The purpose of this study was to assess the possible effects of residential characteristics on the association between oral hygiene and body mass index (BMI) of elderly people in Indonesia. This cross-sectional study involved 186 participants (87 males, 99 females) aged ≥60 years who were randomly recruited from 8 community health stations for the elderly people. Dentition status, oral hygiene index (OHI), probing pocket depth (PPD) and clinical attachment level (CAL) were assessed in accordance with WHO guidelines. Salivary condition was assessed through the unstimulated whole saliva collection method. Education status and oral health behaviours were evaluated using a standardised questionnaire. BMI was calculated as weight in kilograms divided by height in metres squared. Participants were grouped in accordance with their OHI status (poor, moderate or good) combined with their residential characteristic (rural or urban) to assess the independent effect of OHI and residential characteristic on BMI through linear regression analysis with confounder adjustment. In the crude model of linear regression analysis, the poor OHI group is associated with low BMI regardless of their residential characteristic (rural/urban) when compared to the “good OHI, urban” group with P-values of .045 and <.01 and regression coefficients (β) of −2.1 and −4.5, respectively. However, in the adjusted model, only the “poor OHI, rural” group showed a significant association with low BMI when compared to the “good OHI, urban” group (β = −3.4; P < .01). Hence, low BMI is significantly associated with poor OHI and rural residential characteristic among elderly people in Indonesia.

1. Gerodontology. 2018;1–8. wileyonlinelibrary.com/journal/ger | 1© 2018 Gerodontology Association and John
Wiley Sons Ltd
1 | INTRODUCTION
Increased life expectancy has expanded the proportion of elderly
individuals within the general population in many parts of world. In
2010, approximately 524 million people were aged 65 years or older;
this number is estimated to triple by 2050.1
Furthermore, in 2050,
the number of elderly people will increase to 2 billion, 80% of whom
are living in developing countries.2,3
In 2020, the percentage of the
elderly population in Indonesia is estimated to increase to 11.4% of
the total population along increased life expectancy. By 2017, the
population of Daerah Istimewa Yogyakarta, the province with the
highest life expectancy in Indonesia, will include approximately
168 000 (approximately 5% of the total population) elderly individu-
als aged 60-­64 years.4
Oral health impairment and poor oral hygiene are highly com-
mon among elderly individuals because oral tissues are affected by
Accepted: 19 April 2018
DOI: 10.1111/ger.12352
O R I G I N A L A R T I C L E
Residential characteristic on the association between oral
hygiene and body mass index among elderly people in
Indonesia
Lisdrianto Hanindriyo1
| Elastria Widita2
| Rini Widyaningrum3
|
Bambang Priyono1
| Dewi Agustina4
1
Department of Preventive and Community
Dentistry, Faculty of Dentistry, Universitas
Gadjah Mada, Yogyakarta, Indonesia
2
Dental Hygiene Program, Faculty of
Dentistry, Universitas Gadjah Mada,
Yogyakarta, Indonesia
3
Department of Dentomaxillofacial
Radiology, Faculty of Dentistry, Universitas
Gadjah Mada, Yogyakarta, Indonesia
4
Department of Oral Medicine, Faculty
of Dentistry, Universitas GadjahMada,
Yogyakarta, Indonesia
Correspondence
Lisdrianto Hanindriyo, Department of
Preventive and Community Dentistry,
Faculty of Dentistry, Universitas Gadjah
Mada, Yogyakarta, Indonesia.
Email: lisdrianto_hanindriyo@ugm.ac.id
Funding information
Indonesian Ministry of Research and Higher
Education under the scheme of Penelitian
Unggulan Perguruan Tinggi, Grant/Award
Number: 648/UN1-P.III/LT/DIT-LIT/206
Objective: To assess the possible effects of residential characteristics on the associa-
tion between oral hygiene and body mass index (BMI) of elderly people in Indonesia.
Methods: This cross-­sectional study involved 186 participants (87 males, 99 females)
aged ≥60 years who were randomly recruited from 8 community health stations for
the elderly people. Dentition status, oral hygiene index (OHI), probing pocket depth
(PPD) and clinical attachment level (CAL) were assessed in accordance with WHO
guidelines. Salivary condition was assessed through the unstimulated whole saliva
collection method. Education status and oral health behaviours were evaluated using
a standardised questionnaire. BMI was calculated as weight in kilograms divided by
height in metres squared. Participants were grouped in accordance with their OHI
status (poor, moderate or good) combined with their residential characteristic (rural
or urban) to assess the independent effect of OHI and residential characteristic on
BMI through linear regression analysis with confounder adjustment.
