Preliminary analysis of built environmentand cause speciﬁc visits to community
health centres in Medan city, Indonesia
Soo Chen Kwan, Iin Juliani Saragih

Japan-ASEAN Transdisciplinary Studies Working Paper Series No.2
October 2018

Preliminary analysis of built environment and cause specific visits to community
health centres in Medan city, Indonesia
Soo Chen Kwan1*, Iin Juliani Saragih2
Abstract
This study aims to analyse the relationship between urban built environmental featuresand population health in Medan city, Indonesia. We used the local community healthcentres as unit of analysis. We included nine urban environmental features as theindependent factors for the number of visits for mental disorders, hypertension, diabetes
type 2 and all cause mortality as our health outcomes. Poisson model revealed that
increased transport infrastructures, open spaces and education institutes were associatedwith increased cause specific visits to health centres; while increased road intersections
marginally decreased the visits. The prioritization of built environment for health in the
scenario of Medan city might be different from the other cities.
Keywords: built environment, transport infrastructures, open spaces, mental disorders
Acknowledgement
We thank the Planning and Development Agency of Medan City (Bappeda Kota Medan)
for providing the data and information of land use in Medan city, the Medan HealthService Department (Dinas Kesehatan Kota Medan) for providing the health statistics
from the local community health centres, and Dr. Katsuo Kogure (CSEAS) for criticallyreviewing the manuscript. This research was supported by the Japan-ASEAN Platformfor Transdisciplinary Studies, Center for Southeast Asian Studies, Kyoto University.

_____________________________________________________________________________
1

Postdoctoral Research Fellow, Center for Southeast Asian Studies, Kyoto University*E-mail: sc.kwan@cseas.kyoto-u.ac.jp
2

Planning and Development Agency of Medan City (Bappeda Kota Medan), North Sumatra, Indonesia

Introduction
The built environment where we live forms an integral part of our lives as it shapes most of
our daily activities, such as transport, work, grocery shopping, entertainment and recreation.
As we adjust our way of life according to our environment, such lifestyle often persists into
our old age once we settle down at a place with our families. In addition, the built
environment could have irreversible impacts on children growing up, which may determine
their lifelong behaviours and health into their adulthood (Minh et al., 2017; Ding, Sallis,
Kerr, Lee, & Rosenberg, 2011). Therefore, the design and planning of the built
environment in our living space is extremely important to our lifelong well being.
The contribution of built environment to health has been related in many previous
studies. Increased spatial density, land use mix and recreational spaces have been
associated with increased connectivity and opportunities for physical activity in urban areas(Bauman et al., 2012; Durand et al., 2011; Feng et al., 2010). Several studies have also
found evidence that green spaces improve mental well being, and contribute to reduced
mortality through physiological mechanisms (Bowler et al., 2010; Gascon et al., 2016; vanden Berg et al., 2015). However, as causal relationship between built environment andhealth is difficult to establish due to the multiple personal factors and mediating
confounders (McCormack and Shiell, 2011), most of the studies generally look at theirstatistical association. Results on certain built environmental attributes also varied acrossdifferent population groups and methods adopted based on the available measurement
metrics (Ferdinand et al., 2012; Van Cauwenberg et al., 2011). An example is a study by
Saw et al. (2015) that indicated that green spaces did not contribute to the population wellbeing in Singapore, which was highlighted as a tropical country compared to results fromprevious studies in temperate countries. While most of the evidence on the association
between built environment and health were based on observational studies in developed
countries such as Europe, United States, and Australia due to the availability of systematic
data sets, evidence for developing countries in the local context need to be increased.

In this study, we attempt to explore the relationship between urban built
environment and health in Medan city, Indonesia. Medan is the capital of North Sumatranprovince. As of 2016, its population reached a total of 2.2 million, making it the fourthlargest city in Indonesia. Medan covers an area of 265.10 km2, with an average density of
8,409 population per km2 (BPS-Statistic of Medan Municipality, 2017a). It consists of 21
sub-districts (kecamatan) and 151 villages (kelurahan) (Figure 1). In the Green City Action
Plan 2035 (GCAP) of Medan under the National Urban Development Policy and Strategy(NUDPS) (Green City Action Plan 2035, 2016), Medan is working towards becoming aclean, healthy and sustainable city. One of the short term prioritization in the strategy is toimprove public transportation, while green spaces and urban forests are included in the longterm strategy. This study aims to explore the health implications from the local built
environment in Medan city. The results presented in this study is an analysis of aggregateddata collected from local agencies in Medan city to provide a preliminary view of therelationship between the environmental features and health in the city.

