Characteristics of PHCs, District's Population, GRDP per Capita, and

Covid-19 Booster Vaccination Coverage in Indonesia: Negative Binomial

Regression Model

Asep Hermawan

Research Center for Public Health and Nutrition, National Research and Innovation Agency, Gedung Kusnoto,

Jl. Ir. H. Juanda No.18, RT.04/RW.08, Paledang, Kota Bogor, Indonesia

Keywords: Covid-19 Booster Vaccination, Characteristics of PHCs, District's Population, GRDP per Capita.

Abstract: Public Health Center (PHC) is one of the health facilities designated to provide Covid-19 vaccination services

and has an essential role in the success of the Covid-19 booster. This article explores the relationship between

characteristics of PHC, population, Gross Regional Domestic Product (GRDP) per capita) and the Covid-19

booster vaccination coverage. This article used secondary data from the Ministry of Health (MoH) dashboard,

2019 Health Facilities Research data. The observation unit was a district/city has started organizing a Covid-

19 booster vaccination, and all PHC were enumerated, as many as 512 regencies/cities. The model used to

determine the factors affecting coverage of the Covid-19 booster vaccination is a negative binomial regression

model since the dependent variable was not Poisson distributed. The result showed that the number of doctors,

midwives, and nurses in PHCs, the proportion 24 hours electricity PHCs by district/city, remote PHCs, PHCs

had a cold chain, the number of residents, and GRDP per capita had a significant effect on coverage of the

Covid 19 booster vaccination. Improvement in the human health workforce, 24- hour electricity, cold chain

availability, accessibility to PHCs, and the villages are essential factors in increased coverage.

1 INTRODUCTION

On December 31st, 2019, Wuhan, China, received the

first report of the 2019 coronavirus disease (COVID-

19) outbreak. The World Health Organization (WHO)

declared this outbreak a pandemic on March 11th,

2020, following the disease's global spread and the

death of over 3,000 people (World Health

Organization, 2020). One month later, in April 2020,

the Indonesian government declared the COVID-19

pandemic a national disaster after receiving reports

from all provinces confirming the presence of

COVID-19 on their territory (Presiden RI, 2020).

As of July 8th, 2022, there were 551,226,298

confirmed cases of COVID-19 worldwide, with

6,345,595 deaths. There were 6,106,024 approved

COVID 19 in Indonesia, with 156,781 deaths (World

Health Organization, 2022). The Indonesian

government is taking massive anti-epidemic

measures to provide comprehensive public health

protection to prevent the spread of Covid 19. These

efforts include public awareness, security, and mass

vaccination against COVID-19 (Kementerian

Kesehatan RI, 2021).

The mass vaccination campaign in Indonesia

began on January 12th, 2021, one day after the

President of Indonesia and several high-ranking

Indonesian officials received the Sinovac vaccine for

the first time as a symbol of the campaign's start

(Andrianto and Manafe, 2021). The success of

COVID-19 Vaccination can be measured by the

proportion of the population that must be vaccinated

to prevent the spread of infectious diseases. This can

be calculated using the initial reproductive rate (R0),

an epidemic statistic used to explain the transmission

of contagious diseases (Anderson and May, 1985,

Metcalf et al., 2015, Smith, 2019). According to

WHO and the Indonesian Technical Advisory Group

on Immunization (ITAGI), herd immunity can be

formed with a minimum vaccination target of 70%

(Kementerian Kesehatan RI, 2021). Until June 26th,

2022, the first dose of COVID 19 vaccination was

given to 201,229,048 people (92.24%), the second

dose to 168,718,027 people (74.40%), and a booster

to 50,282,986 people (20.06%) (Kementerian

Kesehatan RI, 2022).

On January 12th, 2022, the Advanced Dose of

Hermawan, A.

Characteristics of PHCs, District’s Population, GRDP per Capita, and Covid-19 Booster Vaccination Coverage in Indonesia: Negative Binomial Regression Model.