Results: In the crude model of linear regression analysis, the poor OHI group is as-
sociated with low BMI regardless of their residential characteristic (rural/urban)
when compared to the “good OHI, urban” group with P-­values of .045 and .01 and
regression coefficients (β) of −2.1 and −4.5, respectively. However, in the adjusted
model, only the “poor OHI, rural” group showed a significant association with low
BMI when compared to the “good OHI, urban” group (β = −3.4; P .01).
Conclusion: Low BMI is significantly associated with poor OHI and rural residential
characteristic among elderly people in Indonesia.
K E Y W O R D S
body mass index, elderly people, oral hygiene, residential characteristic

2. 2 | HANINDRIYO et al.
the advanced age and poor oral healthcare practices of this popula-
tion.5
Oral health status is significantly associated with nutritional
status.6
The incidence of tooth loss determines the effectiveness of
mastication and swallowing, which are the initial steps of digestion.7
Healthy oral conditions are necessary to maintain good nutritional
status, which is one of the most influential factors of general health
status.5
Moreover, impaired oral health is a significant risk factor for
systemic disease among the elderly people.8,9
Therefore, reducing
the risk of systemic disease by controlling risk factors at an early
stage is important.
The nutritional status of the elderly people can be assessed
using various parameters. Body mass index (BMI) is the most com-
monly used parameter for the assessment of nutritional status and
is an important outcome measure that represents general nutritional
conditions.10
The risk of deterioration in the nutritional status of the
older people increases with age. A previous study revealed that 33%
of 182 elderly individuals in public nursing homes in Jakarta have
an underweight BMI (18.5 kg/m2
).11
In Indonesia, public nursing
homes provide support for neglected, homeless or impoverished
elderly individuals, most of whom enter the care home in a compro-
mised nutritional condition.
Comprehensive data on the oral health status of the Indonesian
elderly are scarce. The most recent data are provided by the National
Basic Health Research Survey, which was conducted in 2013. This
survey reported that the average number of decayed (DT), missing
(MT) or filled teeth (FT) among the age group of 65 years and older
is 18.9, with MT being the main component (1.8 DT, 17.0 MT and
0.1 FT).12
The characteristics of the elderly population in Indonesia
differ from those of the elderly population in developed countries.
Most elderly individuals in Indonesia have relatively low educa-
tion and compromised health conditions that cause them to grad-
ually lose their independence.11
In addition, the effective medical
demand, defined as the percentage of the population with health
problems who have been treated by health professionals, for oral
treatments among urban residents is higher (8.6%) than that among
rural residents (7.5%) in Indonesia.12
However, only less than half of
the total Indonesian population (49.8%) live in an urban area.4
Previous studies on the oral health of the elderly have been
mainly conducted in developed countries. To our knowledge,
only a few studies on the oral health status of elderly people in
Indonesia have been previously conducted and internationally pub-
lished.11,13-15
Therefore, our study aimed to elucidate the possible
influence of residential characteristics on the association between
the oral health status and BMI of elderly individuals in Indonesia.
2 | METHODS
2.1 | Participants
The participants of this cross-­sectional study were recruited from
8 community health stations (4 rural and 4 urban) for elderly peo-
ple (Posyandu Lansia). These health stations equally represented 57
(34 rural and 23 urban) elderly friendly community health centres
and were considered as the residential characteristics of the partici-
pants. The health stations were located in the province of Daerah
Istimewa Yogyakarta, Indonesia and were randomly stratified using
an unreplaced numbered lottery method for each of the residential
characteristic. Rural–urban characteristics were defined in accord-
ance with the criteria published by the Indonesian National Board
of Statistics in 2010.16
The criteria were established with a scoring
technique that accounted for the population density, proportion of
agriculture-­related professions and the existence of public-­leisure
facilities.
