Clinical Features, Surveillance and Determinant Factors of Malaria

Outbreak in Usili Village, Southwest Papua, Indonesia

Andrew Ivan Humonobe

, Nadiyah Kamilia

and Sintong Halomoan Sianturi

Faculty of Medicine, University of Papua, Aimas-Klamono Street Km.28, Sorong Regency, Indonesia

Keywords: Malaria, Risk Factor, Surveillance.

Abstract: Malaria is a global health concern including in Indonesia. In early 2023, Sorong Regency Health Office

reported that there was a malaria outbreak in Usili Village, Southwest Papua, Indonesia. A total of 38 cases

were confirmed positive by rapid diagnostic test (RDT) and microscopic examination. This study aimed to

describe clinical features, surveillance system and determinant factors that contributed to the outbreak. A mix-

method study was conducted. Clinical features and surveillance were designed with qualitative descriptive,

while determinant factors of malaria used case control design. A total of 144 individuals were participated in

this study. Results showed that 68.7% of the case was uncomplicated malaria. Risk factor analysis illustrated

that working status (OR 2.1; 95% CI 1.005-2.765), night outdoor activities (OR 1.6; 95% CI 0.650 - 1.805),

using mosquito net (OR 1.9; 95% CI 0.005-2.765) were contributed to the case. Surveillance variables

revealed incomplete data reporting, unreliable data collection, and a postponed epidemiology investigation.

We draw conclusion that the majority cases of this outbreak were clinically uncomplicated malaria. The

outbreak was linked to determinant factors like outdoor activities, mosquito net use, and working status. This

study also emphasizes the shortcomings of the surveillance system in preventing malaria.

1 INTRODUCTION

Malaria is one of infectious disease affects globally

which lead to increasing morbidity and mortality.

World Malaria Report 2022 released by WHO states

that there were 247 million cases of malaria in 2021,

which resulted in 619,000 deaths (WHO, 2022). Nine

countries in South-East Asia are endemic for malaria,

which accounts for 22% of malaria deaths and 15.6%

of cases in the region. With an estimated 811,636

cases, Indonesia has the second-highest number of

malaria cases in this region. (WHO, 2022, 2021b).

Annual malaria report showed that there was an

increasing malaria incidence in 2021 roughly up to

thirty percent giving escalated number from 304,607

to 400,253 positive cases in 2022, dominantly

occurred in Papua region which contributed to 90%

of national total cases (Indonesian Ministry of Health,

2023).

https://orcid.org/0009-0008-3043-5466

https://orcid.org/0009-0003-1964-1203

https://orcid.org/0009-0004-4049-7290

Malaria is a parasitic infection of Plasmodium sp

in human. This parasite is transmitted by the

Anopheline mosquito that leads to acute-life

threatening disease (Buck and Finnigan, 2023).

Human infection is known to occur in five

Plasmodium species including P. vivax, P.

falciparum, P. ovale, P. knowlesi, and P. malariae

(Buck and Finnigan, 2023; Tangpukdee et al., 2009).

Furthermore, P. falciparum and P. vivax were the

most prevalent species in Indonesia (Bria et al.,

2021). Malaria transmission is a complex web of

interactions exists between human hosts, Anopheline

mosquitoes, Plasmodium parasites, and local socio-

ecological conditions (Dhewantara et al., 2019).

Malaria risk factors include the use of mosquito nets,

spraying insecticide, age, home construction

conditions, altitude and weather conditions (Mosha et

al., 2020; Sulistyawati et al., 2020).

Typical malaria symptoms are chills, fever and

sweating. Other additional manifestation include

headache, dizziness, vomiting, diarrhea, muscle pain,

Humonobe, A. I., Kamilia, N. and Sianturi, S. H.

Clinical Features, Surveillance and Determinant Factors of Malaria Outbreak in Usili Village, Southwest Papua, Indonesia.

DOI: 10.5220/0013660400003873

Paper published under CC license (CC BY-NC-ND 4.0)

In Proceedings of the 1st International Conference on Medical Science and Health (ICOMESH 2023), pages 55-61

ISBN: 978-989-758-740-5

lack of appetite, weakness, jaundice and even

impaired consciousness. Malaria can, however,

occasionally present with no symptoms (Bartoloni

and Zammarchi, 2012; Bria et al., 2021).