Figure 1 Administrative map of sub-districts and villages in Medan cityMethods
Unit of analysis
In this study, we used local community health centres (Pusat Kesehatan Masyarakat or
Puskesmas) as the unit of analysis. There are 39 health centres distributed in Medan city.
Each health centre covers one or more villages depending on the size of village population
and roads of access to the health centres. Therefore, each sub-districts in Medan city may
have a minimum of one to four health centres, while each health centre could cover up to
11 villages for community healthcare. Table S1 shows the villages covered under eachhealth centre. To get the population number and density based on the villages under eachhealth centre, we manually extracted the information for all villages in year 2016 from theannual reports of each sub-districts in 2017 (BPS-Statistic of Medan Municipality, 2017b).
Then, we aggregated the population sizes, and averaged the population densities of thesevillages to get the population information for each health centres. The population density of
the area for each health centre was included as one of the independent factors in our
analyses.
Health data
We obtained the number of cause specific visits to the 39 local community health centres in2016 from the Health Service Department in Medan city (Dinas Kesehatan Kota Medan).
We got the number of visits for mental disorders, hypertension, diabetes type 2, coronary
heart disease, and obesity as our diseases of interests. However, due to too many missing
values for coronary heart disease and obesity, we did not include them in our analyses. We
also excluded one health centre (Simpang Limun) for hypertension and diabetes for missing
data. For all cause mortality, we extracted the data manually from the annual reports ofeach sub-districts in 2017 (BPS-Statistic of Medan Municipality, 2017b). There were 9 subdistricts which did not have the number of mortality in the reports, and were treated as
missing values in the analyses.

Environmental features
We obtained the information on existing land use and physical environment of Medan cityfrom the Planning and Development Agency of Medan city (Bappeda Kota Medan). We
included nine parameters including four transport infrastructure or services, and five land
use metrics as our focus of analyses on environmental features. All the parametermeasurements were divided according to the boundaries of villages covered under eachhealth centre.
The transport parameters included mini bus service routes, rail tracks currently inuse for people, non-arterial roads and road intersections for connectivity. Based on the data
given, there are eight road types in Medan city. We excluded the arterial roads, given thatthese roads would not be conducive for any potential engagement in active transportation.
The pedestrian walkway was not included in the land use map of Medan city as there is no
official designation of pedestrian walkways in Medan city planning, although they may
exist irregularly in some parts of the roads in the city.
For land use, we included the net residential density, land use mix, densities of open
spaces, places of worship, and educational institutes in the city. Net residential density wasdefined as the number of population per total residential area in Medan city (Frank et al.,2005). We calculated the entropy index as a measure of land use mix based on the formula
below:" ×%&(" )Entropy index = - ∑)*+, # %&()) #where k is the number of land use type, and Pi is the proportion of the land use type in thevillage or villages. We included seven types of land uses in the calculation: low residential
density area, average residential density area, high residential density area, commercial area,
trading area, service area, and open spaces. The residential density was categorized basedon the building width, with smaller building width depicting higher density. The

commercial area consisted mainly of services such as banking, transport hubs, learning
institutions, communications, health care, entertainment and private company offices. The
trading area consisted of areas with permanent or temporary merchants, small stores,
supermarkets, department stores, traditional markets, restaurants, petrol stations, and malls.
The office area consisted mainly of government offices, library, institutions, schools,
religious place of worship in Medan city. Open spaces were defined as green spaces, blue
spaces, and open tourist attraction areas.
In the data given, there were eight categories of open spaces in Medan city. They
were the urban forests, tourist spots, spaces for sports, village parks, city parks, cemetery,open land spaces and blue spaces. We re-categorized these spaces into three types. We
combined the urban forest, village parks, urban parks into green spaces category; the tourist
spots, spaces for sports, and open land spaces into open spaces category; and blue spaces in
one category. We excluded the cemetery as we did not consider it as a place for activities.
To combine the three categories into one composite measurement, we calculated the sum of
their Z scores.
Considering the possibly important role of religion in the local community,
especially on the mental health, we included the density of the places of worship in themodel. We also included density of educational institutes as a potential independent factorof health.
Data analysis
We used quasi-Poisson regression to analyse the count data of cause specific visits to local
community health centres in IBM SPSS version 22. Metrics of transport infrastructures andland uses were produced using ArcMap 10.4.1. The dependent variables were the healthdata while the independent variables were the physical features as described above. As
schizophrenia, and mixed anxiety and depressive disorder topped the visits for mental
disorders, we also ran the analyses on them separately (Figure S1). Population size in year

