rupees.


## 3.2. Behavioural Risk Factors

The urban poor remain neglected part of urban population in terms of exploring their burden of behaviour risk factors and prevalence of NCDs among them. During the present study, it was found that the four important behavioural risk factors of NCDs—tobacco consumption, harmful use of alcohol, low fruit and vegetable consumption, and physical inactivity—are rampant in the slum population. Moreover, hypertension and obesity are also prevalent in the study population.


### 3.2.1. Tobacco Consumption

Age-sex distribution of smoking is presented in Figure2. Prevalence of smoking in men, women, and both sexes is 51.9%, 24.1%, and 35.6%. The overall (both sexes) prevalence of smoking in the present study is higher than the national data (23.8%) [19]. The smoking rate in the population is higher than that in the urban population of Myanmar (22.0%) [22] and the Chennai (20.2%) [23] and Patna (12.54%) [24] slums of India but lower than that in the Mumbai slum (66.7%) [18]. But compared to the urban poor from Kerala, India, there is not a large difference (37.3%) [25].Figure 2
Prevalence (%) of current smokers in the study population according to the age group (n=689). The categories are based on the WHO NCD risk factor steps survey manual [20]. Current smokers included those who responded “yes” to “Do you smoke?”. Dark and light grey bars are for men and women, respectively, while the black bars represent both sexes.By gender-wise stratification also, more men and women in our study smoke compared to the of the Nepal’s national data (31.2% and 15.5%) [19], the urban population of Myanmar (44.8 and 7.8%) [22], and the urban slum of Patna (21.15% and 5.91%) [24]. Comparatively, fewer women (12.2%) and more men (62.6%) in the Kerala slum smoked [25].Regarding smokeless tobacco, the overall consumption rate in our study is 24.8% (men: 38.5%, women: 15.3%). Age-sex distribution of smokeless tobacco is shown in Figure3. Compared to the national figures (18.6% for both sexes, 31.2% for men, and 4.6% for women), prevalence of consumption of smokeless tobacco is also higher in the study population [19]. The figures are however less compared to data from India and Myanmar. There are twice more consumers (55.4%) in the Mumbai slum [18] than in our study. Similarly, the prevalence in the urban Myanmar is also higher with 51.4% of the males and 16.1% of females consuming smokeless tobacco [22].Figure 3
Prevalence (%) of current Smokeless tobacco in the study population according to the age group (n=689). The categories are based on the WHO NCD risk factor steps survey manual [20]. Smokeless tobacco included products such as gutka, khaini, and surti. Dark and light grey bars are for men and women respectively, while the black bars represent both sexes.It is already an established fact that tobacco consumption is now universally more common among lower socioeconomic groups. For example, prevalence of tobacco chewing among women labourers in Dharan, Nepal (22%), was twice as much as among service class women (10%) [26].


### 3.2.2. Alcohol Consumption

Alcohol consumption in the slum is very high with 38.5% of the study population (58% of men and 24.9% of women) being current drinkers, that is, who had alcohol containing drink in the last 30 days. Among the current drinkers, one third consumes it daily (men: 34.6%, women: 31.1%). Comparatively, less people (31.2%) in the Mumbai slum drank [18]. Age-sex distribution of current drinkers is shown in Figure 4. Prevalences of alcohol consumption in the males and females of the study area were higher than the national averages for men (39.3%) and women (16.5%) [19]. Among males, the current drinking rate is higher than that in their counterparts from Patna (16.65%) [24] and Kerala (45.4%) [25]. The highest prevalence of current drinking was seen in the 35–44 years age group (72.1%). Similarly, more females in the Sinamangal slum drink more than the women of Patna slum (3.35%) [24] and in Kerala slum (1.0%) [25].Figure 4
Prevalence (%) of current drinkers in the study population according to the age group (n=689). The categories are based on the WHO NCD risk factor steps survey manual [20]. Current drinkers refer to the ones that consumed alcohol within the previous month. Dark and light grey bars are for men and women, respectively, while the black bars represent both sexes.


### 3.2.3. Fruits and Vegetable Consumption

The mean fruit consumption was less than two days per week with about 20% taking fruits less than once a week (Table2). Comparatively, vegetable consumption was better with average six days per week. A higher level of fruit and vegetable intake has been observed among the urban slum dwellers in Chennai where more than half population consumed vegetables 5 days and fruits 3 days per week [27]. The mean number of servings of fruit and/or vegetables per day in the present study was low—one serving per day for fruits and two servings per day for vegetables (Table 2). Myanmar urban slum population had better fruit and vegetable consumption [22].Table 2
Fruit and vegetable consumption in the study population (N=689). One serving of fruit means one medium-sized piece of fruit (banana, apple, etc.) or half cup of raw, cooked or canned fruit, or a half cup of juice from a fruit (not artificially flavoured). One serving of vegetable is one cup of raw, leafy green vegetables, (spinach, salad, etc.), one half cup of other vegetables, cooked or raw (tomatoes, pumpkin, beans, etc.), or half cup of vegetable juice.
Male
Female
Total
N
%
N
%
N
%
Fruit consumption
Days/week
0
60
21.2
79
19.5
139
20.2
1–3
181
64.0
272
67.0
453
65.7
4–6
29
10.2
42
10.3
71
10.3
7
13
4.6
13
3.2
26
3.8
Mean (SD)
1.96 (1.76)
1.94 (1.66)
1.95 (1.70)
Servings/day
0
60
21.2
79
19.5
139
20.2
1-2
204
72.1
312
76.8
516
74.9
≥3
19
6.7
15
3.7
34
4.9
Mean (SD)
1.08 (0.81)
1.03 (0.72)
1.05 (0.76)
Vegetable consumption
Days/week
0
4
1.4
9
2.2
13
1.9
1–3
26
9.2
38
9.4
64
9.3
4–6
82
29.0
104
25.4
186
27.0
7
171
60.4
255
62.8
426
61.8
Mean (SD)
6.01 (1.59)
5.95 (1.72)
5.98 (1.67)
Servings/day
0
4
1.4
9
2.2
13
1.9
1-2
250
88.3
354
87.2
604
87.7
≥3
29
10.2
43
10.6
72
10.4
Mean (SD)
1.80 (0.84)
1.73 (0.97)
1.76 (0.92)
Combined fruit and vegetable
Servings
<5
256
90.5
379
93.3
635
92.2
≥5
27
9.5
27
6.7
54
7.8
Mean (SD)
2.88 (1.32)
2.76 (1.33)
2.81 (1.33)
SD: standard deviation.More than five servings of fruit and vegetables are recommended for healthy living, but 90.5% men and 93.3% women consume less than the recommended five servings of fruit and vegetables daily in the study area. The situation in the slum is worse compared to the average Nepalese data according to which 60.5% of men and 63.5% of women have inadequate fruit and vegetable consumption [19]. Our study population fared worse in comparison to other studies in the similar settings as well [22, 25, 27]. Low consumption of fruits and vegetables in the urban poor can be due to lack of health knowledge, poor economic status of urban poor, and preference for junk food that put them at high risk of developing NCDs.