The primary method for identifying malaria is by

examining microscopic findings, which is considered

the gold standard. However, there are instances where

misdiagnosis can occur, particularly when the level of

parasites in the blood is low or if the identification of

the specific species is incorrect (Kang et al., 2017;

Tangpukdee et al., 2009). To address this issue, rapid

diagnostic testing (RDT) is frequently employed in

isolated locations with limited access to microscopic

examination expertise (Diallo et al., 2017;

Tangpukdee et al., 2009).

Indonesia's malaria control program goal is to

completely eradicate malaria by 2030, achieving

malaria-free status gradually. Three primary actions

were to raise annual blood examination rate (ABER)

and decrease the annual parasite incidence (API) and

positivity rate (PR). The actions done are to certify

each region for malaria, beginning at the district or

city and moving up to the province, region, and

national levels. These indicate that the following

conditions must be met in order: (1) no indigenous

cases for three years in a row; (2) PR of less than 5%;

and (3) API of less than 1 per 100,000 (Herawati et

al., 2023).

Malaria surveillance program starts with patient

registration and data collection at Public Health

Center (PHC). Next, PHC generates monthly reports

on malaria based on the disease's cases it has

identified and its outpatient services. PHC is in charge

of data analysis and the production of a report on the

trends and distribution of the disease for the local

area. PHCs notify the district malaria control officer

of any malaria-related issues, and the officer compiles

the data into district health profile. Details regarding

the annual and monthly cases of malaria reported at

village level are included in the health profile. Then,

sub-directorate of malaria control at directorate of

vector-borne diseases in Indonesian Ministry of

Health receives aggregated malaria reports from the

district health office, which then forwards them to the

provincial health office three times a year (Elyazar et

al., 2011).

In early 2023, unpublished data from Sorong

Regency Health Office reported that there was a

malaria outbreak in Usili Village as many as 38

positive cases, whereas previously there had been no

history of malaria infection in the region. The

transmission scheme was indigenous. Usili Village is

located in Aimas District, Sorong Regency,

Southwest Papua Province and positioned in the

working area of Malawili PHC under Sorong

Regency Health Office supervision. This study aimed

to describe clinical aspects, implementation of

surveillance system and other risk factors that play

role in the emergence of malaria outbreaks in Usili

Village, Southwest Papua.

2 METHOD

This study was designed with a mixed method.

Clinical features and surveillance variables were

observed with qualitative descriptive design using

interview and secondary data analysis. Surveillance

data was collected from Malawili PHC and Sorong

Regency Health Office. In order to depicting

determinant factors of malaria, an analytical study

with a case-control design with a ratio of 1:2 was

used. Data collection was carried out from primary

and secondary source. Secondary data was taken from

Malawili PHC medical record. Meanwhile, primary

data was obtained by using questionnaire. A total of

144 participants from Usili Village was recruited in

this study, 48 people were grouped as positive malaria

(confirmed through microscopic finding or RDT from

medical record) while 96 people were control.

Samples were selected using the stratified random

sampling.

3 RESULT

3.1 Clinical Features

Malaria can present with a range of clinical

manifestations, from mild to potentially fatal. During

the interviews with all of the positively confirmed

participants, symptoms such as fever, chills,

headache, muscle soreness, nausea, vomiting,

diarrhea, weakness, jaundice, and seizures were

reported. In Figure 1, it is clear that all (100%)

responders who tested positive for malaria had fever.

Additionally, a majority of these individuals

experienced chills (93.75%) and nausea (87.5%).

Other reported symptoms included headache

(68.75%), muscle pain (41.67%), weakness

(35.42%), vomiting (25%), diarrhea (20.83%),

seizures (22.92%), and jaundice (8.33%). By

considering the last set of clinical symptoms, it is

reasonable to assume that approximately 31.25%

(corresponding to 15 individuals) had severe malaria,

while the remaining 68.75% (33 individuals)

exhibited uncomplicated malaria.

ICOMESH 2023 - INTERNATIONAL CONFERENCE ON MEDICAL SCIENCE AND HEALTH

Figure 1: Clinical symptoms of malaria positive

respondents

3.2 Surveillance System Management

Planning, implementing, and evaluating public health

practices are all part of public health surveillance,

which is reliant on ongoing, systematic data

collection, analysis, and interpretation (Centers for

Disease Prevention and Control, 2022).

Although Malawi PHC has carried out malaria

surveillance in accordance with national guidelines,

there are some inconsistencies in the way it has been

carried out. Table 1 shows this in the following.