In consideration of the availability of resources, letters of invita-
tion to this survey were sent to 35 randomly selected participants
taken from 50 to 60 members of each Posyandu Lansia and aged
60 years and over (N = 280). All recipients were informed about the
purpose of the study. Examination appointments were arranged
for individuals upon the submission of their written informed con-
sent. None of the participants required special assistance for their
daily activities. The Medical and Health Research Ethics Committee
Faculty of Medicine, Universitas Gadjah Mada and Dr. Sardjito
Hospital approved the study protocols and protected the rights of
the participant in compliance with the Declaration of Helsinki as re-
vised in 2000 (Approval number: KE/FK/441/EC/2016).
2.2 | Measurements
2.2.1 | Oral health parameters
Oral examinations were performed by 4 trained dentists under suf-
ficient illumination with artificial light. Dentition status for the meas-
urement of decayed, missing, filled teeth (DMFT) index, present
teeth (PT) and oral hygiene index (OHI) were examined using the
procedures outlined in the WHO Basic Oral Health Survey 2013.17
In addition to the actual crude data, categorised OHI (good for 0-­3;
moderate for 3.1-­6; or poor for 6.1-­12)18
was also used. Periodontal
conditions were assessed using dental mirrors and a periodontal
probe, which was applied at a probing force of 20 g. The probing
pocket depth (PPD) and clinical attachment level (CAL) at 6 sites (me-
siobuccal, midbuccal, distobuccal, mesiolingual/palatal, midlingual/
palatal and distolingual/palatal) were recorded for all teeth, including
the third molars, and rounded to the nearest whole millimetre. The
examiners were calibrated before and during the survey, and inter-
examiner reliability was assessed. The Kappa value of the replicated
examinations of 10 patients ranged from 0.75 to 0.9 for PPD and
0.65 to 0.8 for CAL; these ranges were in substantial to almost per-
fect agreement with the basic oral health survey method established
by the WHO.17
The percentage of sites with positive bleeding on
probing (BOP), PPD ≥ 4 mm and CAL ≥ 4 mm were used to represent
periodontal condition.
Furthermore, the unstimulated whole saliva collection method19
was used to assess the salivary condition of the participants.
Participants used a measuring cup to collect the saliva that they pro-
duced within 5 minutes. The total salivary volume was divided by
5 to yield the volume in millilitres per min. The unstimulated whole

3. | 3HANINDRIYO et al.
saliva flow rate (UWSFR) with a category of low (0.2 mL/min) and
normal (≥0.2 mL/min) was used for the analysis in this study.20
2.2.2 | General health parameters
The participant’s height and body weight were measured using a
standard hospital weighing balance and height measure (SMIC
Health Scale; Hangzhou Wanto Precision Tech. Co. Ltd, Zhejiang,
China). Participants were instructed to wear lightweight clothes
and to remove their shoes, jackets, caps and heavy clothing acces-
sories during the measurements.21
BMI was calculated as weight
in kilograms divided by height in metres squared. Moreover, this
study analysed the blood serum parameters of the participants to
explore systemic markers for diabetes mellitus and nutritional in-
dicators. Glycated haemoglobin (HbA1c), as well as albumin serum
levels, was quantified by a well-­trained phlebotomist from the
Universitas Gadjah Mada Academic Hospital laboratory unit. The
participants were instructed to fast during the night before blood
sampling. Participants with HbA1c serum levels of ≥6.5% were con-
sidered to display poor serum HbA1c, whereas participants with
HbA1c serum levels of 6.5% were considered to display normal
serum HbA1c.22
2.2.3 | Oral health behaviours parameters
All participants completed a standardised questionnaire that in-
cluded items on educational status (9 years = 0 or ≥9 years = 1);
oral health behaviours (daily tooth brushing frequency [twice or
more = 0; once or less = 1]; regular use of mouth-­rinse, dental floss,
toothpicks and xylitol chewing gum [yes = 0; no = 1]; and dental visit
[never = 0, symptomatic-­based visit = 1; rarely = 2]).