### 3.2.4. High Salt Containing Food Consumption

About 14% of the respondents are taking high salt containing food such as chips, instant noodles, and pickles every day. While a quarter of the respondents are taking such food less than once a week or 1-2 times in a week, about 17% almost never had them.


### 3.2.5. Level of Physical Inactivity

One third of the study population in the slum have low physical activity. This contrasts with the lower prevalence of physical inactivity across Nepal (5.9%) [19] or the Kerala slum (4.5%) [25] but may be considered less compared to the very high physical inactivity Chennai figure of 95% [23]. The data is however similar to the one from the Patna slum (33.64%) [24].Prevalence of low physical activity in the Sinamangal slum is higher in the females (39.4%) than in the males (21.9%). In fact, the women here are more inactive than those from Patna and Kerala slum settlements (37.65% and 4.9%, resp.) [24, 25]. At the same time, fewer males in the present study were physically inactive in contrast with 30.55% of males in the Patna slum [24]. Furthermore, low physical activity increased drastically from the average of 20% in the younger age group to around 60% in those aged 65 or more (Table 3). Those employed either in the government or nongovernmental organizations had less prevalence of low physical activity (10%). Whereas both men and women belonging to the lowest income group had similar prevalence of low physical activity, the women with high income tended to become less physically active compared to men of the same income group.Table 3
Prevalence (%) of low physical activity in the study population according to demographic variables (N=689). The physical activity section included questions on number of days and time spent on vigorous and/or moderate activities at work, travel to and from places, and recreational activities. The responses were then converted to MET minutes/week. The respondents were labelled as having vigorous activity or moderate activity if they achieved certain MET minutes as given in the WHO steps manual [20]. Someone was termed as having low physical activity (LBA) if s/he did not fulfil the criteria of having vigorous or moderate activity.
Male
Female
Total
N
%
N
%
N
%
Age (years)
15–24
15
20.0
47
40.2
62
32.3
25–34
12
21.4
41
37.6
53
32.1
35–44
12
19.7
28
32.3
40
27.0
45–54
9
18.8
18
34.0
27
26.7
55–64
6
20.7
15
65.2
21
40.2
≥65
8
57.1
11
64.7
19
61.3
Ethnicity
Hill Dalit
6
18.2
25
37.9
31
31.3
Disadvantaged Janajatis (Hill)
29
25.7
78
42.2
107
35.9
Relatively advantaged Janajatis
5
14.7
9
30.0
14
21.9
Upper caste groups
18
20.2
45
40.2
63
31.3
Others
4
28.6
3
23.1
7
25.9
Highest education
No formal education
22
31.0
83
39.7
105
37.5
Less than primary
4
14.8
4
21.1
8
17.4
Primary completed
20
18.3
50
47.2
70
32.6
Secondary completed
13
22.4
16
29.1
29
25.7
High school or more
3
16.7
7
41.2
10
28.6
Occupation
Employed (government/nongovernment)
3
5.2
6
18.8
9
10.0
Self-employed
26
19.0
36
33.0
62
25.2
Student
5
23.8
12
31.6
17
28.8
Housewife
—
—
64
40.3
64
40.3
Unemployed (able to work)
15
34.1
27
60.0
42
47.2
Others
13
68.4
15
65.2
28
66.7
Income status (NRs) (n=391)
1st quartile (≤5000)
13
23.6
20
22.0
33
22.6
2nd quartile (5001–7000)
4
12.5
12
34.3
16
23.9
3rd quartile (7001–10000)
6
11.8
19
45.2
25
26.9
4th quartile (≥10000)
7
11.9
11
42.3
18
21.2
Total
62
21.9
160
39.4
222
32.2
NRs: Nepalese rupees.


