Socioeconomic and Behavioral Factors Associated with Obesity
Across Sex and Age in Honiara, Solomon Islands
Chihiro Tsuchiya MHS; Takuro Furusawa PhD; Samo Tagini RN; and Minato Nakazawa PhD
Abstract
The Solomon Islands is currently experiencing a change in disease burdens,
from communicable to non-communicable diseases. Obesity is one of theleading non-communicable diseases causing death. Urgent action is needed
to decrease the high economic and personal costs associated with obesity.
This study proposes to determine behavioral and socioeconomic factors associated with obesity among different sex and age groups in an urban areaof the Solomon Islands. In 2016, a cross-sectional study was conductedamong adults aged 20 to 80 in Honiara, the capital of the Solomon Islands.
Anthropometric measurements and a survey of socioeconomic status (SES)
and behavioral status were conducted among 176 participants using aquestionnaire. Multiple linear regression analysis was used to identify thesocioeconomic factors significantly associated with higher body mass index(BMI) by age group. The study found a high prevalence of overweight (34%)
and obesity (48%) in both sexes. Multiple linear regression analysis foundthat having a high-income level and being married were positively associatedwith higher BMI among young adults. In the middle age groups, the highest
income level was positively associated with higher BMI. Young and middleaged adults with a high SES might consume higher calorie food, contributing
to weight gain, but this needs confirmation. Moreover, getting married mightlead to more consistent meals and weight gain among the young age group.
These findings suggest that health professionals have to consider the influence of income level and marital status on lifestyle choices when planninginterventions that promote healthy lifestyles.

Keywords
Non-communicable diseases, Solomon Islands, obesity, developing countries,
factor analysis, risk behavior

Abbreviations
BMI = body mass index
FDSK = food diversity score Kyoto
NCDs = non-communicable diseases
PIC = Pacific Island countries
SD = standard deviation
SES = socioeconomic status
Introduction
It has been estimated that of the 57 million deaths worldwide in2016, 72% were caused by non-communicable diseases (NCDs)
such as cardiovascular disease, cancer, chronic respiratory disease, and diabetes.1 More than 78% of the mortality occurs in
low-and middle-income countries that suffer the double burdenof NCDs and communicable diseases.1

Overweight and obesity are serious risk factors that contributeto cardiovascular disease, diabetes, and cancer. The PacificIslands countries (PICs) region is composed of 22 countries
and territories across Polynesia, Micronesia, and Melanesia.
The highest prevalence of obesity and type 2 diabetes in theworld is found there.2 The Solomon Islands is one of the PICs
that has experienced a striking rise in the prevalence of obesity.3
According to Friedlaender et al, obesity was not recognized asa problem in the 1960s.4 However, after independence from
British rule in 1978, Eason, et al (1987), observed adult obesityin the urban areas of Western province.5 According to the 1989
National Nutrition Survey in the Solomon Islands, only 11%of the women were considered overweight and 3.5% obese.3
However, by 2016, according to the World Health Organization, the prevalence of being obese among women was 25%
and among men 16%.1 Currently, NCDs are estimated to cause
69% of the Solomon Islands’ total deaths.1
Since the end of World War II, the political and economic changes
experienced in the PICs have been widely described.6 Dramatictransitions in the food environment due to modernization, migration, and urbanization have occurred in the Solomon Islands
since independence from British rule.6 These transitions have, inturn, dramatically impacted dietary intake and physical activity,
causing an increase in obesity and NCDs in the islands.5 The dietof the Solomon Islands was traditionally based on root crops,
fruits, seafood, vegetables, coconut products, and various nuts.7
However, consumption of higher energy-density refined cereals, animal products, oil, and processed foods has significantly
increased.7 This nutrition transition is associated with the risingburden of NCDs.7 Rising incidence of NCDs in the Solomon
Islands is also impacted by other factors, including negative
lifestyle behaviors seen in unbalanced diets, lack of physicalactivity, drinking, and smoking.7 Another factor is that obesity
is widely considered to affect people of lower socioeconomic
status (SES) more than those of higher SES worldwide.8
The distribution of health-related behaviors is usually affectedby age and gender.9 Considering gender-and age-specific socioeconomic status and behavioral patterns might help healthprofessionals focus on early prevention programs on the mostimportant and cost-effective risk factors. However, there is little
information about the factors associated with obesity by sex
in the Solomon Islands. Therefore, the objective of this study
is to determine the socioeconomic and behavioral factors as-