2.3 | Statistical analysis
Initially, participants’ characteristics were described on the basis of
residential characteristics (urban and rural) using the t test for nor-
mally distributed quantitative data and the χ2
-test for categorical
variables. Subsequently, comparisons among oral health statuses
were analysed on the basis of OHI grouping to evaluate any associa-
tion between them. In addition, the association between some varia-
bles (sociodemographic factors, oral and general health parameters)
and BMI was also assessed. Following this analysis, one-­way analysis
of variance (ANOVA) was used to compare the participants’ mean
BMI after the participants were divided into 6 groups of “good OHI,
urban” (n = 35), “moderate OHI, urban” (n = 51), “poor OHI, urban”
(n = 16), “good OHI, rural” (n = 9), “moderate OHI, rural” (n = 37),
and “poor OHI, rural” (n = 38), with a post hoc analysis using least-­
significant difference (LSD) method.
Finally, linear regression modelling was used to examine the in-
dependent effect of OHI and residential characteristic on the partic-
ipants’ BMI, adjusted for some potential confounders found in the
previous analysis. The values of the variance inflation factors (VIF)
were used to detect any multicollinearities among confounders within
the model. In this analysis, OHI and residential characteristic groups
were used as independent variables, and BMI was used as the depen-
dent variable. The “good OHI, urban group” was used as the reference
category for this analysis. All calculations and statistical analyses were
performed using SPSS for Windows software (ver. 22.0; IBM Corp.,
Armonk, NY, USA). Statistical significance was set at α = 0.05.
3 | RESULTS
The invitation for this survey received 78.6% positive responses
(n = 220). Six urban-­residing and 54 rural-­residing subjects did not
respond. Thirty-­four individuals, all of whom are rural residents,
from 220 positive responders failed to attend the examination be-
cause of personal reasons, such as urgent family matters and lack
of transportation. Finally, 186 participants (87 male and 99 female)
were included in this study.
The description of the participants in accordance with their
residential characteristic is presented in Table 1. Among all of the
sociodemographic parameters, only years of education showed a
statistically significant difference between rural-­and urban-­residing
participants. Furthermore, among the assessed oral health be-
haviours, tooth brushing frequency, mouth rinsing, toothpick use
and dental visit were significantly differed between rural-­and urban-­
residing participants. Sites with positive BOP, and CAL ≥ 4 mm, as
well as UWSFR, OHI, DMFT and PT were the oral health parameters
significantly differed between groups. Serum HbA1c and BMI (as
general health paramaters) showed significant differences between
rural-­and urban-­residing participants.
Furthermore, all variables of oral health status, except for
DMFT and sites with PPD ≥ 4 mm, significantly differed among all
OHI groups (Table 2). Participants with poor OHI tended to have a
higher incidence of poor oral health statuses than good oral health
statuses. Moreover, participants with poor OHI have significantly
fewer PT than participants with good or moderate OHI. BMI was
associated with years of education, residential characteristics and
age (for sociodemographic factors). It also differed among groups
based on OHI, and UWSFR, as well as significantly correlated with
percentage of sites with positive BOP, PPD ≥ 4 mm, CAL ≥ 4 mm and
PT (Table 3). While for blood serum condition, BMI significantly dif-
fered only between HbA1c serum groups.
The results of one-­way ANOVA for OHI and residential char-
acteristic groups showed that BMI means significantly differed
between groups (P .01; F score = 7.6; data not shown). As shown
in Figure 1, LSD post hoc analysis revealed significant differences
(P .05) among “good OHI, urban” group with “poor OHI, urban” and
“moderate OHI, rural” groups, as well as among “poor OHI, rural,”
“poor OHI, urban,” and “good OHI, rural” groups. Furthermore, sig-
nificant differences were also found among “poor OHI, rural” group
with “good OHI, urban,” “moderate OHI, urban” and “moderate OHI,
rural” groups with P-­values of .01.
The result from the crude model of linear regression analysis
shown in Table 4 indicated that the poor OHI group is associated

4. 4 | HANINDRIYO et al.