### 3.2.6. Blood Pressure Status

A respondent who was either a known hypertensive or was found to have high blood pressure during the survey was called having “hypertension.” Twenty-eight percent of the respondents are thus found to have hypertension (Table4). This prevalence is higher than the national average (21.5%) [19]. It is also high compared to the Indian slums of Chennai (22.8%) [23] and Patna (16.36%) [24]. On the other hand, the Kerala slum has a slightly higher (31%) prevalence of high blood pressure [25].Table 4
Prevalence of hypertension in the study population according to demographic variables (N=689). Hypertensives included those who self-reported as having hypertension or those who had high blood pressure according to JNC-VII classification [21] during the time of survey.
Male
Female
Total
N
%
N
%
N
%
Age (years)
15–24
11
14.7
7
6.0
18
9.4
25–34
12
21.4
17
15.6
29
17.6
35–44
26
42.6
34
39.1
60
40.5
45–54
24
50.0
18
34.0
42
41.6
55–64
18
62.1
11
47.8
29
55.8
≥65
8
57.1
7
41.2
15
48.4
Ethnicity
Hill Dalit
14
42.4
12
18.2
26
26.3
Disadvantaged Janajatis (Hill)
39
34.5
47
25.4
86
28.9
Relatively advantaged Janajatis
13
38.2
12
40.0
25
39.1
Upper caste groups
30
33.7
20
17.9
50
24.9
Others
3
21.4
3
23.1
6
22.2
Highest education
No formal education
34
47.9
61
29.2
95
33.9
Less than primary
7
25.9
2
10.5
9
19.6
Primary completed
34
31.2
20
18.9
54
25.1
Secondary completed
16
27.6
8
14.5
24
21.2
High school or more
8
44.4
3
17.6
11
31.4
Occupation
Employed (government/nongovernment)
20
34.5
3
9.4
23
25.6
Self-employed
49
35.8
30
27.5
79
32.1
Student
3
14.3
3
7.9
6
10.2
Housewife
—
—
37
23.3
37
23.3
Unemployed (able to work)
16
36.4
12
26.7
28
31.5
Others
11
57.9
9
39.1
20
47.6
Migration (years)
<1
5
17.9
4
10.5
9
13.6
1–5
20
46.5
16
19.5
36
28.8
5–10
32
32.7
31
22.5
63
26.6
>10
42
36.8
43
29.3
85
32.6
Income status (NRs) (n=391)
1st quartile (≤5000)
15
27.3
18
19.8
33
22.6
2nd quartile (5001–7000)
13
40.6
10
28.6
23
34.3
3rd quartile (7001–10000)
21
41.2
10
23.8
31
33.3
4th quartile (≥10000)
21
35.6
8
30.8
29
34.1
Total
99
35.0
94
23.2
193
28.0
NRs: Nepalese rupees.Prevalence of hypertension according to various demographic variables is given in Table4. The males have a higher prevalence of hypertension (35.0%) than the females (23.2%). In fact, hypertension prevalence in the males of the slum is higher than the national average of 24.5% [19] and the urban males of Dharan town in the Eastern Nepal (22.7%) [28]. Furthermore, the prevalence rates are even higher than some of the slums of India such as Patna (18.79%) [24] and Kerala (31.0%) [25] but lower than the very high figure of Mumbai (71.2%) [18]. Though women in the slum had less hypertension compared to men, it was still high in comparison to the Nepalese average (18.1%) [19] and the Patna slum (14.48%) [24].Blood pressure status of the population according to extent of awareness, treatment, and control is presented in Figure5. About 63% of the study population had their blood pressure measured in the previous 12 months while 34.7% of the males and 29.8% of the females never had their blood pressure checked. Among the latter group, 49% of the males (i.e., 17% of the total male respondents) and 24.8% of the females (i.e., 7.4% of the total female respondents) were found to have high blood pressure during the survey—that is, undiagnosed hypertension. This prevalence of undiagnosed hypertension among both sexes in our study was about 20% which is higher than that in the Chennai slum study (15.4%) [23]. Likewise, our male population also had slightly higher prevalence of undiagnosed hypertension compared to the Dharan study (17% versus 14.4%) [28].Figure 5
Blood pressure status of the study population according to different sub-categories of awareness, treatment and control (N=689). Hypertensives included those who self-reported as having hypertension or those who had high blood pressure according to JNC-VII classification [21] during the time of survey. Black bars are for men and grey bars are for women respectively.In our study, 17.6% males and 15.8% females were known hypertensives—among whom 50% males (i.e., 8.8% of total male respondents) and 42.4% (i.e., 6.7% of total female respondents) were untreated—that is, diagnosed but untreated hypertension. Among the hypertensive population who were receiving treatment, 39.8% of the males and 40.7% of the females still had high blood pressure during the survey—that is, 3.5% of the total male respondents and 3.7% of the total female respondents had undertreated hypertension. The remaining hypertensives had their blood pressure under control—that is, 5.3% of the total male respondents or 30.1% of the male hypertensives, and 5.4% of the total female respondents or 34.2% of the female hypertensives had their blood pressure under control with or without drugs. The control rate among the known male hypertensive population was less in our study compared to the study in the Dharan (30.1% versus 58.5%) [28].


### 3.2.7. General and Abdominal Obesity

According to their body mass index, almost one quarter of the respondents (26.0% of males and 23.9% of females) were overweight, while 5.8% were obese (Table5). In the males, prevalence of overweight was the highest in the 35–44 years group (37.7%) while females had the highest percent (28.4%) of overweight in the 25–34 years group (Table 5). Ethnicity-wise, relatively advantaged Janajatis had the highest prevalence of overweight and obesity, while the prevalence was less in those who had studied more than secondary education and who were currently students. In terms of abdominal obesity, females had higher waist circumference across all demographic parameters than males (Table 5).Table 5
Prevalence (%) of overweight and obesity, increased waist circumference, and increased waist hip ratio according to demographic variables (n=689). Increased waist circumference is waist measurements of ≥80 cm for females and ≥90 cm for males; increased waist hip ratio is ≥0.85 for females and ≥0.90 for males. Income status data is only for 391 persons who disclosed their financial information.
Body mass index
Increased waist circumference
Increased waist hip ratio
Overweight  (25–29.9 kg/m2)
Obesity(≥30 kg/m2)
Male
Female
Male
Female
Male
Female
Male
Female
Age (years)
15–24
8.0
9.4
5.3
0.9
10.7
17.1
49.3
54.7
25–34
28.6
28.4
0.0
3.7
14.3
45.9
50.0
59.6
35–44
37.7
25.3
11.5
8.0
32.8
51.7
63.9
71.3
45–54
31.2
24.5
8.3
9.4
27.1
56.6
60.4
69.8
55–64
17.2
26.1
3.4
4.3
37.9
43.5
58.6
34.8
≥65
28.6
17.6
7.1
5.9
35.7
52.9
71.4
52.9
Ethnicity
Hill Dalit
21.2
16.9
6.1
3.0
18.2
40.9
39.4
48.5
Disadvantaged Janajatis (Hill)
26.5
23.6
6.2
4.3
21.2
43.2
58.4
65.9
Relatively advantaged Janajatis
32.4
30.8
8.8
13.3
29.4
43.3
70.6
60.0
Upper caste groups
20.2
25.3
4.5
4.5
23.6
34.8
53.9
58.9
Others
21.4
36.4
7.1
0
28.6
38.5
64.3
53.8
Highest education
No formal education
21.1
25.8
9.9
6.2
26.8
49.8
59.2
60.8
Less than primary
37.0
21.1
7.4
0
18.5
47.4
59.3
57.9
Primary completed
22.0
27.7
4.6
5.7
16.5
31.1
54.1
62.3
Secondary completed
15.5
12.5
3.4
0
22.4
29.1
56.9
56.4
High school or more
61.1
17.6
5.6
0
55.6
11.8
55.6
58.8
Occupation
Employed (government/nongovernment)
29.3
25.9
5.2
6.2
27.6
34.4
53.4
59.4
Self-employed
28.5
21.6
8.0
7.3
23.4
38.5
60.6
56.9
Student
—
6.1
0
0
9.5
10.5
33.3
50.0
Housewife
—
29.5
—
3.1
—
50.9
—
68.6
Unemployed (able to work)
22.7
23.1
6.8
6.7
29.5
37.8
59.1
53.3
Others
15.8
22.2
0
4.3
10.5
39.1
52.6
52.2
Migration
<1
14.3
17.1
3.6
0
17.9
26.3
25.0
50.0
1–5
25.6
25.6
9.3
8.5
23.3
41.5
72.1
61.0
5–10
28.6
21.4
5.1
5.7
21.4
38.8
58.2
63.3
>10
22.8
25.4
6.1
3.2
25.4
44.9
57.0
59.9
Income status (NRs)
1st quartile (≤5000)
31.3
19.0
2.1
6.3
20.8
43.0
68.8
60.8
2nd quartile (5001–7000)
29.0
33.3
6.5
13.3
27.6
39.4
65.5
66.7
3rd quartile (7001–10000)
38.8
26.3
10.2
0.0
28.9
53.8
62.2
76.9
4th quartile (≥10000)
32.8
33.3
8.6
8.3
29.3
43.5
60.3
65.2
Total
26.0
23.9
6.4
5.3
25.8
45.1
63.5
67.3
NRs: Nepalese rupees.Unlike the other slum studies from India, the problem of high BMI is found to be more in males than in females in our study population. For example, the prevalence of overweight women was higher than men in the urban Myanmar (13.5% males and 22% females) [22] and in Kerala slums (men—29.7%, women—41%) [25]. Even in the Mumbai slum settlement, though 42.1% males were overweight compared to 11.7% females, 20% of women were obese in contrast to 0.8% males [18].