DOI: 10.5220/0011643200003608

In Proceedings of the 4th International Conference on Social Determinants of Health (ICSDH 2022), pages 167-173

ISBN: 978-989-758-621-7; ISSN: 2975-8297

 2023 by SCITEPRESS – Science and Technology Publications, Lda. Under CC license (CC BY-NC-ND 4.0)

167

CQVID-19 Vaccination (booster) began. The

COVID-19 booster vaccination is administered after

a person has received a total primary vaccination dose

to maintain immunity and extend the protection

period. The COVID-19 Vaccination Booster is

provided free of charge by the government to people

aged 18 and up, with priority given to the elderly and

immunocompromised patients. The elderly booster

vaccination target can be implemented

simultaneously in all districts/cities. In contrast, the

non-elderly target is implemented in districts/cities

with dose one coverage of at least 70% and quantity

one coverage for the elderly of at least 60%

(Direktorat Jenderal Pencegahan dan Pengendalian

Penyakit, 2022).

The Public Health Center (PHC) is one of the

healthcare facilities designated to administer the

COVID-19 booster vaccine. The large number of

PHCs spread across all sub-districts in Indonesia

represents a potential health facility for achieving

high booster coverage. In Indonesia, no studies on

PHC and population characteristics have been

conducted. The research hoped to strengthen policies

for implementing the COVID-19 booster vaccination.

2 METHOD

2.1 Data Source

This study used secondary data, which came from

several data sources, including the 2019 health

facility research data (Rifaskes) PHCs module, which

provides data on the characteristics of PHCs. The

information is taken from the Health Development

Policy Agency, MoH (BKPK), which may be

accessed by following specified guidelines at

www.litbang.kemkes.go.id. Rifaskes 2019 is a

national-scale study on health facilities, specifically

PHCs, hospitals, clinics, and the practice of

doctors/midwives. Data collection for Rifaskes 2019

PHCs takes place between June 1st and July 31st,

2019, depending on the number of PHCs in each

district/city throughout Indonesia. Data on the

projected population per district/city in 2019 and

GRDP per capita in 2020 are from Statistics

Indonesia, and booster vaccination data are from the

Ministry of Health's dashboard data on COVID-19

vaccination coverage (Kementerian Kesehatan RI,

2022). The analysis unit consists of 512

districts/cities, and two districts are excluded from the

analysis because of a lack of data.

2.2 Variable

The number of people who have received booster

vaccines by districts/cities until June 26th, 2022, is

the dependent variable in this study (Y). This

information was from the Covid 19 vaccination

coverage dashboard maintained by the Ministry of

Health (Kementerian Kesehatan RI, 2022). The

independent variables are the Number of PHCs by

district/city (X1), the number of doctors, midwives,

and nurses in PHCs by district/city (X2), and the

proportion of PHCs with a travel time of more than 3

hours from PHCs to the remotest villages by

district/city (X3). The following variables are the

proportion of PHCs with 24-hour electricity by

district/city (X4), the proportion of remote PHCs by

district/city (X5), and the proportion of PHCs with

cold chains by district/city (X6). The availability of

refrigerators (freezers open top or side/refrigerator),

refrigerator thermometers, cold boxes/vaccine

carriers/flasks, water pack/ cool pack and vaccine

temperature monitoring thermometer, and 24-hour

availability of electricity from a power source were

indicators of PHCs readiness in maintaining the cold

chain. To calculate X1 to X6 used Rifaskes 2019 data.

The number of population by district/city (X7) is

information on the number of population of

districts/cities obtained from a Statistics Indonesia

report book on population projections in 2019 based

on the Inter-Census Survey (SUPAS) organized by

Statistics Indonesia (Badan Pusat Statistik et al.,

2018). The 2020 GRDP per capita (x1000) (X8) is the

average income of the Indonesian population

calculated from the quotient of GRDP and people in

regencies/cities in 2020, as also published by

Statistics Indonesia (Badan Pusat Statistik RI, 2021).

2.3 Data Analysis

The data were descriptively analyzed to obtain mean,

minimum, maximum, median, and standard deviation

values to describe dependent and independent

variables. Data were analyzed using a Generalized

Linear Models (GLMs) (Greene, 1994).