with low BMI regardless of residential characteristic (urban or rural)
when compared to the “good OHI, urban” group (P = .045 and P .01,
respectively; regression coefficient β = −2.1 and β = −4.5). However,
in the model adjusted for several variables showing significant re-
sults in the previous correlation analysis (years of education, age,
percentage of sites with positive BOP, PPD ≥ 4 mm, CAL ≥ 4 mm,
TA B LE 1 Description of subjects based on residential characteristic
Parameter
Residential
P-­valuea
Rural (n = 84)
n (%)
Urban (n = 102)
n (%)
Sociodemographic factor
Gender
Male 35 (41.7) 52 (51.0) .21
Female 49 (58.3) 50 (49.0)
Years of education
9 y 38 (45.2) 14 (13.7) .01
≥9 y 46 (54.8) 88 (86.3)
Oral health behaviours
Tooth brushing
2 times/d 13 (15.5) 4 (3.9) .01
≥ 2 times/d 71 (84.5) 98 (96.1)
Mouth rinsingd
No 69 (83.1) 71 (70.3) .04
Yes 14 (16.9) 30 (29.7)
Flossingd
No 79 (95.2) 98 (97) .51
Yes 4 (4.8) 3 (3)
Toothpicksd
No 59 (71.1) 51 (50.5) .01
Yes 24 (28.9) 50 (49.5)
Chewing xylitol gumse
No 77 (92.8) 87 (87) .20
Yes 6 (7.2) 13 (13)
Dental visitf
Never 40 (48.8) 25 (24.5) .01
Symptomatic based 31 (37.8) 60 (58.8)
Rarely 11 (13.4) 17 (16.7)
Oral health status and blood serum condition
UWSFR
Normal 44 (52.4) 95 (93.1) .01
Low 40 (47.6) 7 (6.9)
Serum HbA1c
Normal 80 (95.2) 85 (83.3) .01
Poor 4 (4.8) 17 (16.7)
Mean ± SD P-­valueb
BMI 22.7 ± 3.7 24.9 ± 3.4 .01
OHI 5.8 ± 2.2 4.0 ± 2.1 .01
DMFT 16.1 ± 6.5 14.1 ± 6.3 .03
PT 21.1 ± 5.0 23.8 ± 5.5 .01
(Continues)

5. | 5HANINDRIYO et al.
UWSFR, PT, and serum HbA1c), only the “poor OHI, rural” group ex-
hibited a significant association with low BMI when compared to the
“good OHI, urban” group (β = −3.4; P .01). Analysis for the value of
VIF yielded values of 1.191-­1.954, indicating that multicollinearities
among the confounders within the adjusted model can be ignored.
4 | DISCUSSION
The present results showed that among the elderly people, OHI
and residential characteristic are significantly associated with low
BMI. The BMI of rural-­residing participants with poor OHI are 3.4
points lower than those of urban-­residing participants with good
OHI. This finding is consistent with a previous study that found
that poor oral status increases the difficulty of eating hard foods
and the consumption of mashed food while decreasing eating
pleasure. These effects eventually increase the risk of undernutri-
tion among the elderly people.23
Furthermore, these effects limit
the consumption of solid and fibrous foods with self-­cleaning abil-
ity, thus increasing the risk of poor oral hygiene among the elderly
people.24
This observation is validated by the present findings,
which showed that participants with poor OHI tend to have worse
oral health status (more sites with positive BOP, CAL 4 mm and
low saliva flow rate and PT) than those with good OHI.
TA B LE 2 Comparative analysis of OHI based on oral health status
Parameter
OHI
P-­valuea
Good (n = 44) Moderate (n = 88) Poor (n = 54)
Median (25%-­75%)
Sites with positive BOP (%) 3.1 (0-­15.6) 28.1 (12.5-­53.5) 57.2 (27.7-­87.2) .01
Sites with PPD ≥ 4 mm (%) 10.3 (2.0-­30.4) 5.6 (0-­26.3) 7.5 (1.5-­13.1) .17
Sites with CAL ≥ 4 mm (%) 16.7 (5.2-­24.4) 18.5 (10.9-­30.9) 34.1 (18.3-­56.2) .01
n (%) P-­valueb
UWSFR
Normal 42 (95.5) 72 (81.8) 25 (46.3) .01
Low 2 (4.5) 16 (18.2) 29 (53.7)
Mean ± SD P-­valuec
DMFT 15.4 ± 6.2 14.9 ± 6.9 14.8 ± 6.0 .90
PT 24.8 ± 4.3 22.1 ± 5.8 21.5 ± 5.3 .01
BOP, bleeding on probing; CAL, clinical attachment level; DMFT, decayed missing filled teeth; OHI, oral hygiene index; PPD, probing pocket depth; PT,
present teeth; SD, standard deviation; UWSFR, unstimulated whole saliva flow rate.
a
Kruskal-­Wallis test.
b
χ2
test.
c
One-­way ANOVA.