### 3.2.8. Combination of Risk Factors

About forty percent of the males and thirty percent of the females had 3–5 risk factors (Table6). This is much higher than the 12.5% and 5.8% in the national representative data of Nepal [19]. In our study, the upper caste groups had the least occurrence of “3–5 risk factors” in both males and females. Those who were self-employed or were unemployed despite being able to work and those without formal education had more risk factors. Similarly, those who had lived for a longer time in the slum area were found to have more risk factors compared to those who had recently migrated. There was no definite relationship between income and number of risk factors.Table 6
Summary of combined risk factors in the study population, shown in percentage (N=689). The following risk factors were considered while counting the number of risk factors present: current daily smoker, less than 5 servings of fruits and vegetables per day, low physical activity (<600 MET-minutes), overweight or obese (BMI ≥ 25 kg/m²), raised BP (SBP ≥ 140 and/or DBP ≥ 90 mmHg, or currently on medication fro raised BP [20].
Male
Female
Total
Number of risk factors
Number of risk factors
Number of risk factors
0
1-2
3–5
0
1-2
3–5
0
1-2
3–5
Age (years)
15–24
2.7
81.3
16.0
6.0
82.9
11.1
4.7
82.3
13.0
25–34
1.8
57.1
41.1
2.8
67.0
30.3
2.4
63.6
33.9
35–44
0.0
49.2
50.8
1.1
58.6
40.2
0.7
54.7
44.6
45–54
0.0
43.8
56.3
0.0
62.3
37.7
0.0
53.5
46.5
55–64
0.0
55.2
44.8
0.0
39.1
60.9
0.0
48.1
51.9
≥65
0.0
28.6
71.4
0.0
47.1
52.9
0.0
38.7
61.3
Ethnicity
Hill Dalit
0.0
54.5
45.5
1.5
65.2
33.3
1.0
61.6
37.4
Disadvantaged Janajatis (Hill)
0.0
57.1
42.9
2.7
63.2
34.1
1.7
60.4
37.9
Relatively advantaged Janajatis
5.9
50.0
44.1
3.3
56.7
40.0
4.7
53.1
42.2
Upper caste groups
1.1
65.2
33.7
2.7
75.9
21.4
2.0
71.1
26.9
Others
0.0
57.1
42.9
7.7
69.2
23.1
3.7
63.0
33.3
Highest education
No formal education
0.0
49.3
50.7
2.4
57.9
39.7
1.8
55.7
42.5
Less than primary
0.0
55.6
44.4
5.3
73.7
21.1
2.2
63.0
34.8
Primary completed
0.9
63.3
35.8
1.9
72.6
25.5
1.4
67.9
30.7
Secondary completed
3.4
58.6
37.9
5.5
81.8
12.7
4.4
69.9
25.7
High school or more
0.0
61.1
38.9
0.0
82.4
17.6
0.0
71.4
28.6
Occupation
Employed (gov/nongov)
0.0
70.7
29.3
9.4
71.9
18.8
3.3
71.1
25.6
Self-employed
0.0
53.3
46.7
0.9
63.3
35.8
0.4
57.7
41.9
Student
9.5
76.2
14.3
10.5
81.6
7.9
10.2
79.7
10.2
Housewife
—
—
—
1.9
71.7
26.4
1.9
71.7
26.4
Unemployed (able to work)
0.0
52.3
47.7
0.0
53.3
46.7
0.0
52.8
47.2
Others
0.0
42.1
57.9
0.0
43.5
56.5
0.0
42.9
57.1
Migration (years)
<1
3.6
78.6
17.9
0.0
73.7
26.3
1.5
75.8
22.7
1–5
2.3
44.2
53.5
3.7
73.2
23.2
3.2
63.2
33.6
5–10
1.0
57.1
41.8
3.6
65.5
30.9
2.5
62.0
35.4
>10
0.0
58.8
41.2
2.0
62.6
35.4
1.1
60.9
37.9
Income status (NRs) (n=391)
1st quartile (≤5000)
0.0
60.0
40.0
4.4
67.0
28.6
2.7
64.4
32.9
2ndquartile (5001–7000)
0.0
43.8
56.3
2.9
62.9
34.3
1.5
53.7
44.8
3rd quartile (7001–10000)
0.0
56.9
43.1
0.0
73.8
26.2
0.0
64.5
35.5
4th quartile (≥10000)
0.0
57.6
42.4
3.8
53.8
42.3
1.2
56.5
42.4
Total
1.1
58.0
41.0
2.7
66.7
30.5
2.0
63.1
34.8
NRs: Nepalese rupees.


## 3.2.1. Tobacco Consumption

Age-sex distribution of smoking is presented in Figure2. Prevalence of smoking in men, women, and both sexes is 51.9%, 24.1%, and 35.6%. The overall (both sexes) prevalence of smoking in the present study is higher than the national data (23.8%) [19]. The smoking rate in the population is higher than that in the urban population of Myanmar (22.0%) [22] and the Chennai (20.2%) [23] and Patna (12.54%) [24] slums of India but lower than that in the Mumbai slum (66.7%) [18]. But compared to the urban poor from Kerala, India, there is not a large difference (37.3%) [25].Figure 2
Prevalence (%) of current smokers in the study population according to the age group (n=689). The categories are based on the WHO NCD risk factor steps survey manual [20]. Current smokers included those who responded “yes” to “Do you smoke?”. Dark and light grey bars are for men and women, respectively, while the black bars represent both sexes.By gender-wise stratification also, more men and women in our study smoke compared to the of the Nepal’s national data (31.2% and 15.5%) [19], the urban population of Myanmar (44.8 and 7.8%) [22], and the urban slum of Patna (21.15% and 5.91%) [24]. Comparatively, fewer women (12.2%) and more men (62.6%) in the Kerala slum smoked [25].Regarding smokeless tobacco, the overall consumption rate in our study is 24.8% (men: 38.5%, women: 15.3%). Age-sex distribution of smokeless tobacco is shown in Figure3. Compared to the national figures (18.6% for both sexes, 31.2% for men, and 4.6% for women), prevalence of consumption of smokeless tobacco is also higher in the study population [19]. The figures are however less compared to data from India and Myanmar. There are twice more consumers (55.4%) in the Mumbai slum [18] than in our study. Similarly, the prevalence in the urban Myanmar is also higher with 51.4% of the males and 16.1% of females consuming smokeless tobacco [22].Figure 3
Prevalence (%) of current Smokeless tobacco in the study population according to the age group (n=689). The categories are based on the WHO NCD risk factor steps survey manual [20]. Smokeless tobacco included products such as gutka, khaini, and surti. Dark and light grey bars are for men and women respectively, while the black bars represent both sexes.It is already an established fact that tobacco consumption is now universally more common among lower socioeconomic groups. For example, prevalence of tobacco chewing among women labourers in Dharan, Nepal (22%), was twice as much as among service class women (10%) [26].