GLMs are a

general form of the Linear Model. In the classical

linear model, Y is assumed to be normally

distributed. In GLM, the Y response variable can

be distributed other than normal but is included in

the exponential family (Exponential Terms)

(Myers and Montgomery, 1997).

The

equisdispersion assumption, which states that the

mean value and variance value are equal, is applied in

Poisson Regression. However, the equisdispersion

postulate is not always met in the real world.

ICSDH 2022 - The International Conference on Social Determinants of Health

168

Overdispersion occurs when the variance value is

higher than the average value. Overdispersion in

Poisson regression causes the regression parameter

conjecture biased because the standard error's value is

underestimated. Negative binomial regression is a

method for dealing with overdispersion in Poisson

regression (Hilbe, 2011, Greene, 1994). There are

stages in the GLMs test. The first identified the

distribution and then examined correlations between

predictor variables in linear regression models, also

known as multicollinearity, using the Variance

Inflation Factor (VIF). We excluded a variable that

variable had a VIF value greater than ten. The last

stage was running GLMs, a negative binomial test

with family(log) link (log).Results and discussion

2.4 Descriptive Statistics

Compared to other locations, the number of persons

vaccinated with boosters seems to be the highest in

Java and Bali. The population of Papua, Maluku,

Nusa Tenggara and Sulawesi is still below 61080.

However, certain regencies/cities in Kalimantan and

Sumatra seem to be doing better. It resulted in a lower

population density than in the Java and Bali areas

(Figure 1). One of the reasons for low vaccination

coverage in some locations is insufficient access to

PHC services.

Figure 1: The number of people who have received booster

vaccines.

Table 1: The statistical description of dependent and

independent variables.

Variable

Statistic Descriptive

Mean

Mini

Maxi

Media

The number

of people has

received

booster

vaccines

98399,4

21 14760

39824 167195,

X1 The

Number of

PHCs

19,20 3 101 17 11,86

X2 The

number of

doctors,

midwives,

and nurses in

PHCs

571,71 40 2177 475 367,13

X3 The

proportion of

PHCs with a

travel time of

more than 3

hours from

PHCs to the

remotest

villages

7,99 0 100 0 14,40

X4 The

proportion of

PHCs with

24-hour

electricit

87,08 0 100 97 21,80

X5 The

proportion of

remote PHCs

district/cit

23,67 0 100 11 28,86

X6 The

proportion of

PHCs with

cold chains

68,70 0 100 75 24,08

X7 The

number of

ulation

52164

8,90

138

5965

410

276

597

64783

6,40

X8 The 2020

GRDP per

capita

(x1000)

54538

,93

685

7537

387

62366

,84

Table 1 reveals that, out of 512 regencies/cities,

the average number of persons who have gotten the

Covid 19 booster vaccination is 98399.41, with a

minimum of 21 people and a high of 1476072, with a

standard deviation of 167195.1. The Number of PHCs

varies greatly, ranging from 3-101 PHCs with a

median of 17 PHCs with a median number of

physicians, nurses, and midwives (health

professionals) PHCs in districts/cities with as many

as 475 people, ranging from 40-2175 people.

The proportion of PHCs with travel time from

PHCs to the remotest village of more than 3 hours,

PHCs with 24-hour electricity, remote PHCs, and the

proportion of PHCs with cold chain availability from

storage until the vaccination spot ranges from 0 to 100

percent. The median percentage of PHCs with travel

duration from PHCs to the remotest village of more

than 3 hours (0 %) and the proportion of remote PHCs

Characteristics of PHCs, District’s Population, GRDP per Capita, and Covid-19 Booster Vaccination Coverage in Indonesia: Negative

Binomial Regression Model

169

(11 %) indicate that access to the community is

limited in the coverage of the Covid 19 immunization.