Table 1: Surveillance data taken from Malawili PHC.

Surveillance data Result

Data simplicit

Easy to get

Data

ualit

Incom

lete data

Data stabilit

Unreliable

System

representation

Distributed over all

opulation

ortin

schedule Dela

ed investi

ation

3.3 Analysis of Malaria Determinant

Factors

Table 2 shows the characteristics of respondents and

the type of infection in the people of Usili Village. In

total, 144 people were included in the study. The type

of infection that occurs is mostly by P. vivax

(45.83%), P. falciparum (37.5%) and mixed malaria

(16.67%). Based on the characteristics of the

respondents, the most (70.83%) are those who are

actively working, 36.11% are not in school, 63.89%

have nighttime activities outside the home, and

51.39% do not use mosquito nets while sleeping.

Table 2: Type of malaria and characteristic of participants

in Usili Village.

Characteristics

Frequency

(

)

Percentage

(

)

Type of malaria

infection

P. falciparum 18 37.50

P. vivax 22 45.83

Mixed malaria 8 16.67

Working

Yes 102 70.83

No 42 29.17

Education level

Not joining school 52 36.11

Primary school 40 27.78

Middle school 15 10.42

High school 20 13.89

University 17 11.81

Night outdoor

activities

Yes 92 63.89

No 52 36.11

Use of mosquito nets

Yes 70 48.61

No 74 51.39

Table 3 presents working status factor and the

occurrence of malaria. Out of the 48 individuals who

have malaria, 50% of them are engaged in some form

of work, while the remaining 50% are not employed.

Interestingly, among those who work, 81.91% do not

have a malaria infection. Statistical results indicate a

significant association between people who do not

work and experience malaria (p = <0.001; OR CI 95%

= 2.1 (1.005-2.765). These findings indicate that the

risk of contracting malaria is 2.1 times higher in non-

working individuals than in working individuals.

Table 3: Bivariate analysis of working status and malaria

incidence.

Working Malaria infection p

Positive Negative

Yes

(

50.0%

)

(

81.91%

)

<0.001

OR 2.1

95%

CI(1.005-

2.765)*

(50.0%)

(18.11%)

*chi-square analysis

Clinical Features, Surveillance and Determinant Factors of Malaria Outbreak in Usili Village, Southwest Papua, Indonesia

Table 4 describes education level factor and

malaria incidence in Usili Village. Based on the data

of respondents who are positive for malaria, 18.75%

are individuals who are not in school, 27.08% are

only up to primary education, 41.67% are only up to

secondary education, 6.25% are high school and

6.25% are up to higher education. There was no

significant result displayed in the statistical analysis

(p=0.114; OR 1.308 95% CI=(1.005-1.702)) although

the odds ratio showed that people with low education

level were at risk 1.308 times higher than individuals

with high levels of education.

Table 4: Bivariate analysis of education level with malaria

incidence.

Education level Malaria infection p

Positive Negative

Not joining

school

(

18.75%

)

(

44.79%

)

0.114

OR 1.308

95%

CI(1.005-

1.702)*

Primary school

(27.08%)

(28.12%)

Middle school

(41.67%)

(12.5%)

High school

(

6.25%

)

(

17.7%

)

University

(

6.25%

)

(

4.16%

)

*chi-square analysis

According to the data shown on Table 5, up to

52.08% (25 persons) of those who engage in

nighttime activities outside of their homes suffer from

malaria, compared to 47.91% (23 people) of those

who do not. There is a statistically significant

correlation (p<0.05) between malaria incidence and

activity during the night. These findings also indicate

that there is a 1.6-fold increase in the risk of malaria

infection in individuals who are active at night.

Table 5: Bivariate analysis of night outdoor activities with

the incidence of malaria.

Night outdoor

activities

Malaria infection p

Positive Negative

Yes

(52.08%)

(69.79%)

0.037

OR 1.6

95%

CI(0.650-

1.805)*

(47.91%)

(30.20%)

*chi-square analysis

The chance of contracting malaria while using

mosquito nets is shown in Table 6. Considering the

findings of this research, 70.83% of the population

who do not use mosquito nets are at risk of

developing malaria, while 29.16% of people who use

less mosquito nets are affected by malaria. Significant

findings from statistical analysis indicated a

relationship between the prevalence of malaria and

the use of mosquito nets (p=0.001). This research also

shows that sleeping without a mosquito net increases

the risk of malaria by 1.9 times compared to sleeping

with one.