## 3.2.2. Alcohol Consumption

Alcohol consumption in the slum is very high with 38.5% of the study population (58% of men and 24.9% of women) being current drinkers, that is, who had alcohol containing drink in the last 30 days. Among the current drinkers, one third consumes it daily (men: 34.6%, women: 31.1%). Comparatively, less people (31.2%) in the Mumbai slum drank [18]. Age-sex distribution of current drinkers is shown in Figure 4. Prevalences of alcohol consumption in the males and females of the study area were higher than the national averages for men (39.3%) and women (16.5%) [19]. Among males, the current drinking rate is higher than that in their counterparts from Patna (16.65%) [24] and Kerala (45.4%) [25]. The highest prevalence of current drinking was seen in the 35–44 years age group (72.1%). Similarly, more females in the Sinamangal slum drink more than the women of Patna slum (3.35%) [24] and in Kerala slum (1.0%) [25].Figure 4
Prevalence (%) of current drinkers in the study population according to the age group (n=689). The categories are based on the WHO NCD risk factor steps survey manual [20]. Current drinkers refer to the ones that consumed alcohol within the previous month. Dark and light grey bars are for men and women, respectively, while the black bars represent both sexes.


## 3.2.3. Fruits and Vegetable Consumption

The mean fruit consumption was less than two days per week with about 20% taking fruits less than once a week (Table2). Comparatively, vegetable consumption was better with average six days per week. A higher level of fruit and vegetable intake has been observed among the urban slum dwellers in Chennai where more than half population consumed vegetables 5 days and fruits 3 days per week [27]. The mean number of servings of fruit and/or vegetables per day in the present study was low—one serving per day for fruits and two servings per day for vegetables (Table 2). Myanmar urban slum population had better fruit and vegetable consumption [22].Table 2
Fruit and vegetable consumption in the study population (N=689). One serving of fruit means one medium-sized piece of fruit (banana, apple, etc.) or half cup of raw, cooked or canned fruit, or a half cup of juice from a fruit (not artificially flavoured). One serving of vegetable is one cup of raw, leafy green vegetables, (spinach, salad, etc.), one half cup of other vegetables, cooked or raw (tomatoes, pumpkin, beans, etc.), or half cup of vegetable juice.
Male
Female
Total
N
%
N
%
N
%
Fruit consumption
Days/week
0
60
21.2
79
19.5
139
20.2
1–3
181
64.0
272
67.0
453
65.7
4–6
29
10.2
42
10.3
71
10.3
7
13
4.6
13
3.2
26
3.8
Mean (SD)
1.96 (1.76)
1.94 (1.66)
1.95 (1.70)
Servings/day
0
60
21.2
79
19.5
139
20.2
1-2
204
72.1
312
76.8
516
74.9
≥3
19
6.7
15
3.7
34
4.9
Mean (SD)
1.08 (0.81)
1.03 (0.72)
1.05 (0.76)
Vegetable consumption
Days/week
0
4
1.4
9
2.2
13
1.9
1–3
26
9.2
38
9.4
64
9.3
4–6
82
29.0
104
25.4
186
27.0
7
171
60.4
255
62.8
426
61.8
Mean (SD)
6.01 (1.59)
5.95 (1.72)
5.98 (1.67)
Servings/day
0
4
1.4
9
2.2
13
1.9
1-2
250
88.3
354
87.2
604
87.7
≥3
29
10.2
43
10.6
72
10.4
Mean (SD)
1.80 (0.84)
1.73 (0.97)
1.76 (0.92)
Combined fruit and vegetable
Servings
<5
256
90.5
379
93.3
635
92.2
≥5
27
9.5
27
6.7
54
7.8
Mean (SD)
2.88 (1.32)
2.76 (1.33)
2.81 (1.33)
SD: standard deviation.More than five servings of fruit and vegetables are recommended for healthy living, but 90.5% men and 93.3% women consume less than the recommended five servings of fruit and vegetables daily in the study area. The situation in the slum is worse compared to the average Nepalese data according to which 60.5% of men and 63.5% of women have inadequate fruit and vegetable consumption [19]. Our study population fared worse in comparison to other studies in the similar settings as well [22, 25, 27]. Low consumption of fruits and vegetables in the urban poor can be due to lack of health knowledge, poor economic status of urban poor, and preference for junk food that put them at high risk of developing NCDs.


## 3.2.4. High Salt Containing Food Consumption

About 14% of the respondents are taking high salt containing food such as chips, instant noodles, and pickles every day. While a quarter of the respondents are taking such food less than once a week or 1-2 times in a week, about 17% almost never had them.