The median percentage of health facilities with 24-

hour electricity availability (97%) and good cold

chain availability (75 %) is, on the other hand,

relatively high. Similarly, the range of population

(13879-5965410) and GRDP per capita (6851-

753710) is reasonably comprehensive, with a

significant standard deviation. So, the data look varies

greatly

2.5 Identification of Distribution

First, identify the distribution of the Y response

variable using the histogram. Based on figure 2, the

distribution of the Y response variable is not a bell-

shaped curve (normally distributed) but right-skewed.

Count data numbers identified as having a Poisson

distribution (Maxwell et al., 2018). Identifying

whether or not the bound variables follow the Poisson

distribution is necessary. This distribution test used

the Kolmogorov-Smirnov Test with a significance

threshold of 0.05. The test findings indicated that the

data for the Y variable was not Poisson distributed (P

value < 0.005) (Table 2). Hence a negative binomial

regression model was used for the GLM test.

2.6 Multicollinearity Test

Multicollinearity appears when two or more

independent variables in the regression model are

correlated. A little multicollinearity can occasionally

result in significant issues, but when it is moderate to

high, it becomes a problem that needs to be addressed

(Daoud, 2017). We use the variance of inflation factor

(VIF) to identify multicollinearity. We omitted the

Figure 2: Histogram of Y variable.

Table 2: Kolmogorov Smirnov test.

The number of the

ulation receivin

a Covid 19 vaccine booster Result test

N 511

Poisson Paramete

Mean 98399,41

Most Extreme Differences

Absolute 0,742

Positive 0,742

ative -0,254

Kolmo

orov-Smirnov Z 16,766

Nilai P 0,000

Table 3. Multicollinearity Test.

Variable VIF

X1 The number of PHCs

4,96

X2 The number of doctors, midwives, and nurses in PHCs

4,29

X3 The proportion of PHCs with a travel time of more than 3 hours from PHCs to the remotest villages

3,81

X4 The proportion of PHCs with 24-hour electricity

3,77

X5 The proportion of remote PHCs by district/city

2,46

X6 The proportion of PHCs with cold chains

1,92

X7 The Number of Population

1,78

X8 The 2020 GRDP per capita (x1000) 1,03

ICSDH 2022 - The International Conference on Social Determinants of Health

170

Table 4: Modelling using negative binomial regression predicts factors affecting the population receiving a Covid-19 booster

vaccine.

Variable Coef (95% CI)

Standard

error

Pr (>|z|)

X1 The number of PHCs 1x10

-3

0,009 0,894

X2 The number of doctors, midwives, and nurses in PHCs 4.8x10

-4

0,000 0,019

X3 The proportion of PHCs with a travel time of more than 3 hours from PHCs

to the remotest villages

-7x10

-3

0,004 0,097

X4 The proportion of PHCs with 24-hour electricity 8x10

-3

0,004 0,049

X5 The proportion of remote PHCs by district/city -6x10

-3

0,002 0,010

X6 The proportion of PHCs with cold chains 1,2x10

-2

0,004 0,001

X7 The Number of Population 1x10

-6

0,000 0,000

X8 The 2020 GRDP per capita (x1000) 3x10

-6

0,000 0,000

Constant 8,39 0,329 0,000

variable if the variable exhibits multicollinearity (VIF

> 10). Table 3 demonstrates that all independent

variables have a VIF value of 10, indicating no

multicollinearity in all variables. So, we included all

variables in the ensuing study.

2.7 Generalized Linear Model (GLM)

GLM modelling will use a negative binomial

distribution with a link function (log). The results

demonstrated that the number of doctors, nurses, and

midwives in PHCs, the proportion of PHCs with 24-

hour electricity, remote PHCs, and PHCs with cold

chain availability, as well as the population and

GRDP per capita (x1000), had a significant impact on

the coverage of the Covid 19 booster vaccination

(Table 4).

