In the Lao PDR, malaria morbidity and mortality have remarkably decreased over the past decade. However, asymptomatic infections in rural villages contribute to the on-going local transmission. The primary objective of this study was to explore the characteristics of infections in a malaria-endemic district of the Lao PDR. The specific objectives were to investigate the prevalence and species of malaria parasites using molecular methods and to assess individual and household parasite levels and the characteristics associated with malaria infection.
Methods
The study population included 870 participants from 236 households in 10 villages of the Xepon district. Interviews, blood examinations and body temperature measurements were conducted between August and September 2013. A multilevel logistic regression model, with adjustment for clustering effects, was used to assess the association between predictor variables and an outcome variable (malaria infection status as principally determined by PCR). The predictive factors included individual-level factors (age, gender, past fever episode, and forest activity during night time) and household-level factors (household member size, household bed net usage/density and a household with one other malaria-infected member).
Results
Fifty-two participants (including 26 children) tested positive (positive rate: 6.0 %): Plasmodium falciparum mono-infection was the most common infection (n = 41, 78.8 %), followed by P. falciparum and Plasmodium vivax mixed infections (n = 9, 17.3 %). The majority of infected participants (n = 42, 80.8 %) had no fever episodes in the two previous weeks or a measurable fever (>37 °C) at the time of survey. Living in a household with one other malaria-infected member significantly increased the odds of infection (odds ratio 24.33, 95 % confidence interval 10.15–58.32). Among the 40 households that had at least one infected member, nine households were responsible for 40.4 % of the total infections.
Conclusions
Plasmodium vivax was detected more frequently than it was reported from the district hospital. Most infections were asymptomatic and sub-microscopic and were highly clustered within households. To further eliminate malaria in Xepon and other similar settings in the country, the National Malaria Control Programme should consider household-based strategies, including reactive case detection targeting the household members of index cases.
Keywords
Asymptomatic infectionsSub-microscopic infectionsMalaria eliminationActive case detectionLaos
Background
The Lao People’s Democratic Republic (Lao PDR) is a lower-middle-income country in Southeast Asia, bordering Thailand, Vietnam, Cambodia, China, and Myanmar. The population of the Lao PDR was 6.5 million in 2015, and 63 % of the population lives in rural areas [1]. The predominant crop is rice, and 71 % of the households in the nation were engaged in rice farming in 2010/11 [2]. Two-thirds of the land is either hilly or mountainous. The Lao PDR has a monsoonal climate. The rainy season extends from April to November, with peak rainfall in July and August [1, 2]. Although malaria cases are reported from healthcare facilities throughout the year, there is an increase in the number of reported cases during the rainy season [3]. Plasmodium falciparum has long been the predominant reported infection, but the incidence Plasmodium vivax has been increasing in recent years [4]. The groups most at risk of malaria infection in the Lao PDR include ethnic minorities, forest fringe inhabitants, migrant workers, and new forest settlers [3, 5, 6]. People in these groups live or work in the forest where they are engaged in cropping, hunting, collecting of forest products, mining, or constructing roads/dams. Therefore, their exposure to vector mosquitoes is increased. Among the ethnic minorities and forest fringe inhabitants, adult males in particular report that they stay in the forest overnight to hunt or collect wood [3, 7, 8].
Since 1992, the Lao PDR has implemented a nationwide malaria control programme [8]. The current malaria control strategies emphasize the promotion of long-lasting insecticide-treated bed nets, early diagnosis by microscopic examination and rapid diagnostic tests (RDT), and prompt treatment with an artemisinin-based combination therapy (ACT). Since 2008, the use of ACT has gradually been scaled-up to cover the whole public health sector, including village health volunteers (community health workers) and some businesses in the private sector, including registered private pharmacies [3].
As a result of the above-mentioned control efforts, there have been remarkable decreases in the rates of mortality and morbidity due to malaria over the past decade. The Lao PDR achieved a >75 % decrease in the incidence of malaria between 2000 and 2015. However, asymptomatic infections continue to be detected in rural, remote villages [7, 9, 10], which contributes to on-going local transmission. Asymptomatic carriers are not detected by the current national surveillance system, which is based on passive case detection at healthcare facilities. Furthermore, patients who are symptomatic may not necessarily seek treatment from healthcare facilities in which the surveillance system is in place [11]. Thus, the actual intensity of malaria transmission in the communities of the Lao PDR remains poorly understood.
Artemisinin resistance has been increasingly reported from many areas in the Greater Mekong Sub-region (Lao PDR, Cambodia, Myanmar, Thailand, Vietnam, and China) [12], posing a threat to regional and global health security. In 2014, the Malaria Policy Advisory Committee to the World Health Organization reviewed the situation of the region, considered malaria elimination as technically and operationally feasible, and thus recommended that the regional countries adopt a goal of malaria elimination by 2030 [13]. The elimination strategies in each country should be based on the local epidemiology [13]. However, information is lacking to guide and implement elimination strategies in the Lao PDR. A community-based survey with a highly sensitive molecular measurement method [i.e., polymerase chain reaction (PCR)] can help to better understand the epidemiological trends in the communities of the Lao PDR.