HAWAI‘I JOURNAL OF HEALTH & SOCIAL WELFARE, FEBRUARY 2021, VOL 80, NO 224sociated with obesity differentiated by sex and age in an urban
setting in the Solomon Islands to develop effective obesity
prevention techniques.
Methods
Study Area and Population
The Solomon Islands is part of the Melanesian group of countries located in Oceania, northeast of Australia. According to
the Solomon Islands National Statistics Office, in 2019, its
population was approximately 680 806, with 13% living in
Honiara, the capital, situated in the main province of Guadalcanal (Figure 1).10
Design and Subjects
A cross-sectional study was conducted with 176 participantsin an area health center in Honiara. The study recruited adultoutpatients aged 20 or over at the clinic with informed consent
and without incentives. The subjects were patients visiting theclinic between July 1 and 31, 2016. The subjects were recruited
through a convenience sampling of general department patients,
an outpatient facility that provides diagnosis, treatment, injections, and counseling services. Pregnant participants were

excluded. The following items were investigated for all subjects: anthropometric measurements and interviews on socioeconomic status. A semi-structured, interviewer-administeredquestionnaire was divided into 3 sections: socio-demographic
characteristics with 5 questions, nutrition status with 11 questions, and lifestyle behavior with 3 questions. The anthropometric measurements took 10 minutes, and the interview took
30 minutes. The cultural acceptability of the questions was
confirmed through a pilot study with several local people. The
surveys were conducted through one-on-one interviews by aJapanese researcher fluent in Pidgin, the common language of
the Solomon Islands.
Anthropometric Measurements
Height was measured using a 0.1 cm precision Seca 213 Portable Stadiometer (model 213, SECA, Germany). Weight wasmeasured using a 0.1 kg precision portable scale (BC-705N,
TANITA, Japan).
The participants were asked to wear minimal clothing and no
shoes or socks. Body composition was defined using bodymass index (BMI) in kg/m2, and normal weight was defined as
BMI lower than 25, overweight as BMI 25–29.9, and obesity
as BMI higher than 30.11

Figure 1. Map of the Solomon Islands

Map Source: file:///C:/Users/PC5/Downloads/Solomon_Islands_Regions_map.pdf

HAWAI‘I JOURNAL OF HEALTH & SOCIAL WELFARE, FEBRUARY 2021, VOL 80, NO 2
25

Socioeconomic Status
A one-on-one interview was conducted with a questionnaire of
22 questions to obtain data consisting of age, sex, occupation,
marriage status, household income level, and education level
between July 1 and July 31, 2016. Age was categorized into 3
groups: young adult (20–39 years), middle adult (40–59 years),and older adult (60 years or older) following the National Healthand Nutrition Examination Survey in the United States.12
Education level was categorized into 2 groups: low, consistingof those who did not go to school or only completed primaryschool or less, and high, those who went to secondary school orhigher. Income level was categorized into 3 groups: low, middle,
and high. The Wages and Benefits Survey, conducted by the
Solomon Islands Chamber of Commerce and Industry, whichtargeted approximately 80% of the Solomon Islands private
sector workforce, revealed the average employee wage per hourby skill level: unskilled (8.8 Solomon Islands dollars [SBD]),casual (12.5SBD), clerical (14.7SBD), skilled (28.0SBD, andmanagement (54.0SBD).13 Thirty-seven percent of employees
worked 7 hours per day, and 67% worked 8 hours per day.13
Based on the survey, the casual and unskilled employees werecategorized as the low-income group earning 999SBD or lessevery 2 weeks.13 Skilled and clerical were categorized as the
middle-income group earning 1000SBD to 2999SBD every 2
weeks.13 Management were classified in the high-income group,with earnings over 3000SBD every 2 weeks.13
Behavioral Status
A one-on-one interview was conducted with a questionnaire
which consisted of items regarding smoking (Never, 1–2 days/
week, 3–4 days/week, 5–6 days/week, 7 days/week) with thequestion “How many days per week did you smoke during thepast week?” and alcohol drinking behavior (Never, 1–2 days/
week, 3–4 days/week, 5–6 days/week, 7 days/week) with thequestion “How often did you drink alcohol during the past
week?” following a questionnaire from a previous study.7 We
did not ask about betel chewing in our interviews. Exercise
frequency was estimated using the rapid assessment of physical activity scale, a useful tool to assess physical activity usedby a previous study in the Solomon Islands.7,14 The question
items were carefully checked by multiple specialists in this area
for content validity. The questionnaire seemed suitable for theSolomon Islands people because most respondents gave valid
answers with few omissions in the pilot study.
Nutrition Status
Nutrition status was investigated using the 11-item Food Diversity Score Kyoto (FDSK) questionnaire, which was developed
to easily assess nutritional intake in a community setting withoutcomplex measuring or the necessity to remember exact details.15
It covers all basic food items eaten in the Solomon Islands andhas been used successfully in the Solomon Islands, China,
Thailand, and Japan; and thus, was deemed appropriate for our
survey.7,16–20 The questionnaire consists of 11 food items (grains,potatoes, vegetables, meat, fish, dairy products, eggs, beans,seaweed, fruits, nuts) and asks the frequency of consumptionof these foods in an average week over the previous 6 months(Hardly eat [0 days/week], Sometimes [1–2 days/week], Often[3–4 days/week], Everyday [6–7 days/week]).
Ethical Considerations
This study was conducted following the guidelines of the Declaration of Helsinki and was approved by the Ethics Committee ofKobe University (project number 475) and the Solomon Islands
Health Research and Ethics Review Board, Ministry of Healthand Medical Services (HRE No. 034/16). All participants were
provided written information outlining the nature and purposeof the study, and oral informed consent was obtained from allparticipants.
Statistical Analysis
Statistical analyses and calculations were conducted using R
software with the EZR package. 21 Student’s T-test and Chisquared test were used for univariate comparisons between
men and women. Students’ T-test and Fisher’s exact test wereused for univariate comparisons among age groups. Multiple
regression analysis was used to identify separate age groupfactors significantly associated with higher BMI among explanatory variables, including education level, income level,
occupation, and marriage status, while the effects of age andsex were adjusted.
Results
Sample Size and Characteristics by Sex
Sample means and proportions of the characteristics of theparticipants (n = 176) were calculated separately for men andwomen (Table 1). The participants were 43% (n = 75) men and
57% (n = 101) women. Mean age was 43 ± 15 years among
men and 40 ± 12 years among women. Significant differences
between the sexes were observed in BMI, weight classification, education level, and occupation. The mean BMI was
significantly greater in women (32 ± 6) than in men (29 ± 5)
(P < .001). The prevalence of overweight was 41% amongmen, and 29% among women and obesity was 35% among
men and 58% among women, and women had a significantly
higher prevalence of obesity (P = .006). Men had significantly
higher education levels (P = .033) and were more likely to be
employed compared to women (P < .001).