2016 was inserted as the offset variable. In order to test the hypothesis on the genderdisparity in the effect of built environment observed in previous studies (Lake andTownshend, 2013; Bel and Hanes, 2013), we ran the analyses on male and female for thevisits for mental disorders, where it was the only variable with available gender segregation.
Results
Descriptive statistics
The population size covered under a health centre ranged from 11,029 to 167,984 people in2016 (Table S1). Terjun health centre covered the largest population size followed byMedan Deli and Helvetia health centre. The number of population corresponded with thenumber of households in the villages, with an average of 4.32 members in each household.
The cause specific visits for mental disorders were categorized into 16 types asshown in Figure S1. A total of 7,846 visits were recorded for mental disorders in Medancity. Visits for schizophrenia and other primary psychotic disorder constituted more thanhalf of the total mental disorders (54%), followed by mixed anxiety and depressive disorder(MADD) (12%). Helvetia health centre had the highest number of visits for mental
disorders (9.4%) in 2016, followed by Padang Bulan (6.8%) and Sentosa Baru (6.3%).
Padang Bulan had the highest number of visits for both schizophrenia and other primary
psychotic disorder (7.8%), and MADD (20.6%) among all the local community healthcentres.
In 2016, there were 59,841 and 34,312 visits for hypertension and diabetes type 2,
respectively in Medan city. Helvetia (12.1%; 13.4%), Martubung (6.0%; 9.6%) and Medan
Deli (9.4%; 6.7%) health centres topped the number of visits for the both diseases. Table 1
shows the variations in the number of cause specific visits to the health centres in Medancity in 2016. For all cause mortality among the villages with available data, villages under
Mandala health centre area had the highest number of mortality.

Table 1 Descriptive statistics of cause specific visits to local community health centres andall cause mortality in Medan city for 2016
Cause of visits

Mean

s.d.

Minimum

MedianMaximum

IQR

Mental disorders (n=39)

201

157

22

138

737

172

Schizophrenia and otherprimary disorders

109

73

0

93

333

95

Mixed depressive andanxiety disorder

24

42

0

4

191

5

Hypertension (n=38)

1,575

1,403

122

1,204

7,212

721

Diabetes type 2 (n=38)

903

895

63

654

4,587

841

All cause mortality (n=30)

258

207

24

174

781

289

There is a considerably high variability in the distribution of the population density,transport infrastructures and land uses between the areas (Table 2). The area with thehighest population density is Sei Agui, while Teladan has the highest net residential density(Table S2). The public transport infrastructure in Medan city is still under development.
Teladan (6.8%) and Bestari (6.1%) have the highest concentration of minibus routes, while
rail services are only available across several villages, with most concentrating in areas inPulo Brayan (14.1%), and Glugur Kota (13.9%). Kota Matsum has the highest density of
non-arterial roads while Medan Labuhan has the lowest density. For land use mix, Padang
Bulan and Polonia score the highest entropy index (LUM7= 0.91). The amount of open
spaces (green, open and blue) varies widely across the areas, with the Z scores ranging
from -1.92 (Pasar Merah) to 6.45 (Belawan). Polonia has the highest density for green
spaces (12%), while Medan Labuhan contains the most open spaces (26%) among all areas.
Belawan is covered by the most rivers for blue spaces (27%) as it is situated near to thecoastal region.

Correlation matrix in Table 3 shows that road density is highly correlated withintersection density. Besides, it seems that road and intersection density have highly
negative correlations with the density of open spaces.
Table 2 Descriptive statistics of environmental features (n=39)
Features

Unit

Mean

s.d.

Minimum

MedianMaximum

IQR

Population density

person/km2

13,148.64

8,832.47

1,656.50

11,582.50

47,512.00

9,768. 00

Minibus routekm/area width
(km2)

2.24

1.36

0.07

2.27

5.93

2.03

Rail track in use

km/area width
(km2)

0.35

0.50

0.00

0.07

1.93

0.57

Roads density

km/area width
(km2)

17.98

6.40

3.26

18.78

29.05

29.04

Road intersection
densitynumber/area
width (km2)

290.21145.3536.05

264.90627.25417.86

Entropy index(LUM7)

Range 0 to 1

0.62

0.17

0.30

0.65

0.91

0.29

Green, open andblue spaces

Z-score of
km2/area width
(km2)

0.00

2.21

-1.92

-0.73

6.45

1.41

Net residential
densityperson/residential area
width (km2)