The number of doctors, nurses, and midwives at

the PHCs, the proportion of PHCs with 24-hour

electricity, the Number of PHCs with a cold chain, the

district population, and the GRDP per capita (x1000)

have a positive impact. It indicates that the coverage

of the Covid 19 booster vaccine increases as the

number or fraction of variables increases. On the

other hand, the proportion of remote PHCs and the

fraction of PHCs with more than a three-hour travel

time to the most distant villages had a negative effect

on vaccination coverage for Covid 19 boosters. The

higher the proportion, the lower the vaccine coverage.

The GLMs Model for Negative Binomial

Regression distribution summarized:

μ = Exp (8.39 x10

+ 4.8x10

-4

+ 8x10

-3

−

6x10

-3

+1,2x10

-2

+ 1x10

-6

+3x10

-6

)

The study found that the number of doctors,

nurses, and midwives at the PHCs positively impacts

COVID-19 booster vaccination. This finding was still

consistent with previous studies. The number or

density of health workers affects the health indicators

outcome such as life expectancy (Nguyen et al.,

2016), immunization coverage (Mitchell et al., 2008),

neonatal mortality (Sousa et al., 2013) and skilled

birth attendant (Mayhew et al., 2008). Health workers

are an integral part of the health system and play an

essential role in achieving effective healthcare

delivery. Health workers with adequate quantity and

quality are essential because they directly affect the

quality of health services (World Health

Organisation, 2013, Ghosh, 2014).

The cold chain is used to keep vaccinations in

potent condition during storage. It is also known as

the supply chain for vaccinations or the immunization

supply chain. From the point of manufacture to the

point of administration, the cold chain comprises

several linkages intended to keep vaccines within the

temperatures specified by the WHO (2-8

C). Health

facility freezers may be powered by electricity, solar

energy, or gas to remain at the recommended

temperature. A refrigerator for a medical facility

should be chosen based on the most dependable

power source (World Health Organization

Department of Immunization, 2015). So, it was

important that the availability of a cold chain and

electricity as a power source in delivery services for

COVID-19 booster vaccination.

The proportion of remote PHCs by district/city

and PHCs with a travel time of more than 3 hours

from PHCs to the remotest villages negatively

impacts Covid-19 booster vaccination. Both are

indicators of access to transport the Covid-19 vaccine

booster and to deliver the vaccine to the receivers.

Access will make it difficult for officials to provide

covid 19 vaccine boosters. This result remains

consistent with earlier research, which showed that

accessing PHCs is a major obstacle to ensuring

immunization services.(Ayeni et al., 1987, Al-Taiar

et al., 2010, Hierink et al., 2021, Nainggolan et al.,

2016). GRDP per capita is one of the indicators of

Characteristics of PHCs, District’s Population, GRDP per Capita, and Covid-19 Booster Vaccination Coverage in Indonesia: Negative

Binomial Regression Model

171

the success of development implementation that can

be used as a macro benchmark is economic growth

(Romhadhoni et al., 2019). GRDP at the prevailing

price describes the added value of goods and services

calculated using the price per current year, while

GRDP based on constant prices shows the added

value of goods and services calculated using the

prevailing price in one year as the base year (Badan

Pusat Statistik RI, 2021). This study found that GRDP

per capita affects Covid-19 booster vaccination

success in districts/cities in Indonesia.

3 CONCLUSION

The study reveals that Java and Bali are regions with

the highest number of people who have received

Covid 19 booster vaccines. The number of doctors,

nurses, and midwives at the PHCs, the proportion of

PHCs with 24-hour electricity, the proportion of

PHCs with a cold chain, the district population, and

the GRDP per capita (x1000) have a positive impact.

On the other hand, the proportion of remote PHCs and

the fraction of PHCs with more than a three-hour

travel time to the most distant villages had a negative

effect on vaccination coverage for Covid 19 boosters.

Improvement in the human health workforce, 24-

hour electricity, cold chain availability, accessibility

to PHCs, and the villages are essential factors in

increased coverage

ACKNOWLEDGEMENTS

We would like to thank the Head of NIHRD MoH for

allowing the author to analyze the 2019 Health

Facility Research (Rifaskes) data. We also would like

to thank Drs. Max Joseph Herman Apt., M.Kes for

guiding me in writing this article

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