HAWAI‘I JOURNAL OF HEALTH & SOCIAL WELFARE, FEBRUARY 2021, VOL 80, NO 2
26

Demographic and Socioeconomic Characteristics by Sex
and Age Group

Nutrition Status by Age Group
Table 3 shows the results of the eating frequency for each itemfrom the results of FDSK by age group.
Consumption of the food groups which were eaten by over 50%of the participants more than 3 days a week for the young age
group were dairy, fruit, grain, meat; for the middle age group,
fish, grain, potatoes and vegetables; and for the old age group,
fish, grain, potatoes, and vegetables.

Table 2 shows the demographic, socioeconomic, and lifestyle
behavior characteristics stratified for 3 age groups by sex. The
mean BMI was significantly higher among middle-aged men(P=.03). The young adult groups of both sexes had substantiallyhigher education than the middle and old age groups at P<.001
among men and P=.007 among women. Household income wassignificantly higher among middle-aged women (P=.001). In
addition, the old age group among men had significantly higher
unemployment than other age groups (P=.041).

The results showed that the young age group had significantly
higher consumption of dairy food (P<.001), meat (P<.001),
nuts (P=.042), and seaweed (P<.001) than the middle and old
age groups. The young age group also had significantly lower
consumption of fish (P=.004), potatoes (P=.01), and vegetables(P<.001) than the middle and old age groups. There was nodifference in grain consumption across groups.

Regarding lifestyle behavior, young men who smoked or drank
alcohol more than 1 day a week was significantly higher thanfor middle and old age men (P=.032). Young men also hadsignificantly more moderate or vigorous exercise than middle
and old age men (P=.02). Women showed no significant differences in lifestyle behaviors by age group.

Table 1. Socio-demographic and Health Characteristics of Study Participants
Total

Men

Women

n

Mean %n

Mean %n

Mean %

P valued

Age in years (mean±SDa)

17641.3 (13.4)

75

43.4 (15.4)

101

39.7 (11.6).07

BMI (mean±SD)17630.3 (5.7)

75

28.5 (4.8)

101

31.7 (6.0)<.001

Normal weight (BMI < 25)

31

18

18

24

13

13

Overweight (25 ≤ BMI < 30)

60

34

31

41

29

29

Obese (BMI ≥ 30)

85

48

26

35

59

58

Low education(No schooling or Primary school)

92

52

32

43

60

59

High education(Secondary school or higher)

84

48

43

57

41

41

Unemployed

68

39

15

20

53

53

Employee

81

46

49

65

32

32

Employer

27

15

11

15

16

16

Low (Income<1000)

47

27

19

25

28

28

Middle (1000≤Income<3000)

100

57

43

57

57

56

High (Income≥3000)

29

17

13

17

16

16

b

Weight classification (%).006

Educational achievement (%).033

Occupation (%)<.001

Household income (%)c.93

Marital status (%)
Married

151
86
67
89
84
83
.28
Never married/Divorced/Widowed
25
14
8
11
17
17
a
SD = standard deviation; b BMI = body mass index; c Income = Solomon Islands dollar/2 weeks; d P values were calculated using the Chi-squared test and Student’s t-test.