Approximately 90 % of all malaria cases in the Lao PDR occur in the southernmost provinces including Savannakhet province [3]. Of the 15 districts in this province, malaria is endemic in four districts including Xepon district. Xepon district is a remote, rural district on the Vietnamese border, approximately 500 km from the national capital of Vientiane. Ethnic minorities comprise 75 % of the total district population, which is approximately 45,000. There are 88 villages in the district, most of which are at risk of malaria. According to the Xepon District Health Office, 163 malaria cases were reported from the Xepon District Hospital in 2013, including 151 cases of P. falciparum mono-infection, 10 cases of P. vivax mono-infection, and two cases of co-infections. In 2012, 218 cases were reported, including 214 cases of P. falciparum mono-infection and four cases of P. vivax mono-infection. Xepon district was deliberately selected because reliable demographic data were available on households in the 20 villages in which the Health and Demographic Surveillance System (HDSS) was in place. The HDSS has been described elsewhere [14].
The primary objective of this study was to explore the characteristics of individuals with malaria infections in a rural district of the Lao PDR. The specific objectives were (1) to investigate the prevalence and species of malaria parasites in the study community using molecular methods, and (2) to assess the individual- and household-level characteristics that are associated with malaria infection.
Methods
Study site and population
This study was conducted in Xepon district, Savannakhet province, Lao PDR between August and September of 2013. From the 20 villages where the HDSS was in place, 10 villages were deliberately selected as the study site based on the geographical characteristics of these villages: two hillside villages, five roadside villages, and three riverside villages were selected (Fig. 1). Data were collected from 870 participants in 236 households (participation rate: 47.8 %).
Fig. 1
Map of the study villages. Yellow pins indicate the locations of the study villages
Data collection
Data were collected through a cross-sectional, community-based survey. The survey consisted of interviews, blood examinations, and body temperature measurements. All of the villagers in each study village were invited to assemble at a designated place, including a primary school and village health volunteers’ houses. First, trained surveyors, who were nurses or medical technologists, identified a participant by his/her name, the name of his/her household head, and the household ID used for the HDSS. Second, surveyors conducted an interview using a pre-tested questionnaire. In the case of child participants, a guardian responded to the interview. Third, the surveyors measured the body temperature of the participants with a digital ear thermometer (MC-510, Omron Healthcare Co., Ltd., Kyoto, Japan). Finally, the surveyors collected a finger-prick blood sample from the participants that was subjected to RDT (SD Bioline Malaria Ag Pf/Pv, Standard Diagnostics, Inc., Gyeonggi-do, Republic of Korea). Blood samples were also collected for microscopic examination and PCR. For PCR, blood samples were collected on Whatman FTA Classic Cards (GE Healthcare Life Science, Little Chalfont, UK).
Laboratory procedures
Microscopy
Experienced medical technologists prepared Giemsa-stained thick and thin blood smears and performed the microscopic examinations.
DNA extraction and PCR
Deoxyribonucleic acid (DNA) was extracted from dried blood spots on the FTA Classic Cards with a QIAamp DNA Mini Kit (Qiagen, Hilden, Germany) in accordance with the manufacturer’s instructions. The extracted DNA was eluted with 50 µl of elution buffer in the kit and preserved at 4 °C until the PCR. The PCR was performed in two steps. First, screening by a real-time PCR was performed with genus-specific primers targeting the cytochrome b gene in the mitochondrial genome of Plasmodium parasites [15]. Second, Plasmodium species identification was performed with species-specific primer sets targeting the same gene by a nested PCR with DNA samples that were identified as being Plasmodium parasite-positive in the screening. SaoAdvanced Universal SYBR Green Supermix (Bio-Rad, CA, USA) was used for the screening PCR with 2 µl of the extracted DNA and the primer set (Additional file 1). When the melting temperature of the PCR product was between 76.5 and 78.5 °C, the sample was considered to be Plasmodium DNA positive.
For the PCR for species identification, primer sets for P. falciparum, P. vivax, Plasmodium malariae, and Plasmodium ovale were used. The primary PCR for the identification was conducted by the real-time PCR. The secondary PCR for the identification was conducted using a conventional PCR with the first PCR product diluted 1000 times with Go-Taq DNA Polymerase (Promega, Fitchburg, WI, USA). The PCR products were visualized by 2 % agarose gel electrophoresis with Gel Red (Biotium, Fremont, CA, USA) staining by UV transillumination. All of the DNA extraction procedures and PCRs were conducted at the National Center for Global Health and Medicine, Japan.