27,089.66

14,868.07

3,485.94

25,280.16

76,078.42

21,818.52

Worship placedensity

Number/10,000population

3.69

2.34

0.41

3.61

10.46

2.75

Education institutedensity

Number/10,000population

4.98

4.39

0.10

3.84

22.72

5.19

Table 4 shows the Poisson log-linear regression model between the built
environment features and the number of cause specific visits to health centres. The modelshows that the total visits for mental disorders significantly increased with increased
density of rail, roads, open spaces, and educational institutions in the area. Separate
analysis for schizophrenia did not find any significant associations. However, mixed
anxiety and depressive disorder (MADD) produced similar trends of association as the total
visits for mental disorders. All the significant features increased the number of visits for
MADD except for intersection density where its increase reduced the number of visits by amarginal 1%. Rail density seemed to have the largest effects on these visits for mental
disorders. Comparing the number of visits for total mental disorders and MADD between
male and female, the type of environmental features that were significantly associated wereconsistent with the main results (Table S3). Though, the females seemed to have higher
effect sizes for rail density (RR = 2.54 (female); 1.93 (male)) and road density (RR = 1.26
(female); 1.16 (male)); while the males had the higher effects from the density of open
spaces (RR =1.26 (male); 1.24 (female)) and educational institutions (RR =1.10 (male);1.09 (female)). The effects of road intersection on both males and females were about thesame.
The increase in visits for hypertension was significantly associated with increasedrail density, while increased visits for diabetes type 2 was associated with both increasedrail and road density. Road intersection was associated with a marginal reduction in thevisits for diabetes type 2 by 1%. On the other hand, all cause mortality did not show any
statistically significant associations with the environmental features. Figure 2 shows therelative risks for the cause specific visits and all cause mortality for the significant
environmental features. Although not significant, the effects of the environmental featureson the relative risk for all cause mortality were observed to have opposite directions to thatof the cause specific visits. There were reductions in all cause mortality with regards toincreased environmental features, except for a slight increase from increased roadintersection density.

Table 3 Pearson's correlation between urban features
Population
density
2016

Minibus routedensity

Rail density

1

0.339*

0.174

0.627**

0.597**-0.542**0.471**-0.494**

0.152

0.049

Minibus route density

0.339*

1

0.449**

0.637**

0.561**

0.073

0.683**-0.557**0.129

0.428**

Rail density

0.174

0.449**

1

0.068

0.053

-0.05

0.483**-0.191-0.016

0.096

Road density

0.627**

0.637**

0.068

1

0.943**-0.386*0.512**-0.819**

0.243

0.258

Intersection density

0.597**

0.561**

0.053

0.943**

1-0.382*0.458**-0.700**

0.192

0.225

Entropy index (LUM7)-0.542**

0.073

-0.05

-0.386*-0.382*

1-0.2350.275-0.213-0.089Net residential density

0.471**

0.683**

0.483**

0.512**

0.458**-0.2351-0.396*-0.0520.421**

Density of open spaces(Z-score)-0.494**-0.557**-0.191-0.819**-0.700**0.275-0.396*1-0.27-0.259Density of place ofworship

0.152

0.129

-0.016

0.243

0.192

-0.213-0.052-0.27

10.383*

Density of educational
institutes

0.049

0.428**

0.096

0.258

0.225

-0.0890.421**-0.2590.383*

1

Features
Population density**p<0.01 *p<0.05Roaddensity

Intersection
density

Entropyindex(LUM7)

Net
residential
densityDensity ofopen spaces(Z-score)

Density ofplace ofworship

Density ofeducational
institutes

Table 4 Relative risk of all cause mortality and cause specific visits to health centres in relation to environmental features
RR (95% CI)
Features

Mental disorders(n=39)

Schizophrenia(n=39)

Population density

1.00 (1.00, 1.00)

1.00 (1.00, 1.00)

1.00 (1.00, 1.00)

1.00 (1.00, 1.00)

1.00 (1.00, 1.00)

1.00 (1.00, 1.00)

Minibus route density

0.91 (0.67, 1.23)

1.33 (0.93, 1.89)

0.84 (0.38, 1.84)

0.76 (0.51, 1.13)

1.00 (0.69, 1.44)

1.88 (0.98, 3.59)

Rail density

2.16 (1.19, 3.92)*

1.05 (0.48, 2.31)

7.49 (2.26, 24.91)*

2.44 (1.15, 5.16)*

1.96 (1.02, 3.77)*

0.89 (0.28, 2.82)

Road density

1.20 (1.04, 1.39)*

1.07 (0.88, 1.29)

1.87 (1.31, 2.66)*

1.19 (0.99, 1.44)

1.29 (1.08, 1.53)*

0.82 (0.57, 1.17)

Intersection density

1.00 (0.99, 1.00)

1.00 (0.99, 1.00)

0.99 (0.98, 1.00)*

1.00 (0.99, 1.00)

0.99 (0.99, 1.00)*

1.00 (0.99, 1.01)

Entropy index (LUM7)

4.54 (0.89, 23.28)

2.11 (0.26, 16.88)

25.61 (0.33, 2019.47)

5.82 (0.76, 44.69)

1.20 (0.19, 7.40)

0.39 (0.01, 13.19)

Net residential density

1.00 (1.00, 1.00)

1.00 (1.00, 1.00)

1.00 (1.00, 1.00)*

1.00 (1.00, 1.00)

1.00 (1.00, 1.00)

1.00 (1.00, 1.00)

Density of open spaces(Z-score)

1.27 (1.07, 1.50)*

1.23 (0.98, 1.54)

1.19 (0.71, 2.00)

1.05 (0.85, 1.30)

1.09 (0.89, 1.32)

0.87 (0.57, 1.32)

Density of place ofworship

0.95 (0.84, 1.06)

1.00 (0.86, 1.15)