HAWAI‘I JOURNAL OF HEALTH & SOCIAL WELFARE, FEBRUARY 2021, VOL 80, NO 2
27

Table 2. Socio-demographic Characteristics, Health Characteristics and Lifestyle Behaviors by Sex and Age Group
Men
Young age(20-39 years)

Total

Women

Middle age(40-59 years)

Old age(60 and over)

n

Mean
%

n

Mean
%

n

Mean
%

n

Mean
%

75

28.5
(4.8)

33

27.1
(4.4)

26

30.4
(4.9)

16

28.5
(4.3)

Normal weight (BMI < 25)

18

24

11

33

3

12

4

25

Overweight (25 ≤ BMI <30)

31

41

18

55

9

35

4

25

Obese (BMI ≥ 30)

26

35

4

12

14

54

8

50

75

43.4
(15.4)

33

29.5
(5.1)

26

46.3
(4.4)

16

67.3
(6.0)

67

89

26

79

25

96

16

100

Young age(20-39 years)

Total
P

valuedMiddle age(40-59 years)

Old age(60 and over)

n

Mean
%

n

Mean
%

n

Mean
%

n

Mean
%

101

31.7
(6.0)

50

30.8
(5.7)

45

32.4
(6.1)

6

32.6
(8.3)

13

13

8

16

4

9

1

17

29

29

15

30

13

29

1

17

59

58

27

54

28

62

4

67

101

39.7
(11.6)

50

29.9
(5.8)

45

47.6
(5.1)

6

62.0
(2.8)

84

83

41

82

38

84

5

83

17

17

9

18

7

16

1

17

60

59

22

44

33

73

5

83

P

valuedWeight Status
BMIa (mean ± SDb)

.032

.41

Weight classification (%)
.006

.79

Demographic characteristics
Age in years (mean±SD)

.006

<.001

Socio-economic Status
Marital status (%)
MarriedNever married/Divorced/
Widowed

8

11

7

21

1

4

0

0

43

8

24

10

39

14

88

.031

.95

Educational achievement (%)
Low education (No schooling or Primary school)

32

High education (Secondary school or higher)

43

57

25

76

16

62

2

13

41

41

28

56

12

27

1

17

Unemployed

15

20

4

12

4

15

7

44

53

53

29

58

38

84

5

83

Employee

49

65

24

73

16

62

9

56

32

32

10

20

5

11

1

17

Employer

11

15

5

15

6

23

0

0

16

16

11

22

2

4

0

0

Low (Income < 1000)

19

25

9

27

5

19

5

31

28

28

9

18

14

31

5

83

Middle (1000 ≤ Income< 3000)

43

57

18

55

16

62

9

56

57

56

36

72

20

44

1

17

High (Income ≥ 3000)

13

17

6

18

5

19

2

13

16

16

5

10

11

24

0

0

Never

42

57

12

38

14

54

16

100

79

79

34

68

40

89

5

100

1 - 2 days/week

26

35

18

56

8

31

0

0

15

15

12

24

3

7

0

0

3 - 4 days/week

4

5

1

3

3

12

0

0

3

3

2

4

1

2

0

0

5 - 6 days/week

2

2

1

3

1

4

0

0

3

3

2

4

1

2

0

0

7 days/week

0

0

0

0

0

0

0

0

0

0

0

0

0

0

0

0

Sedentary or light physicalactivities/week

62

83

23

70

23

89

16

100

97

97

47

94

45

100

5

100

Moderate or vigorous
physical activities/week

13

17

10

30

3

12

0

0

3

3

3

6

0

0

0

0

Never

58

77

22

67

21

81

15

94

95

95

47

94

44

98

4

80

1 - 2 days/day

12

16

10

30

2

8

0

0

2

2

2

4

0

0

0

0

3 - 4 days/week

0

0

0

0

0

0

0

0

1

1

1

2

0

0

0

0

5 - 6 days/week

0

0

0

0

0

0

0

0

0

0

0

0

0

0

0

0

5

7

1

3

3

12

1

6

2

2

0

0

1

2

1

20

<.001

.007

Occupation (%)
.041

.08

Household income (%)c

.90

.001

Lifestyle Behavior
Alcohol frequency (%)

.002

.24

Exercise behavior (%)
.020

.21

Smoking behavior (%)