## 3.2.5. Level of Physical Inactivity

One third of the study population in the slum have low physical activity. This contrasts with the lower prevalence of physical inactivity across Nepal (5.9%) [19] or the Kerala slum (4.5%) [25] but may be considered less compared to the very high physical inactivity Chennai figure of 95% [23]. The data is however similar to the one from the Patna slum (33.64%) [24].Prevalence of low physical activity in the Sinamangal slum is higher in the females (39.4%) than in the males (21.9%). In fact, the women here are more inactive than those from Patna and Kerala slum settlements (37.65% and 4.9%, resp.) [24, 25]. At the same time, fewer males in the present study were physically inactive in contrast with 30.55% of males in the Patna slum [24]. Furthermore, low physical activity increased drastically from the average of 20% in the younger age group to around 60% in those aged 65 or more (Table 3). Those employed either in the government or nongovernmental organizations had less prevalence of low physical activity (10%). Whereas both men and women belonging to the lowest income group had similar prevalence of low physical activity, the women with high income tended to become less physically active compared to men of the same income group.Table 3
Prevalence (%) of low physical activity in the study population according to demographic variables (N=689). The physical activity section included questions on number of days and time spent on vigorous and/or moderate activities at work, travel to and from places, and recreational activities. The responses were then converted to MET minutes/week. The respondents were labelled as having vigorous activity or moderate activity if they achieved certain MET minutes as given in the WHO steps manual [20]. Someone was termed as having low physical activity (LBA) if s/he did not fulfil the criteria of having vigorous or moderate activity.
Male
Female
Total
N
%
N
%
N
%
Age (years)
15–24
15
20.0
47
40.2
62
32.3
25–34
12
21.4
41
37.6
53
32.1
35–44
12
19.7
28
32.3
40
27.0
45–54
9
18.8
18
34.0
27
26.7
55–64
6
20.7
15
65.2
21
40.2
≥65
8
57.1
11
64.7
19
61.3
Ethnicity
Hill Dalit
6
18.2
25
37.9
31
31.3
Disadvantaged Janajatis (Hill)
29
25.7
78
42.2
107
35.9
Relatively advantaged Janajatis
5
14.7
9
30.0
14
21.9
Upper caste groups
18
20.2
45
40.2
63
31.3
Others
4
28.6
3
23.1
7
25.9
Highest education
No formal education
22
31.0
83
39.7
105
37.5
Less than primary
4
14.8
4
21.1
8
17.4
Primary completed
20
18.3
50
47.2
70
32.6
Secondary completed
13
22.4
16
29.1
29
25.7
High school or more
3
16.7
7
41.2
10
28.6
Occupation
Employed (government/nongovernment)
3
5.2
6
18.8
9
10.0
Self-employed
26
19.0
36
33.0
62
25.2
Student
5
23.8
12
31.6
17
28.8
Housewife
—
—
64
40.3
64
40.3
Unemployed (able to work)
15
34.1
27
60.0
42
47.2
Others
13
68.4
15
65.2
28
66.7
Income status (NRs) (n=391)
1st quartile (≤5000)
13
23.6
20
22.0
33
22.6
2nd quartile (5001–7000)
4
12.5
12
34.3
16
23.9
3rd quartile (7001–10000)
6
11.8
19
45.2
25
26.9
4th quartile (≥10000)
7
11.9
11
42.3
18
21.2
Total
62
21.9
160
39.4
222
32.2
NRs: Nepalese rupees.


## 3.2.6. Blood Pressure Status

A respondent who was either a known hypertensive or was found to have high blood pressure during the survey was called having “hypertension.” Twenty-eight percent of the respondents are thus found to have hypertension (Table4). This prevalence is higher than the national average (21.5%) [19]. It is also high compared to the Indian slums of Chennai (22.8%) [23] and Patna (16.36%) [24]. On the other hand, the Kerala slum has a slightly higher (31%) prevalence of high blood pressure [25].Table 4
Prevalence of hypertension in the study population according to demographic variables (N=689). Hypertensives included those who self-reported as having hypertension or those who had high blood pressure according to JNC-VII classification [21] during the time of survey.
Male
Female
Total
N
%
N
%
N
%
Age (years)
15–24
11
14.7
7
6.0
18
9.4
25–34
12
21.4
17
15.6
29
17.6
35–44
26
42.6
34
39.1
60
40.5
45–54
24
50.0
18
34.0
42
41.6
55–64
18
62.1
11
47.8
29
55.8
≥65
8
57.1
7
41.2
15
48.4
Ethnicity
Hill Dalit
14
42.4
12
18.2
26
26.3
Disadvantaged Janajatis (Hill)
39
34.5
47
25.4
86
28.9
Relatively advantaged Janajatis
13
38.2
12
40.0
25
39.1
Upper caste groups
30
33.7
20
17.9
50
24.9
Others
3
21.4
3
23.1
6
22.2
Highest education
No formal education
34
47.9
61
29.2
95
33.9
Less than primary
7
25.9
2
10.5
9
19.6
Primary completed
34
31.2
20
18.9
54
25.1
Secondary completed
16
27.6
8
14.5
24
21.2
High school or more
8
44.4
3
17.6
11
31.4
Occupation
Employed (government/nongovernment)
20
34.5
3
9.4
23
25.6
Self-employed
49
35.8
30
27.5
79
32.1
Student
3
14.3
3
7.9
6
10.2
Housewife
—
—
37
23.3
37
23.3
Unemployed (able to work)
16
36.4
12
26.7
28
31.5
Others
11
57.9
9
39.1
20
47.6
Migration (years)
<1
5
17.9
4
10.5
9
13.6
1–5
20
46.5
16
19.5
36
28.8
5–10
32
32.7
31
22.5
63
26.6
>10
42
36.8
43
29.3
85
32.6
Income status (NRs) (n=391)
1st quartile (≤5000)
15
27.3
18
19.8
33
22.6
2nd quartile (5001–7000)
13
40.6
10
28.6
23
34.3
3rd quartile (7001–10000)
21
41.2
10
23.8
31
33.3
4th quartile (≥10000)
21
35.6
8
30.8
29
34.1
Total
99
35.0
94
23.2
193
28.0
NRs: Nepalese rupees.Prevalence of hypertension according to various demographic variables is given in Table4. The males have a higher prevalence of hypertension (35.0%) than the females (23.2%). In fact, hypertension prevalence in the males of the slum is higher than the national average of 24.5% [19] and the urban males of Dharan town in the Eastern Nepal (22.7%) [28]. Furthermore, the prevalence rates are even higher than some of the slums of India such as Patna (18.79%) [24] and Kerala (31.0%) [25] but lower than the very high figure of Mumbai (71.2%) [18]. Though women in the slum had less hypertension compared to men, it was still high in comparison to the Nepalese average (18.1%) [19] and the Patna slum (14.48%) [24].Blood pressure status of the population according to extent of awareness, treatment, and control is presented in Figure5. About 63% of the study population had their blood pressure measured in the previous 12 months while 34.7% of the males and 29.8% of the females never had their blood pressure checked. Among the latter group, 49% of the males (i.e., 17% of the total male respondents) and 24.8% of the females (i.e., 7.4% of the total female respondents) were found to have high blood pressure during the survey—that is, undiagnosed hypertension. This prevalence of undiagnosed hypertension among both sexes in our study was about 20% which is higher than that in the Chennai slum study (15.4%) [23]. Likewise, our male population also had slightly higher prevalence of undiagnosed hypertension compared to the Dharan study (17% versus 14.4%) [28].Figure 5
Blood pressure status of the study population according to different sub-categories of awareness, treatment and control (N=689). Hypertensives included those who self-reported as having hypertension or those who had high blood pressure according to JNC-VII classification [21] during the time of survey. Black bars are for men and grey bars are for women respectively.In our study, 17.6% males and 15.8% females were known hypertensives—among whom 50% males (i.e., 8.8% of total male respondents) and 42.4% (i.e., 6.7% of total female respondents) were untreated—that is, diagnosed but untreated hypertension. Among the hypertensive population who were receiving treatment, 39.8% of the males and 40.7% of the females still had high blood pressure during the survey—that is, 3.5% of the total male respondents and 3.7% of the total female respondents had undertreated hypertension. The remaining hypertensives had their blood pressure under control—that is, 5.3% of the total male respondents or 30.1% of the male hypertensives, and 5.4% of the total female respondents or 34.2% of the female hypertensives had their blood pressure under control with or without drugs. The control rate among the known male hypertensive population was less in our study compared to the study in the Dharan (30.1% versus 58.5%) [28].