0.74 (0.54, 1.02)

1.07 (0.95, 1.22)

0.97 (0.85, 1.10)

1.22 (0.99, 1.50)

Density of educational
institutes

1.10 (1.05, 1.16)*

1.03 (0.96, 1.10)

1.20 (1.08, 1.34)*

1.05 (0.98, 1.13)

1.05 (0.99, 1.12)

0.90 (0.77, 1.05)*p<0.05

Mixed anxiety anddepressive disorder (n=39)

Hypertension(n=38)

Diabetes type 2(n=38)All cause mortality(n=30)

Rail density
30.0
RR (95% CI)

25.0
20.0
15.0
10.0
5.0
0.0
Total mental Schizophrenia
disorders

MADD

Hypertension Diabetes type2

All causemortality

Road density
3.00

RR (95%)

2.50
2.00
1.50
1.00
0.50
0.00
Total mental Schizophrenia
disorders

MADD

Hypertension Diabetes type2

All causemortality

Road intersection density
1.02
RR (95% CI)

1.01
1.00
0.99
0.98
0.97
0.96
Total mental Schizophrenia
disorders

MADD

Hypertension Diabetes type2

All causemortality

Open spaces

RR (95% CI)

2.50
2.00
1.50
1.00
0.50
0.00
Total mental Schizophrenia
disorders

MADD

Hypertension Diabetes type2

All causemortality

Figure 2 Relative risks of cause specific visits and all cause mortality in association withbuilt environment.
Sensitivity analysis
The initial model without including the density of place of worship and educational
institutes returned only open spaces as the significant factor on mental disorders (RR = 1.24
(1.02, 1.52)), while the significant features for hypertension, diabetes and all cause
mortality remained the same. Adding the density of places of worship returned the same
results. However, adding the density of education institutes gave more number ofsignificant features that affected the number of visits for mental disorders, as shown in themain results.
Due to the high correlation between road intersection and road density, we tested
the model by excluding either one of them from the model. When we included road density
in the predictors, the result remain approximately the same as the main results for mental
disorders, hypertension and all cause mortality, while no significance was found fordiabetes. When we included the road intersection in the predictors, only education density
appeared to be the significant feature for the visits for mental disorders (RR= 1.09 (1.03,
1.15)), while no significance were found for all the other diseases.

Discussion
Given the majority of the past studies on the relationship between built environment andhealth were done in developed countries, we attempt to find out if the pattern of therelationship is similar in the context of a developing country (Indonesia) in the Southeast
Asian region. Using the existing data from the local community health centres in the city of
Medan, we analyzed the number of cause specific visits to the health centres, and all cause
mortality in relation to the environmental infrastructures. The Poisson regressions
suggested that while increased transport infrastructures (rails, roads), open spaces, andeducational institutes were associated with increased visits to health centres for the specificcauses, the number of all cause mortality seemed to follow the opposite direction in therelationship. In addition, it was observed that increased road intersections marginallydecreased the number of visits but increased the all cause mortality.
Increased urban density and public transport infrastructures have often been related
to positive health impacts, mainly from the expectations that people walk more in acompact environment which contributes to active living (Melis et al., 2015; Sallis et al.,2016). However, such effects did not show in this study, which revealed negative effects ofboth increase in rail and road densities on the number of visits to health clinics in the area.
This is unsurprising as the public transport network in Medan city is still under a very early
development stage (Basuki Joewono et al., 2007), with the most recent one being the rail
link connecting the airport to the city. Similar to other developing countries, the transport
infrastructures in Medan are mainly automobile oriented with limited spaces to walk. The
local people also rely heavily on local traditional transportations such as ‘becak’ (cycle
rickshaws) and ‘gojek’ (motorbike taxis) for short distances, or share the walking spaces on
the curb of the roads which function equally as parkings and waiting areas for cars andmotorcycles. As transport facilities often denote level of urbanization, it is likely that therails and roads contributed more negative health impacts from the amount of traffic noise
and air pollution than physical activity in the area (Buchari and Matondang, 2017), whichwere reflected in the increased visits to health centres. Transport noise and air pollution
have been shown to increase the incidence of hypertension and diabetes (Clark et al., 2017;
Fuks et al., 2011), and cause annoyances and sleep disturbances which are detrimental tomental health (Sygna et al., 2014). In addition, several studies have shown that increased
urbanization and higher density of transport infrastructures were actually associated withincreased prevalence of diabetes (Attard et al., 2012; DenBraver et al., 2018).
One exception to the transport metrics that showed significant but marginally
positive effect was road intersection density on the visits for MADD and diabetes type 2.
This result is unforeseen, judging from the earlier results from roads and rails. The potential
role of road intersections offering more walkability or other potential factors compared toroad and rail infrastructures need to be further studied. For open spaces, our study did notshow any significant association with the number of visits in general. In contrary, there wasa positive association with mental disorders, which was controversial to the protective
effects suggested by previous studies (Gascon et al., 2015). One possible explanation to it
might be the quality and aesthetic conditions of these spaces, which we did not account inthis study. Previous studies have found that the management of green spaces are equallyimportant to encourage their usage in contribution to health (Nasution and Zahrah, 2014;
Galea et al., 2005; van Dillen et al., 2012). All cause mortality did not show any significant
relationship with any of the urban infrastructures. A previous study suggested that higher
junction density was associated with 12% more premature mortality risk (Fecht et al., 2016),
but such association was not found in this study. However, based on Figure 2, all thesignificant environmental infrastructures decreased the relative risks of all cause mortality
except for the density of road intersection.
It is noted that the number of visits to local community health centres may not be
the same as the number of incidence of diseases. Although visits to health centres are often
related to the health conditions of population, increased visits to health centres could be
mediated by other factors, and may not necessary indicate negative population healthoutcomes. Instead, it could also imply that the higher infrastructure density allows people tohave good access to health care, thereby reducing the mortality rate in the area. As with allstudies using secondary data, this study was limited by the availability and quality of thedata. There might be other covariates such as regional income that could affect therelationship, but was not included in this analysis. The density of educational institutes
could in part be a proxy to the local economy, and thus acted as a significant factor in thevisits for mental disorders. In addition, the Medan local health department is at the initialstage of compiling disease specific health statistics, and developing the health information
system, therefore, flaws such as incomplete submission records from health centres, andmissing data were common. Besides, due to the aggregated nature of the data set, the resultsof this study were based on small sample size and large area coverage in each sample(health center). These limitations should be addressed in the future studies of Medan city.
Conclusion
This study offers a preliminary overview of the built environment and health in Medan city.
It shows that the priorities in urban built environment and health that need to be tackled in
the scenario of Medan city might be different from the other cities in previous studies.
Currently, it seems that the negative effects of environmental infrastructure density
including rail transport outweighed their benefits for health. This is probably due to thepresent insufficient infrastructure development to support a health conducive environment
locally. More specific health implications from the environmental features might only beobserved with smaller area studies and more detailed data with the aim of establishingcausal relationship. Factors that could mediate the relationship between built environmentand health such as measurements of air pollutant, noise and physical activity in Medan city
may be incorporated in future studies.