7 days/week
a

.032

.06

BMI = body mass index; SD = standard deviation; Income = Solomon Islands dollar/2 weeks; P values were calculated using the Chi-squared test and Student’s t-test.
bcd

HAWAI‘I JOURNAL OF HEALTH & SOCIAL WELFARE, FEBRUARY 2021, VOL 80, NO 228

Table 3. Eating Frequency of Each Food Item from based on the Food Diversity Score Kyoto Survey
Total

Food items

Middle age (40-59 years)

Old age (60 and over)

n

%

n

%

n

%

n

%

Hardly eat

56

32

27

33

22

31

7

33

Sometimes

94

54

42

51

39

55

13

62

Often

16

9

9

11

7

10

0

0

P valueb

a

Beans

Dairy

Egg

Fish

Fruits

Grain

Meat

Nuts

PotatoSeaweedVegetablesaYoung age (20-39 years)

Everyday

9

5

5

6

3

4

1

5

Hardly eat

78

45

21

25

39

55

18

86

Sometimes

22

13

12

15

9

13

1

5

Often

21

12

13

16

7

10

1

5

Everyday

54

31

37

45

16

23

1

5

Hardly eat

131

75

57

69

54

76

20

95

Sometimes

33

19

17

21

15

21

1

5

Often

4

2

3

4

1

1

0

0

Everyday

7

4

6

7

1

1

0

0

Hardly eat

59

34

35

42

20

28

4

19

Sometimes

46

26

26

31

14

20

6

29

Often

29

17

11

13

11

16

7

33

Everyday

41

23

11

13

26

37

4

19

Hardly eat

20

12

9

12

9

14

2

10

Sometimes

65

40

25

33

25

38

15

71

Often

25

15

14

19

9

14

2

10

Everyday

52

32

27

36

23

35

2

10

Hardly eat

8

5

3

4

4

6

1

5

Sometimes

35

20

16

19

17

24

2

10

Often

56

32

28

34

21

30

7

33

Everyday

76

43

36

43

29

41

11

52

Hardly eat

73

42

20

24

34

48

19

91

Sometimes

32

18

15

18

15

21

2

10

Often

22

13

17

21

5

7

0

0

Everyday

48

27

31

37

17

24

0

0

Hardly eat

56

32

20

24

24

34

12

57

Sometimes

82

47

39

47

34

48

9

43

Often

15

9

10

12

5

7

0

0

Everyday

22

13

14

17

8

11

0

0

Hardly eat

44

25

28

34

14

20

2

10

Sometimes

53

30

30

36

18

25

5

24

Often

31

18

11

13

13

18

7

33

Everyday

47

27

14

17

26

37

7

33

Hardly eat

112

64

39

47

54

76

19

91

Sometimes

52

30

37

45

13

18

2

10

Often

8

5

4

5

4

6

0

0

Everyday

3

2

3

4

0

0

0

0

Hardly eat

75

43

49

59

25

35

1

5

Sometimes

19

11

11

13

6

9

2

10

Often

23

13

8

10

8

11

7

33

Everyday
58
33
15
18
32
45
11
52
Hardly eat (0 day/week), Sometimes(1-2 days/week), Often(3-4 days/week), Everyday(6-7days/week); b P values were calculated using the Fisher’s exact test.
HAWAI‘I JOURNAL OF HEALTH & SOCIAL WELFARE, FEBRUARY 2021, VOL 80, NO 2
29

.82

<.001

.14

.004

.08

.84

<.001

.042

.010

<.001

<.001

Table 4. Multiple Linear Regression Analysis with Body Mass Index by Age Group
Young age (20-39 years)

Middle age (40-59 years)

Old age (60 and over)β(SE)P valueβ(SE)P valueβ(SE)P valueSex (Men=0, Women=1)

4.0(1.3).003

2.0(1.5).17

6.1(3.6).32

Marriage status (Married=0,
Not married=1)-4(1.4).006-0.4(2.1).87-3.6(7.0).67

Education (Low education=0,
High education=1)

0.3(1.3).90

0.6(1.6).72

2.8(3.9).07(Low=0, Middle=1, High=0)

3.2(1.9).019

2.9(1.7).09

1.1(3.0).44(Low=0, Middle=0, High=1)

4.4(1.9).023

7.5(1.9)<.001

5.8(5.0).14(Unemployed=0, Employee=1,
Employer=0)

1.2(1.5).96-0.8(1.5).73

1.4(2.8).06(Unemployed=0, Employee=0,
Employer=1)

1.3(1.6).46-1.5(2.2)

.49

-

-

Smoking behavior (Never=0,
Yes=1)-1.1(1.7).50

1.8(2.4).46

8.7(6.4).19

Alcohol behavior (Never=0, Yes=1)-0.9(1.2).46-3.2(1.2)

.08

-

-

Exercise behavior (Sedentary or
light=0, Moderate or vigorous=1)

0.9(1.7).59

0.3(3.4)

.93

-

-

a

Household income

OccupationaAdjusted R2β(SE) presented separately for age group.