Table 6: Bivariate analysis of mosquito net use activities

and malaria incidence.

Mosquito

net usage

Malaria infection

Positive Negative

Yes

(29.16%)

(58.33%)

0.001

OR 1.9

95%CI(0.005-

2.765)*

(70.83%)

(41.67%)

*chi-square analysis

4 DISCUSSION

The objectives of this study were to look into a

malaria outbreak, characterize clinical characteristics,

identify risk factors, and look into health centers'

malaria surveillance.

Diagnosing malaria can be challenging. In this

study it appears that all patients experienced common

symptom such as fever. Based on WHO guidelines all

patients who have a fever or a history of fever can be

suspected of being infected with malaria.

Nevertheless, diagnosis based only on clinical

symptoms has low specificity and can lead to

overtreatment. To get a definite diagnosis should be

carried out further examination of microscopic blood

smear examination or with RDT (WHO, 2023). It was

discovered that the majority of the malaria outbreaks

in Usili Village were caused by P. vivax infection,

with P. falciparum infections coming in second and

mixed malaria making up the least. According to the

clinical symptoms reported, the majority of cases of

malaria experienced by residents of Usili Village in

this study are uncomplicated cases with a lesser

number of severe cases. This finding is in accordance

with the results that appear that most malaria

infections that occur in the village is caused by

P.vivax (45.83%). Additionally, P. vivax tends to

cause less parasitemia and is less severe than P.

falciparum, according to the study (Baird, 2013;

Menkin-Smith and Winders, 2023). Although no

additional laboratory tests were performed in these

cases, the severe malaria cases in this study were

based on those who experienced symptoms such as

ICOMESH 2023 - INTERNATIONAL CONFERENCE ON MEDICAL SCIENCE AND HEALTH

jaundice and seizures (Bartoloni and Zammarchi,

2012).

In this study, we use five attributes—data quality,

data stability, system representativeness, timeliness,

and surveillance system simplicity—to evaluate the

surveillance success indicators based on WHO

guidelines (Centers for Disease Prevention and

Control, 2022; Nsubuga et al., 2006). System

simplicity can be assessed by looking at indicators

such as ease of defining cases, collecting, recording

and reporting data. The case definition used by the

Malawili PHC is in accordance with the national

guidelines for malaria management, where case

enforcement is based on anamesis and laboratory

diagnosis. It is necessary to underline the importance

of correct diagnosis in establishing the definition of

malaria cases (Indonesian Ministry of Health, 2019;

WHO, 2023). Malawili PHC is already good at

screening and confirmation activities.

The validity and completeness of the data entered

into the epidemiological surveillance system can be

used to assess the quality of the data. Data is said to

be complete if the confirmation case is filled in

completely and validates to health facilities that

report confirmed cases of malaria (Centers for

Disease Prevention and Control, 2022). The data

available in the Malawili PHC surveillance are

monthly situation monitoring, individual basic data

reports, monthly reports by summing patients/deaths

including some basic eradication/countermeasures

activities every month to the Sorong Regency Health

Office. However, the weakness in the presentation of

the data is made data stratification determination of

malaria endemicity sub-district level by making the

sub-district table by summing malaria patients. This

is similar to the results of other studies in the Kupang

area which found that PHC in Kupang regency had

not made an endemicity map of the area, so it was

considered incompatible with the role of PHC as a

work unit in the surveillance system to carry out

malaria in the working area by village (stratification)

annually where the dissemination of information by

the health office and PHC was only in the form of

reports, and did not publish malaria

bulletins/magazines. Cross-organizational meetings

have also not been conducted by the health office.

This is not in accordance with the role of the

district/city health office as a work unit in the

surveillance system, namely distributing malaria

information across programs and across sectors

(Desita et al., 2021).

Data stability relates to the reliability and

availability of surveillance system. The capacity to

accurately gather, arrange, and deliver data is known

as reliability. Conversely, availability is the capacity

to function when called upon (Centers for Disease

Prevention and Control, 2022). The Malawili PHC

does not report weekly and monthly cases to the

Sorong Regency Health Office. Data is received if

there are cases from outbreak reports and new cases

resulting from epidemiological investigations in the

field. The Sorong Regency Health Office does not

receive individual case data directly from Malawili

PHC unless the PHC diagnoses individual data from

referral health facilities. This is related to the absence

of cross-sector involvement to carry out laboratory

and RDT examinations in primary care facilities

around the Aimas district. Surveillance data stability

refers to the consistency and reliability of data

collected through a health surveillance system.