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Supplementary materials

Figure S1 Percentage distribution of type of mental disorder visits to Medan health centres
in 2016

Table S1 The sub-districts and villages under the coverage of local community healthcentres
Sub-districts(Kecamatan)

Medan Amplas

Medan Area

Medan Barat

Medan Baru

Medan Belawan

Medan Deli

Villages (Kelurahan)
AmplasBangun Mulia
Harjosari 1
Harjosari 2
Sitirejo 2
Sitirejo 3
Timbang Deli
Kota Matsum 1
Kota Matsum 2
Kota Matsum 4
Sei Rengas Permata
Pandau Hulu 2
Sei Rengas 2
Sukaramai 1
Sukaramai 2
Pasar Merah Timur
Tegal Sari 1
Tegal Sari 2
Tegal Sari 3
KesawanSilalasGlugur Kota
Pulo Brayan Kota
Karang Berombak
Sei Agul
BaburaDarat
Merdeka
Padang Bulan
Petisah Hulu
Titi Rantai
Bagan Deli
Belawan Bahagia
Belawan Bahari
Belawan I
Belawan II
Belawan Sicanang
Reklamasi Pantai
Kota Bangun

Local community
health centres(Puskesmas)

Amplas

Kota Matsum

Medan Area Selatan

Sukaramai

Glugur Kota
Pulo Brayan
Sei Agul

Padang Bulan

BelawanMedan Deli

Medan Denai

Medan Helvetia

Medan Johor

Medan Kota

Medan Labuhan

Medan Maimun

Mabar
Mabar Hilir
Tanjung Mulia
Tanjung Mulia Hilir
Titi Papan
Tegal Sari Mandala 2
Binjai
Denai
Medan Tenggara
Tegal Sari Mandala 1
Tegal Sari Mandala 3
Cinta Damai
DwikoraHelvetia
Helvetia Tengah
Helvetia TimurSei Kambing C II
Sei Sikambing C II
Tanjung Gusta
Kedai Durian
Suka Maju
Titi Kuning
Gedung Johor
Kwala Bekala
Pangkalan Masyhur
Kota Matsum 3
Pasar Merah Barat
Sei Rengas 1
Teladan Timur
Sitirejo 1
Sudi Rejo 1
Sudi Rejo 2
MesjidPandau Hulu 1
Pasar Baru
Pusat Pasar
Teladan Barat
Besar
Tangkahan
MartubungSei Mati I
Nelayan Indah
Pekan Labuhan
Aur
HamdanJati