0.2

Association Between Factors for Higher BMI
Table 4 shows the results of multiple regression analysis broken
down by age group. It was found that in the young adult group,
being female (P=.003), being married (P=.006), and being at a
middle-income level or higher (P=.019 among middle income
and P=.023 among high income) were all significantly associatedwith higher BMI. In the middle age group, the highest income
level (P<.001) was significantly associated with higher BMI.
On the other hand, there was no significant factor regarding
higher BMI in the old age group. Regarding lifestyle behavior
factors, there was no significant factor regarding higher BMI
in any age group. Scatter plots showed the linearity, and theresiduals were normally distributed. Variance inflation factorsof all variables were less than 2, so multicollinearity was absent.
Discussion
Demographic Factors
While a previous study conducted countrywide found 16% of
men and 25% of women were obese, the current study focusedon the capital city and found a higher prevalence of obesity
among men and women at 35% and 58%, respectively.1 The
difference in obesity prevalence is possibly due to an effect inthe different study area. The previous study targeted all areas,
including the capital. The obesity prevalence in other provinces
is lower than in the capital city. Alternatively, this study focused

0.2

-0.02

on only people living in the capital city, which is urban, more
economically developed, and exhibits a higher prevalence ofobesity than other parts of the country. Urbanization is known
to be one of the factors that cause significant changes in dietand activity patterns.22 A previous study indicated that PICs are
rapidly becoming more urban as people move to cities to escaperural poverty and get better health and education services.23
Urbanization includes an increased intake of fat, sugar, andrefined carbohydrates.24
The present study found women had significantly higher BMI
than men. This is consistent with previous findings in the Solomon Islands and other developing countries.1,16,25–27 One possible
reason for a higher prevalence of obesity in women may befound in marital status. According to our multiple regression
analysis, being a woman and being married is related to obesity
only in the young adult age group. There was no relation shownin the middle and old age groups.
A previous study in the United States found the reason BMI
increased during marriage was due to the social obligations ofmarriage, indicating couples tend to have regular meals andeat heavier, denser food.28 Birth-giving might also contributethe higher prevalence of obesity among women. According toanother study, pregnancy was a factor in gaining bodyweight.29,30
Solomon Islands has a high fertility rate with more than 4 children per woman.31 This suggests that being pregnant may be a
trigger to gain body weight among young women.

HAWAI‘I JOURNAL OF HEALTH & SOCIAL WELFARE, FEBRUARY 2021, VOL 80, NO 2
30

Food Intake Situation by Gender and Age

Limitations

There was no difference in grain consumption across groups.
A previous study found that rice is chosen as the most frequent
staple food in the Solomon Islands because of its price, shelflife, and ease of preparation.32 The present study also showeda nutrition transition from traditional staples like potatoes toimported rice, bread, and noodles, and this has occurred acrossall age groups in this urban setting.

There were several limitations to this study. First, it is a crosssectional study, which can lead to limited conclusions given thatthe causation between SES and obesity cannot be determined.
Second, the small sample size might also affect the sample’s
margin of error, which lowers its statistical power. Third, thisstudy only looked at the factors of patients who came to theclinic. Thus, the sampling could be biased, and the study did
not have a control group of people who do not go to a clinic.

This study found that traditional foods such as fish, vegetables,and potatoes have been supplemented by animal protein, dairy,
and rice, especially among young adults. Previous studies in the
Pacific indicated a relationship between age and food preferences, with younger people generally preferring to eat imported
processed foods and older people generally preferring local
foods.33 In a recent study, the dietary analysis indicated thaturban residents in the Solomon Islands consumed more refined
grains and processed fish and meats due to the adoption of awestern lifestyle than those still living the traditional lifestyle of
the Solomon Islands.7,16,25 The present study did not look at how
much processed food the young age group consumed, though
it did find that they consumed fewer vegetables and potatoes,
which contribute fiber and micronutrients to the diet than the
middle and old age groups.
A previous study in the Solomon Islands found that consumption of less green leafy vegetables is related to obesity.7 The
prevalence of obesity is forecasted to increase if young adults
continue to pursue a “western” lifestyle.
Socioeconomic Factors for Obesity
In this study, SES was measured by occupation, householdincome, marital status, and education level. The study foundthat BMI was positively associated with income in both theyoung and middle adult groups. This finding was consistent
with other studies. A previous study found that SES, such as
income and property, was positively related to body weight indeveloping countries.34 Conversely, it was just the opposite indeveloped countries.34 Some studies suggest the reason SES has
now become positively associated with obesity in developing
countries; a study conducted in Tonga showed that those withhigher SES found greater availability of imported foods suchas processed foods and fatty imported meats.35 A previous study
conducted in the Solomon Islands found when there is money
available, people buy rice and other imported processed food.36
In the Solomon Islands, increased economic resources mightallow people to buy and eat higher-calorie processed foods,
which contribute to weight gain.