## 3.2.7. General and Abdominal Obesity

According to their body mass index, almost one quarter of the respondents (26.0% of males and 23.9% of females) were overweight, while 5.8% were obese (Table5). In the males, prevalence of overweight was the highest in the 35–44 years group (37.7%) while females had the highest percent (28.4%) of overweight in the 25–34 years group (Table 5). Ethnicity-wise, relatively advantaged Janajatis had the highest prevalence of overweight and obesity, while the prevalence was less in those who had studied more than secondary education and who were currently students. In terms of abdominal obesity, females had higher waist circumference across all demographic parameters than males (Table 5).Table 5
Prevalence (%) of overweight and obesity, increased waist circumference, and increased waist hip ratio according to demographic variables (n=689). Increased waist circumference is waist measurements of ≥80 cm for females and ≥90 cm for males; increased waist hip ratio is ≥0.85 for females and ≥0.90 for males. Income status data is only for 391 persons who disclosed their financial information.
Body mass index
Increased waist circumference
Increased waist hip ratio
Overweight  (25–29.9 kg/m2)
Obesity(≥30 kg/m2)
Male
Female
Male
Female
Male
Female
Male
Female
Age (years)
15–24
8.0
9.4
5.3
0.9
10.7
17.1
49.3
54.7
25–34
28.6
28.4
0.0
3.7
14.3
45.9
50.0
59.6
35–44
37.7
25.3
11.5
8.0
32.8
51.7
63.9
71.3
45–54
31.2
24.5
8.3
9.4
27.1
56.6
60.4
69.8
55–64
17.2
26.1
3.4
4.3
37.9
43.5
58.6
34.8
≥65
28.6
17.6
7.1
5.9
35.7
52.9
71.4
52.9
Ethnicity
Hill Dalit
21.2
16.9
6.1
3.0
18.2
40.9
39.4
48.5
Disadvantaged Janajatis (Hill)
26.5
23.6
6.2
4.3
21.2
43.2
58.4
65.9
Relatively advantaged Janajatis
32.4
30.8
8.8
13.3
29.4
43.3
70.6
60.0
Upper caste groups
20.2
25.3
4.5
4.5
23.6
34.8
53.9
58.9
Others
21.4
36.4
7.1
0
28.6
38.5
64.3
53.8
Highest education
No formal education
21.1
25.8
9.9
6.2
26.8
49.8
59.2
60.8
Less than primary
37.0
21.1
7.4
0
18.5
47.4
59.3
57.9
Primary completed
22.0
27.7
4.6
5.7
16.5
31.1
54.1
62.3
Secondary completed
15.5
12.5
3.4
0
22.4
29.1
56.9
56.4
High school or more
61.1
17.6
5.6
0
55.6
11.8
55.6
58.8
Occupation
Employed (government/nongovernment)
29.3
25.9
5.2
6.2
27.6
34.4
53.4
59.4
Self-employed
28.5
21.6
8.0
7.3
23.4
38.5
60.6
56.9
Student
—
6.1
0
0
9.5
10.5
33.3
50.0
Housewife
—
29.5
—
3.1
—
50.9
—
68.6
Unemployed (able to work)
22.7
23.1
6.8
6.7
29.5
37.8
59.1
53.3
Others
15.8
22.2
0
4.3
10.5
39.1
52.6
52.2
Migration
<1
14.3
17.1
3.6
0
17.9
26.3
25.0
50.0
1–5
25.6
25.6
9.3
8.5
23.3
41.5
72.1
61.0
5–10
28.6
21.4
5.1
5.7
21.4
38.8
58.2
63.3
>10
22.8
25.4
6.1
3.2
25.4
44.9
57.0
59.9
Income status (NRs)
1st quartile (≤5000)
31.3
19.0
2.1
6.3
20.8
43.0
68.8
60.8
2nd quartile (5001–7000)
29.0
33.3
6.5
13.3
27.6
39.4
65.5
66.7
3rd quartile (7001–10000)
38.8
26.3
10.2
0.0
28.9
53.8
62.2
76.9
4th quartile (≥10000)
32.8
33.3
8.6
8.3
29.3
43.5
60.3
65.2
Total
26.0
23.9
6.4
5.3
25.8
45.1
63.5
67.3
NRs: Nepalese rupees.Unlike the other slum studies from India, the problem of high BMI is found to be more in males than in females in our study population. For example, the prevalence of overweight women was higher than men in the urban Myanmar (13.5% males and 22% females) [22] and in Kerala slums (men—29.7%, women—41%) [25]. Even in the Mumbai slum settlement, though 42.1% males were overweight compared to 11.7% females, 20% of women were obese in contrast to 0.8% males [18].