Titi Papan
Bromo
Desa Binjai
Medan Denai
Tegal Sari

Helvetia

Kedai Durian

Medan Johor

Pasar Merah

Simpang Limun

TeladanMartubungMedan Labuhan
Pekan Labuhan
Kampung Baru

Medan Marelan

Medan Perjuangan

Medan Petisah

Medan Polonia

Medan Selayang

Medan Sunggal

Medan Tembung

Kampung Baru
Sei Mati II
Suka Raja
Labuhan Deli
Paya Pasir
Rengas Pulau
Tanah Enam Ratus
TerjunPahlawan
Pandau Hilir
Sei Kera Hilir 2
Sei Kera Hilir I
Sei Kera Hulu
Sidorame Barat 1
Sidorame Barat 2
Sidorame Timur
Tegal Rejo
Petisah Tengah
Sei Putih Timur I
Sekip
Sei Putih Barat
Sei Sikambing D
Sei Putih Tengah
Sei Putih Timur II
Anggrung
Madras Hulu
Polonia
Sari Rejo
Suka Damai
Asam Kumbang
Beringin
Padang Bulan Selayang 1
Padang Bulan Selayang 2
SempakataTanjung Sari
LalangSei Sikambing
Baburakwala Batuan
Simpang Tanjung
SunggalTanjung Rejo
Bandar Selamat
BantanBantan Timur
Tembung
Indra Kasih

TerjunSentosa Baru

Bestari
Darussalam
Rantang

Polonia

Padang Bulan
SelayangLalangSunggalMandalaSeringMedan Timur

Medan Tuntungan

Sidorejo
Sidorejo Hilir
DurianGaharuGang Buntu
Glugur Darat 1
Glugur Darat 2
Perintis
Pulo Brayan Bengkel Baru
Pulo Brayan Bengkel Lama
Pulo Brayan Darat 1
Pulo Brayan Darat 2
Sidodadi
ManggaSimalingkar B
Simpang Selayang
Baru Ladang Bambu
Kemenangan Tani
Lau CihNamo Gajah
Sidomulyo
Tanjung Selamat

Glugur Darat

Simalingkar

Tuntungan

Table S2 Characteristics of environmental features according to areas of health centres

Health centre
AmplasBelawanBestariBromo
Darussalam
Desa Binjai
Glugur Darat
Glugur Kota
Helvetia
Kampung Baru
Kedai Durian
Kota Matsum
LalangMandalaMartubungMedan Area
SelatanMedan Deli
Medan Denai
Medan Johor
Medan
Labuhan
Padang Bulan
Padang Bulan
Selayang
Pasar Merah
Pekan Labuhan
Polonia
Pulo Brayan

Population
2016

Population
density
2016

Minibusroute
densityRaildensityRoaddensityRoadintersection
density

Entropyindex(LUM7)

Net
residential
densityDensity ofopen spaces(Z score)

Density ofplaces ofworship

Density ofeducational
institutions

126,340
98,167
23,761
20,637
21,40545,778111,547
11,029151,580
40,69040,18529,65642,243
82,11857,534

13,658.71
10,544.71
13,573.33
23,667.00
11,291.50
11,032.00
14,137.09
2,755.50
13,003.43
14,970.67
8,885.00
25,789.25
11,582.50
18,186.25
4,793.00

1.18
0.36
5.36
2.93
3.47
1.14
2.62
3.90
2.27
2.86
1.15
1.89
2.89
1.96
0.07

0.00
0.16
0.44
0.92
0.34
0.00
0.60
1.89
0.35
0.00
0.00
0.00
0.03
1.01
0.07

18.39
4.21
22.42
19.31
22.94
24.56
18.76
15.81
20.07
18.78
17.05
29.05
19.69
17.90
10.72

319.3060.36
361.87221.75403.18448.86309.13259.45303.37264.69209.65583.44353.50188.72165.28

0.42
0.53
0.71
0.38
0.61
0.39
0.71
0.86
0.67
0.81
0.30
0.43
0.82
0.34
0.53

18,001.96
24,769.12
46,832.76
45,739.95
20,536.45
21,839.27
25,280.16
30,261.51
19,465.35
32,907.61
14,813.37
44,226.15
18,133.60
35,005.89
11,131.88

-0.90
6.45
-0.59
-1.57
-1.21
0.08
-1.33
-0.14
-0.96
-0.48
-1.15
-1.81
-0.52
-1.85
-0.03