Conclusion
The present study indicates a high prevalence of obesity in both
sexes and found that women have significantly higher BMI thanmen across all age groups. Data from the present study showedthat participants with more than a middle-income level andbeing married were significantly associated with higher BMI
in young adults. In the middle age group, high-income level
was significantly associated with higher BMI. In the Solomon
Islands, higher SES individuals might consume higher-calorie
foods, which contribute to weight gain. The findings strongly
suggest that making an effective plan to develop health promotion programs for young people who have more than a middleincome level or are married is of paramount importance.
Conflicts of Interest
None of the authors identify a conflict of interest.
Acknowledgements
The present study was supported financially by the KAKENHI Grant-in-Aid
for Scientific Research (Project Number: 15H05123 [Takuro Furusawa, Kyoto
University]) of the Japan Society for the Promotion of Science, Japan. I would
also like to thank Mr. Don Moore (Kobe University) for his proofreading.
Authors’ Affiliations:- School of Nursing, Tokyo Medical University, Tokyo, Japan (CT)- Graduate School of Asian and African Area Studies, Kyoto University,
Kyoto, Japan (TF)
- Kukum Clinic, Honiara, Solomon Islands (ST)- Department of Public Health, Kobe University Graduate School of Health Sciences,
Kobe, Hyogo, Japan (MN)
Correspondence to:
Chihiro Tsuchiya MHS; School of Nursing, Tokyo Medical University, Tokyo, Japan,
6-1-1 Shinjuku, Shinjuku-ku, Tokyo 160-8402; Email: chihiro.tsuchiya227@gmail.
com

HAWAI‘I JOURNAL OF HEALTH & SOCIAL WELFARE, FEBRUARY 2021, VOL 80, NO 2
31

References

1. World Health Organization. Noncommunicable diseases country profiles 2018. https://apps.
who.int/iris/handle/10665/274512. Published 2018. Accessed October 7, 2020.
2. Hawley NL, McGarvey ST. Obesity and diabetes in Pacific Islanders: The current burden andthe need for urgent action. Current Diabetes Reports. 2015;15(5):29.
3. Ministry of Health and Medical Services of the Solomon Islands. Solomon Islands national NCD
action plan. https://www.iccp-portal.org/solomon-islands-national-ncd-action-plan. Published2017. Accessed October 7, 2020.
4. Friedlaender JS. The Solomon Islands project: A long-term study of health, human biology, andculture change. Clarendon Press; 1987.
5. Eason RJ, Pada J, Wallace R, Henry A, Thornton R, Thornton R. Changing patterns of hypertension, diabetes, obesity and diet among Melanesians and Micronesians in the Solomon Islands.
Medical Journal of Australia. 1987;146(9):465-473.
6. Ulijaszek S. Modernisation, migration and nutritional health of Pacific island populations.
Environmental Sciences: An International Journal of Environmental Physiology and Toxicology.
2005;12(3):167-176.
7. Tsuchiya C, Tagini S, Cafa D, Nakazawa M. Socio-environmental and behavioral risk factors
associated with obesity in the capital (Honiara), the Solomon Islands; case-control study.
Obesity Medicine. 2017;7:34-42.
8. Sobal J. Social and cultural influences on obesity. International Textbook of Obesity. 2001:305322.
9. Tseng T-S, Lin H-Y. Gender and age disparity in health-related behaviors and behavioral
patterns based on a national survey of Taiwan. International Journal of Behavioral Medicine.
2008;15(1):14-20.
10. The Solomon Islands National Statistics Office. Projected population by province. https://www.
statistics.gov.sb/statistics/social-statistics/population. Published 2019. Accessed October 7,
2020.
11. World Health Organization. Obesity and overweight. https://www.who.int/news-room/fact-sheets/
detail/obesity-and-overweight. Published 2018. Accessed October 7, 2020.
12. Ogden CL, Carroll MD, Fryar CD, Flegal KM. Prevalence of obesity among adults and youth:
United States, 2011–2014. https://www.cdc.gov/nchs/products/databriefs/db219.htm. Published2015. Accessed October 7, 2020.
13. Solomon Islands Chamber of Commerce and Industry. Wages and benefits survey. https://www.solomonchamber.com.sb/media/1123/2010-sicci-wages-and-benefits-survey-report.pdf.
Published 2010. Accessed October 7, 2020.
14. Topolski TD, LoGerfo J, Patrick DL, Williams B, Walwick J, Patrick MMB. Peer reviewed: The
rapid assessment of physical activity (RAPA) among older adults. Preventing Chronic Disease.
2006;3(4).
15. Kimura Y, Wada T, Okumiya K, et al. Eating alone among community-dwelling Japaneseelderly: Association with depression and food diversity. The Journal of Nutrition, Health &Aging. 2012;16(8):728-731.
16. Saka K. Food intake patterns of adult women living in the capital city of Solomon Islands andthe relation between socioeconomic situation and obesity risk [Master’s thesis]. Sapporo,
Japan, Hokkaido University; 2013.