## 3.2.8. Combination of Risk Factors

About forty percent of the males and thirty percent of the females had 3–5 risk factors (Table6). This is much higher than the 12.5% and 5.8% in the national representative data of Nepal [19]. In our study, the upper caste groups had the least occurrence of “3–5 risk factors” in both males and females. Those who were self-employed or were unemployed despite being able to work and those without formal education had more risk factors. Similarly, those who had lived for a longer time in the slum area were found to have more risk factors compared to those who had recently migrated. There was no definite relationship between income and number of risk factors.Table 6
Summary of combined risk factors in the study population, shown in percentage (N=689). The following risk factors were considered while counting the number of risk factors present: current daily smoker, less than 5 servings of fruits and vegetables per day, low physical activity (<600 MET-minutes), overweight or obese (BMI ≥ 25 kg/m²), raised BP (SBP ≥ 140 and/or DBP ≥ 90 mmHg, or currently on medication fro raised BP [20].
Male
Female
Total
Number of risk factors
Number of risk factors
Number of risk factors
0
1-2
3–5
0
1-2
3–5
0
1-2
3–5
Age (years)
15–24
2.7
81.3
16.0
6.0
82.9
11.1
4.7
82.3
13.0
25–34
1.8
57.1
41.1
2.8
67.0
30.3
2.4
63.6
33.9
35–44
0.0
49.2
50.8
1.1
58.6
40.2
0.7
54.7
44.6
45–54
0.0
43.8
56.3
0.0
62.3
37.7
0.0
53.5
46.5
55–64
0.0
55.2
44.8
0.0
39.1
60.9
0.0
48.1
51.9
≥65
0.0
28.6
71.4
0.0
47.1
52.9
0.0
38.7
61.3
Ethnicity
Hill Dalit
0.0
54.5
45.5
1.5
65.2
33.3
1.0
61.6
37.4
Disadvantaged Janajatis (Hill)
0.0
57.1
42.9
2.7
63.2
34.1
1.7
60.4
37.9
Relatively advantaged Janajatis
5.9
50.0
44.1
3.3
56.7
40.0
4.7
53.1
42.2
Upper caste groups
1.1
65.2
33.7
2.7
75.9
21.4
2.0
71.1
26.9
Others
0.0
57.1
42.9
7.7
69.2
23.1
3.7
63.0
33.3
Highest education
No formal education
0.0
49.3
50.7
2.4
57.9
39.7
1.8
55.7
42.5
Less than primary
0.0
55.6
44.4
5.3
73.7
21.1
2.2
63.0
34.8
Primary completed
0.9
63.3
35.8
1.9
72.6
25.5
1.4
67.9
30.7
Secondary completed
3.4
58.6
37.9
5.5
81.8
12.7
4.4
69.9
25.7
High school or more
0.0
61.1
38.9
0.0
82.4
17.6
0.0
71.4
28.6
Occupation
Employed (gov/nongov)
0.0
70.7
29.3
9.4
71.9
18.8
3.3
71.1
25.6
Self-employed
0.0
53.3
46.7
0.9
63.3
35.8
0.4
57.7
41.9
Student
9.5
76.2
14.3
10.5
81.6
7.9
10.2
79.7
10.2
Housewife
—
—
—
1.9
71.7
26.4
1.9
71.7
26.4
Unemployed (able to work)
0.0
52.3
47.7
0.0
53.3
46.7
0.0
52.8
47.2
Others
0.0
42.1
57.9
0.0
43.5
56.5
0.0
42.9
57.1
Migration (years)
<1
3.6
78.6
17.9
0.0
73.7
26.3
1.5
75.8
22.7
1–5
2.3
44.2
53.5
3.7
73.2
23.2
3.2
63.2
33.6
5–10
1.0
57.1
41.8
3.6
65.5
30.9
2.5
62.0
35.4
>10
0.0
58.8
41.2
2.0
62.6
35.4
1.1
60.9
37.9
Income status (NRs) (n=391)
1st quartile (≤5000)
0.0
60.0
40.0
4.4
67.0
28.6
2.7
64.4
32.9
2ndquartile (5001–7000)
0.0
43.8
56.3
2.9
62.9
34.3
1.5
53.7
44.8
3rd quartile (7001–10000)
0.0
56.9
43.1
0.0
73.8
26.2
0.0
64.5
35.5
4th quartile (≥10000)
0.0
57.6
42.4
3.8
53.8
42.3
1.2
56.5
42.4
Total
1.1
58.0
41.0
2.7
66.7
30.5
2.0
63.1
34.8
NRs: Nepalese rupees.


## 3.3. Strengths and Limitations of the Study

Our study explored NCD risk factors in an urban slum for the first time in Nepal. Through this study, we intended to measure the status of NCD risk factors in this population group, so that appropriate recommendations could be made to improve any prevalent risk factors. We followed the WHO STEPwise approach to surveillance which is a standardized method for collecting, analysing, and disseminating data in WHO member countries. Data collection was done by trained medical students under continuous supervision of medical faculty. During the course of data collection, any needy respondent was referred to the hospital for further management ensuring that the study incorporated socio-ethical values.Our study had certain limitations and weaknesses. There were disproportionately more women respondents (60%) in our study. Although there is no actual demographic data to compare if the male : female ratio is different in the population, unintentional oversampling of women is possible as the data collection was done during daytime when many men may have gone for work. We tried to minimize this by visiting the households in the morning time before the men left for work. Also, we did stratified analysis for men and women to eliminate this selection bias.Certain biological risk factors such as blood glucose and cholesterol were not included in the study due to logistic limitations. Because the sample size is moderate, only one slum area was studied and nonprobability sampling was opted, the results may not be generalized to other slum populations.


## 4. Conclusions

NCDs are emerging as a public health problem in Nepal. Hence, surveillance of its risk factors in different population subgroups, including the urban poor, is essential for future policy making and planning by the government and other stakeholders. Our study has shown that the behavioural risk factors of NCDs are very common in the urban poor population of Kathmandu. In fact, most of the risk factors are present in at least a quarter of the slum population: smokeless tobacco, high BMI and waist hip ratio, and hypertension. Other risk factors such as tobacco smoking, current alcohol drinking, and low physical activity are found in one third of them. Fruit and vegetable consumption is very low with 92.2% of the population not consuming 5 or more servings. One third of the population have 3–5 risk factors.Health system of Nepal is based on primary health care with focus on rural health and with emphasis on communicable diseases, maternal, and child health issues including nutrition. Thus, as both urban poor population and NCDs have remained neglected by the government and other stakeholders, the urban poor are having a double jeopardy when it comes to NCDs. With such a huge burden of risk factors, even the burden of NCDs can be expected to be high among the urban poor. Tragically, though the facilities are often nearby, they cannot afford the costly diagnostic and treatment modalities that are accessible only to the higher socioeconomic stratum. Thus, in lack of access to proper promotive, preventive and curative services, and in absence of a civic system that acknowledges their existence, the slum people may therefore be suffering and dying from NCDs unnoticed and unaccounted.Despite all this, it is indeed very heartening to note that the government has recently finalized the final draft of the National Urban Health Policy. Under this, free primary health care will be given to urban dwellers with low economic status, targeting mainly the poor living in the slum areas. We recommend that, because the slum people are probably the most severely hit population in terms of double burden of disease, the policy should give equal emphasis, if not more, to the NCDs and the underlying risk factors.



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*Source: 329156-2013-11-04.xml*