Median (25%-­75%) P-­valuec
Age 64 (61-­70) 64 (60-­66) .34
Serum Albumin (g/dL) 4.4 (4.2-­4.5) 4.5 (4.3-­4.6) .13
Sites with BOP (%) 36.9 (21.5-­68.9) 14.0 (3.13-­43.8) .01
Sites with PPD ≥ 4 mm (%) 5.9 (1.4-­21.9) 7.7 (1.2-­22.9) .97
Sites with CAL ≥ 4 mm (%) 25.0 (14.4-­45.0) 17.7 (9.9-­32.4) .01
BMI, body mass index; BOP, bleeding on probing; CAL, clinical attachment level; DMFT, decayed missing filled teeth; HbA1c, glycated haemoglobin;
OHI, oral hygiene index; PPD, probing pocket depth; PT, present teeth; UWSFR, unstimulated whole saliva flow rate.
a
χ2
test.
b
Independent t test.
c
Mann-­Whitney U-­test.
d
Missing data for 2 (1 rural-­and 1 urban-­residing) participants.
e
Missing data for 3 (1 rural-­and 2 urban-­residing) participants.
f
Missing data for 2 rural-­residing participants.
TA B LE 1 (Continued)

6. 6 | HANINDRIYO et al.
The present study found that OHI is significantly higher, and
BMI is lower among rural-­residing participants given that residents
of rural areas in Indonesia have limited access to various types of
foods.16
The association between urban residence and high BMI in
low and middle-­income countries is naturally driven by the higher
individual-­and community-­level socioeconomic status of urban res-
idents than that of rural residents; this pattern is typical pattern in
developing countries.25
In addition, the present findings on oral health behaviour pa-
rameters could be useful in explaining effects of OHI and resi-
dential characteristic on BMI. Urban-­residing participants have
a higher frequency of tooth brushing and dental visits than their
rural counterparts, and rural-­residing participants have higher OHI
scores than their counterparts. These results indicated that resi-
dential characteristics are associated with oral health behaviour
and OHI. Differences in the degree of access of rural and urban res-
idents to dental treatment facilities and professionals in Indonesia
might be a factor underlying this phenomenon. These notions are
in line with a previous study that cited accessibility as major de-
terminant followed by quality of services for seeking oral health
care.26
Inaccessibility includes the lack of transport services and
nonavailability of a dentist.27,28
The functional and medical status
TA B LE 3 Associations between
variables and body mass index (BMI)
Parameter
BMI
P-­valuea
Mean ± SD
Gender
Male 23.5 ± 3.6 .11
Female 24.4 ± 3.7
Years of education
9 y 22.3 ± 3.8 .01
≥ 9 y 24.6 ± 3.5
Residential characteristic
Rural 22.7 ± 3.7 .01
Urban 25.0 ± 3.4
OHI
Good 25.3 ± 3.5 .01d
Moderate 24.5 ± 3.3
Poor 21.9 ± 3.7
UWSFR
Normal 25.6 ± 3.4 .01
Low 22.2 ± 4.0
Serum HbA1c
Normal 23.7 ± 3.7 .01
Poor 26.0 ± 3.3
Correlation coefficient P-­valueb
Age −2.42 .01
Serum albumin −0.17 .82
Sites with positive BOP (%) −0.24 .01
Sites with PPD ≥ 4 mm (%) 0.22 .01
Sites with CAL ≥ 4 mm (%) −0.21 .01
P-­valuec
DMFT −1.37 .06
Present teeth 0.23 .01
BMI, body mass index; BOP, bleeding on probing; CAL, clinical attachment level; DMFT, decayed
missing filled teeth; HbA1c, glycated haemoglobin; OHI, oral hygiene index; PPD, probing pocket
depth; PT, present teeth; SD, standard deviation; UWSFR, unstimulated whole saliva flow rate;
a
Independent t test.
b
Spearman’s correlation test.
c
Pearson’s correlation test.
d
One-­way ANOVA.

7. | 7HANINDRIYO et al.
of elderly people prevents them from travelling long distances to
seek medical or dental care.
In Indonesia, the inaccessibility of dental care for the rural
population may be caused by the misdistribution of dentists
and dental auxiliaries. In 2017, 32 411 dentists registered with
the Indonesian Medical Council.29
Approximately 78% of these
dentists live in the western part of Indonesia, which is known
for its more developed provinces than its eastern counterpart.