17. Horsey B, Swanepoel L, Underhill S, Aliakbari J, Burkhart S. Dietary diversity of an adult
Solomon Islands population. Nutrients. 2019;11(7):1622.
18. Kimura Y, Okumiya K, Sakamoto R, et al. Comprehensive geriatric assessment of elderly
highlanders in Qinghai, China IV: Comparison of food diversity and its relation to health of
Han and Tibetan elderly. Geriatrics & Gerontology International. 2009;9(4):359-365.
19. Tsuchiya C, Amnatsatsue K, Sirikulchayanonta C, Kerdmongkol P, Nakazawa M. Lifestylerelated factors for obesity among community-dwelling adults in Bangkok, Thailand. Journal ofInternational Health. 2017;32(1):9-16.
20. Zhang X, Meng Q, Feng J, et al. The prevalence of hyperuricemia and its correlates in Ganzi
Tibetan Autonomous Prefecture, Sichuan Province, China. Lipids in Health and Disease.
2018;17(1):235.
21. Kanda Y. Investigation of the freely available easy-to-use software ‘EZR’ for medical statistics.
Bone Marrow Transplantation. 2013;48(3):452-458.
22. Keen M, Barbara J. Pacific urbanisation: Changing times. Australian National University,
Australia; 2015.
23. Connell J. Pacific urbanisation and its discontents: Is there a way forward? Population andDevelopment in the Pacific Islands: Accelerating the ICPD programme of Action at 15. 2009;15.
24. Brouwers C. Obesity in the Pacific island countries [BSc Thesis]. The Netherlands, Wageningen
University; 2016.
25. Shimizu A. Obesity epidemic in adult women in urban setting of Solomon Islands [Masters
thesis]. Kobe, Japan, Kobe University; 2011.
26. Ford ND, Patel Sa, Narayan KV. Obesity in low-and middle-income countries: Burden, drivers,and emerging challenges. Annual Review of Public Health. 2017;38:145-164.
27. Duda RB, Darko R, Seffah J, Adanu R, Anarfi JK, Hill AG. Prevalence of obesity in women of
Accra, Ghana. African Journal of Health Sciences. 2007;14(3):154-159.
28. Dinour L, Leung MM, Tripicchio G, Khan S, Yeh M-C. The association between marital transitions, body mass index, and weight: A review of the literature. Journal of Obesity. 2012;2012.
29. Lederman SA. The effect of pregnancy weight gain on later obesity. Obstetrics and Gynecology.
1993;82(1):148-155.
30. Ziauddeen N, Roderick PJ, Macklon NS, Alwan Na. The duration of the interpregnancy interval
in multiparous women and maternal weight gain between pregnancies: Findings from a UK
population-based cohort. Scientific Reports. 2019;9(1):1-10.
31. Organisation for Economic Co-operation and Development. Society at a glance 2016: OECDsocial indicators. OECD Publishing; 2016.
32. Bottcher C, Underhill SJ, Aliakbari J, Burkhart SJ. Food purchasing behaviors of a remote andrural adult solomon islander population. Foods. 2019;8(10):464.
33. McCubbin SG, Pearce T, Ford JD, Smit B. Social–ecological change and implications for foodsecurity in Funafuti, Tuvalu. Ecology and Society. 2017;22(1).
34. McLaren L. Socioeconomic status and obesity. Epidemiologic Reviews. 2007;29(1):29-48.
35. Evans M, Sinclair RC, Fusimalohi C, Liava a V. Diet, health and the nutrition transition: Some
impacts of economic and socio-economic factors on food consumption patterns in the Kingdomof Tonga. Pacific Health Dialog. 2002;9(2):309-315.
36. Liloqula R, Saelea J, Levela H. Traditional taro cultivation in the Solomon Islands. Sustainable
Taro Culture for the Pacific Conference. 1993.

HAWAI‘I JOURNAL OF HEALTH & SOCIAL WELFARE, FEBRUARY 2021, VOL 80, NO 2
32