4.20
0.41
0.42
9.21
9.34
3.71
2.33
3.63
2.57
0.74
4.73
5.73
4.97
4.99
2.09

4.35
0.10
1.68
7.75
6.07
3.71
2.15
2.72
3.96
2.46
8.46
11.13
2.84
3.90
1.91

28,592
151,892
37,83093,461

19,257.50
8,129.80
11,885.00
7,519.33

3.50
1.08
2.02
1.61

1.03
0.29
0.00
0.00

24.98
13.84
22.74
16.86

461.47180.37378.22239.87

0.54
0.82
0.42
0.69

53,750.16
33,697.89
19,177.32
11,834.67

-1.11
-0.95
-0.15
-0.41

4.90
1.19
3.70
3.64

7.34
1.38
5.02
4.07

32,22940,5601,656.50
7,723.17

0.40
3.40

0.27
0.00

3.26
19.34

36.05
264.900.65
0.91

10,006.91
17,906.68

5.94
-0.59

3.72
4.19

5.59
14.05

107,831
23,431
28,78856,513
20,1155,757.00
14,352.00
3,708.50
4,558.40
14,920.00

2.30
3.53
0.60
0.71
3.11

0.00
0.36
0.43
0.00
1.93

15.15
22.74
5.30
10.60
17.02

181.19485.8454.74
106.90256.48

0.78
0.58
0.65
0.91
0.59

12,133.12
37,089.08
28,017.29
27,844.96
37,155.27

1.05
-1.92
4.68
3.49
-0.72

3.34
3.41
1.74
2.48
0.99

3.34
11.52
4.17
0.71
0.99

Rantang
Sei Agul
Sentosa Baru
SeringSimalingkar
Simpang
Limun
Sukaramai
SunggalTegal Sari
Teladan
TerjunTiti Papan
Tuntungan

18,224
41,573
95,93564,242
57,70922,134.00
47,512.00
19,793.22
17,022.67
7,254.67

3.51
2.56
2.36
2.00
0.61

0.66
0.85
0.01
0.00
0.00

28.42
20.77
25.74
17.87
11.36

627.25364.87401.93222.16144.16

0.47
0.49
0.49
0.69
0.75

33,380.84
27,257.59
38,918.67
27,314.09
7,203.17

-1.57
-0.73
-1.49
-1.67
3.70

4.94
3.61
2.19
2.96
1.21

3.84
2.65
3.34
1.71
1.04

29,02437,50473,594
42,14322,006167,984
32,87027,716

14,196.00
27,644.00
6,788.25
26,061.00
10,799.20
4,685.40
8,218.00
3,351.33

1.86
3.83
3.04
2.64
5.93
0.68
0.95
0.93

0.00
0.00
0.00
0.30
1.12
0.00
0.57
0.00

21.51
25.12
19.38
26.34
20.87
9.73
13.46
9.12

452.91437.53303.79498.54327.64123.33212.31103.36

0.68
0.36
0.81
0.37
0.73
0.68
0.60
0.86

21,062.94
41,509.98
15,270.56
44,258.05
76,078.42
8,964.95
14,233.19
3,485.94

-1.64
-1.44
-0.63
-1.52
-0.95
4.21
-0.99
1.44

0.69
5.60
6.25
5.22
2.73
2.32
3.35
10.46

0.34
8.53
3.40
5.22
22.72
2.86
8.82
8.66

Table S3 Relative risks of mental disorders and MADD between genders in relation toenvironmental features
Features
Population density
Minibus routedensity
Rail density
Road density
Intersection density
Entropy index(LUM7)Net residential
densityDensity of open
spaces (Z-score)Density of place ofworship
Density ofeducational institutes

RR (95% CI)
Mental disorders (n=39)
Mixed anxiety and depressive disorder (n=39)
Male
FemaleMale
Female1.00 (1.00, 1.00)
1.00 (1.00, 1.00)
1.00 (1.00, 1.00)
1.00 (1.00, 1.00)
0.89 (0.67, 1.18)
1.04 (0.72, 1.49)
0.73 (0.34, 1.58)
1.27 (0.54, 2.99)
1.93 (1.06, 3.52)*
1.16 (1.01, 1.34)*
1.00 (0.99, 1.00)
4.58 (0.97, 21.64)

2.54 (1.32, 4.91)*
1.26 (1.06, 1.51)*
1.00 (1.00, 1.00)
3.18 (0.43, 23.42)

4.44 (1.07, 18.34)*
1.73 (1.20, 2.51)*
0.99 (0.98, 1.00)*
12.94 (0.19, 862.53)

1.00 (1.00, 1.00)

1.00 (1.00, 1.00)

1.00 (1.00, 1.00)

11.56 (3.72, 35.89)*
2.08 (1.43, 3.03)*
0.98 (0.97, 1.00)*
16.58 (0.12,2323.432)
1.00 (1.00, 1.00)*

1.26 (1.07, 1.48)*

1.24 (1.00, 1.54)*

1.30 (0.82, 2.07)

0.77 (0.33, 1.76)

0.94 (0.84, 1.05)

0.95 (0.83, 1.09)

0.77 (0.55, 1.08)

0.70 (0.51, 0.95)

1.10 (1.05, 1.16)*

1.09 (1.03, 1.15)*

1.20 (1.06, 1.35)*

1.20 (1.08, 1.33)*