Reporting consistency factors influencing the

stability of surveillance data at the Malawili PHC are

consistency in reporting data to responsible entities,

such as health office, ensuring that information is

available and can be used for decision making and

then related to officer training where officers must

know that one of their duties is to improve networking

with health service facilities.

Timeliness of surveillance refers to the extent to

which surveillance data is collected, analyzed and

reported according to a predetermined schedule. This

timeliness has a pivotal role in surveillance system

effectiveness and response to public health issues.

Case reporting confirm malaria according to

agreement that is less than 24 hours, while reporting

case recaps per sub-district by the public health center

that is the 4th of every month and agreed to require a

maximum of 24 hours to immediately carry out an

epidemiological investigation (Centers for Disease

Prevention and Control, 2022; Indonesian Ministry of

Health, 2019). However, when the observation was

carried out there was a delay in the report from the

hospital and more than 24 hours was needed to carry

out an epidemiological investigation.

The risk factors most associated with malaria

cases in Usili Village are night outdoor activities,

working status and using mosquito nets. Outdoor

activities at night can have implications for the risk of

malaria transmission, especially since the Anopheles

mosquito, which is a vector of the disease, is

generally active at night, especially at dusk and early

morning. A person's chance of contracting malaria is

increased if they spend a lot of time outside at night

in an area where Anopheles mosquito vectors are

common (Sulistyawati et al., 2020). This result is in

line with other studies conducted at the Remu PHC in

Sorong City with a sample of 84 people where it was

found that there was a significant relationship

Clinical Features, Surveillance and Determinant Factors of Malaria Outbreak in Usili Village, Southwest Papua, Indonesia

between nighttime activities outside the home with

the risk of malaria infection (OR 3.411 95% CI

(1.363-8.542)). This is due to the activities of people

who often go out of the house at night, namely people

who have a lot of work so they come home at night,

as students who study at night, as motorbike taxi

drivers, mothers who do social gatherings or who

worship at night. and most groups of young people,

even children, who often hang out, walk around and

date at night, without wearing protective clothing

such as jackets/long sleeves/trousers/sarongs can be

at risk of contracting malaria (Papilaya et al., 2015).

The factor of not using mosquito nets also

contributed to the malaria

outbreak in Usili Village. This result is in line with

research observed at Remu PHC Sorong City, where

based on bivariate analysis conducted, there is a

connection between the risk of getting malaria and the

use of mosquito nets. (p=0.042; OR 2.562 95% CI =

1.025-6.406)) (Papilaya et al., 2015). Based on

interviews with respondents in the positive malaria

group and the control group said that although they

had been given mosquito nets from health workers

from the Malawili PHC, they did not use them

because they felt uncomfortable and the room became

hot when using mosquito nets at night and there were

even those who sold and discarded mosquito nets that

had been given.

This study also found that working status have a

relationship with malaria incidence. We observed that

people who do not work typically spend their time

doing household chores and running the risk of

contracting malaria. People who are less active for a

long time at home have a high chance of exposure to

Anopheles mosquitoes. This is because the houses of

Usili Village residents are located in a swamp area

which is a suitable condition for the breeding of

anopheles mosquitoes as malaria vectors (Munthe et

al., 2022).

5 CONCLUSIONS

The first malaria outbreak in Usili Village, Southwest

Papua was contributed by several weaknesses in the

surveillance system and malaria prevention

programs, including: shortcomings of surveillance

system at the Malawili PHC in recording and

reporting incomplete cases, unreliable data collection

and management, furthermore receiving and sending

case reports was not on time. In addition, there are

still many residents who are not aware of malaria

prevention that are seen in the sub-optimal use of

mosquito nets, high outdoor activity at night and

passive activities in the house also become risk

factors in this malaria outbreak.

In an effort to improve malaria control so that

similar outbreaks do not occur in Sorong regency, the

quality of reporting must be supported by effort to

strengthen networks that are integrated with health

offices and health facilities. Vector control programs

also need careful supervision such as submitting

procedures and circulars on clear duties related to the

implementation of health promotion on intensive

malaria prevention.

ACKNOWLEDGEMENTS

We are grateful for the research grant from Lembaga

Penelitian dan Pengabdian kepada Masyarakat

(LPPM) University of Papua.

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