Among all registered Indonesian dentists, 77% are women who
tend to live in urban areas with their families. Dentists naturally
concentrate in big cities, and they might not be as available
in rural areas.30
A possible solution to the inequity of den-
tal care delivery in Indonesia is to constantly deploy recently
graduated dentists to remote districts as mandatory service.
This solution will ensure the availability of dentists and dental
treatment equipment at most Puskesmas.31
Establishing oral
health promotion and prevention programmes for the elderly
people in Indonesia and ensuring the equal distribution of den-
tists in all parts of the country are necessary to guarantee the
health and improve the quality of life of the elderly population.
In addition, the elderly individuals tend to neglect their oral
health, including oral hygiene. This tendency increases the suscep-
tibility of the elderly people for being underweight11
and eventu-
ally increases all-­cause mortality rate.32
Therefore, the Ministry of
Health of Indonesia should advocate and promote preventive oral
health programmes that specifically target the elderly. The success
of these programmes will provide benefits for the government and
decrease expenditure on medical treatment, particularly among the
elderly population.
Several limitations should be considered when interpreting the
results of this study. This study did not assess the participants’
socioeconomic condition. Thus, the important factor of the par-
ticipants’ ability and willingness to pay for access to dental care,
which might have a profound effect, was not assessed. Moreover,
future studies should consider information about dietary hab-
its, which are common risk factors for BMI and oral health out-
comes, to validate the significant association between variables.
Furthermore, the cross-­sectional design used in this study pre-
cludes revealing causality among variables. Moreover, the reli-
ability of the observed association might not be fully dependable
given the possibility of the influence of unobserved and unknown
confounders. In addition, given the nature of the sample size used
in this study, the results may not be generalisable.
FI G U R E 1 Mean plots for OHI and residential characteristic
groups. Horizontal brackets represent the mean difference
between groups (*P-­value .001, **P-­value .05)
Parametersa
Dependent variable: BMI
Crude Adjustedb
Coeff. 95% CI P-­valuec
Coeff. 95% CI P-­valuec
Moderate OHI,
Urban (n = 51)
−0.8 −2.3, 0.7 .27 −0.6 −2.1, 0.9 .41
Poor OHI, Urban
(n = 16)
−2.1 −4.1, −0.04 .05 −1.4 −3.5, 0.7 .19
Good OHI, Rural
(n = 9)
−1.8 −4.3, 0.7 .15 −1.9 −4.4, 0.7 .15
Moderate OHI, Rural
(n = 37)
−1.7 −3.3, −0.1 .04 −1.2 −2.9, 0.5 .16
Poor OHI, Rural
(n = 38)
−4.5 −6.1, −2.9 .01 −3.4 −5.3, −1.5 .01
Coeff., coefficient; CI, confidence interval; BMI, body mass index; OHI, oral hygiene index.
a
Good OHI, Urban as reference group.
b
Adjusted for: Education, age, percentage of sites with positive BOP, percentage of sites with
PPD ≥ 4 mm, percentage of sites with CAL ≥ 4 mm, unstimulated whole saliva flow rate, present
teeth, and HbA1c.
c
Linear regression.
TA B LE 4 Linear regression analysis for
BMI among OHI and residential
characteristic groups

8. 8 | HANINDRIYO et al.
5 | CONCLUSION
Low BMI is significantly associated with poor OHI and rural resi-
dence among elderly people in Indonesia.
ACKNOWLEDGEMENTS
This study was supported by a Research Grant from the Indonesian
Ministry of Research and Higher Education under the scheme of
Penelitian Unggulan Perguruan Tinggi (648/UN1-­P.III/LT/DIT-­LIT/206).
We are profoundly grateful to the study participants for their assistance.
CONFLICT OF INTEREST
The authors declare that they have no conflict of interests.
ORCID
Lisdrianto Hanindriyo http://orcid.org/0000-0003-0525-7836
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How to cite this article: Hanindriyo L, Widita E, Widyaningrum
R, Priyono B, Agustina D. Residential characteristic on the
association between oral hygiene and body mass index among
elderly people in Indonesia. Gerodontology. 2018;00:1–8.
https://doi.org/10.1111/ger.12352