Factors and measurements
The primary outcome measure in this study was the malaria infection status principally determined by PCR. The secondary outcome measure was the malaria infection status determined by RDT and then confirmed principally by PCR, i.e., true positives detected by RDT. Malaria infection was defined by a positive test result with or without clinical symptoms. The individual-level predictive factors included age, gender, bed net use on the night before the survey, the experience of working in the forest at night during the previous month, and the experience of a fever during the two weeks prior to the survey. The age and gender variables were determined from the information in the HDSS database because the self-reported ages of the subjects at the study site might not have been reliable. Other variables were measured using a questionnaire-based interview. At the household level, the predictive factors included the number of household members, the person-per-net ratio (defined as the number of household members divided by the number of available bed nets in a household), and living in a household with one other malaria-infected member. These household-level factors were determined according to the information in the HDSS database and the results of a bed net observation survey that was conducted prior to the present study [16].
Statistical analysis
For the analysis of associations between the outcomes and predictive factors, multi-level modelling was used to account for the hierarchical structure of the data; i.e., 870 individuals in 236 households. Thus, a two-level, mixed-effects logistic regression model was used for the multivariate analysis. For the bi-variate analysis, a Chi square test or Fisher’s exact test was used. For other statistical analyses, a Chi square test or Fisher’s exact test was used for a categorical variable and a t test was used for a discrete variable. A p < 0.05 was considered statistically significant. All statistical analyses were performed with the STATA MP12 software program (StataCorp LP, College Station, TX, USA).
Results
Infection characteristics
Fifty-two participants were confirmed to have malaria parasitaemia: 49 individuals were confirmed by PCR and the other three individuals, whose filter paper samples were not available, were determined by microscopic examination (Table 1). The total positive rate was 6.0 % (52/870). Mono-infection with P. falciparum was the most common form of infection (n = 41, 78.8 %), followed by mixed infection with P. falciparum and P. vivax (n = 9, 17.3 %). RDT and microscopic examinations could only detect 36.7 % of the PCR-diagnosed infections.
Table 1
Blood examination results
n
%
Species
Total (n = 52)
P. falciparum mono-infection
41
78.8
P. vivax mono-infection
2
3.8
P. falciparum and P. vivax mixed infection
9
17.3
PCR positives (n = 49)
P. falciparum mono-infection
38
77.6
P. vivax mono-infection
2
4.1
P. falciparum and P. vivax mixed infection
9
18.4
Rapid diagnostic test positives (n = 21)
P. falciparum mono-infection
19
90.5
P. vivax mono-infection
1
4.8
P. falciparum and P. vivax mixed infection
1
4.8
Microscopic examination positives (n = 21)
P. falciparum mono-infection
18
85.7
P. vivax mono-infection
3
14.3
P. falciparum and P. vivax mixed infection
0
0.0
Sensitivity of rapid diagnostic test against PCR positives
Positive
18
36.7
Negative
31
63.3
Sensitivity of microscopic examination against PCR positives
Positive
18
36.7
Negative
31
63.3
Characteristics of individuals
Approximately one half of the participants (54.0 %) were female (Table 2), and nearly one half of the participants (47.2 %) were over 15 years of age. Bed net use was very common, and almost all of the participants (99.2 %) reported that they slept under a bed net on the night before the survey. Most of the participants did not work in the forests at night (96.2 %) and had not slept in forest/hut (99.1 %) over the past month. Most of the participants (91.8 %) had not experienced a fever episode in the past 2 weeks; this was also the case for the participants who showed positive test results (92.3 %). Most of the participants (77.2 %) did not have a measurable fever; this was also the case among the participants who showed positive test results (82.7 %).
Table 2
Characteristics of the participants
Characteristics
Total (n = 870)
Positive (n = 52)
Negative (n = 818)
n
%
n
%
n
%
Gender
Female
470
54.0
24
47.1
446
54.5
Male
400
46.0
27
52.9
373
45.5
Age (years)
<5
67
7.7
1
1.9
66
8.1
5–14
392
45.1
25
48.1
367
44.9
≥15
411
47.2
26
50.0
385
47.1
Slept under a bed net last night
Yes
863
99.2
52
100.0
811
99.1
No
7
0.8
0
0.0
7
0.9
Worked in forest at night in the past month
Yes
33
3.8
2
3.8
31
3.8
No
837
96.2
50
96.2
787
96.2
Slept in forest/hut in the past month
Yes
8
0.9
1
1.9
7
0.9
No
862
99.1
51
98.1
811
99.1
Fever episode in the past 2 weeks
Yes
71
8.2
4
7.7
67
8.2
No
799
91.8
48
92.3
751
91.8
Body temperature (°C)
≤37
682
77.2
43
82.7
629
76.9
>37
198
22.8
9
17.3
189
23.1
Mean (standard deviation)
36.8 (0.4)
36.8 (0.6)
36.8 (0.4)
The majority (n = 42, 80.8 %) of the participants with positive test results did not have a fever episode in the two weeks before the survey or a measurable fever (>37 °C) at the time of the survey (Additional file 2).
Demographic differences between participants and non-participants
There were significant differences in gender and age between the study participants and non-participants in the study villages (Table 3). The proportion of males was significantly lower in the participants than in the non-participants (46.0 vs 51.8 %, p = 0.012). The proportion of the population aged over 14 years of age was significantly lower in the participants than in the non-participants (47.1 vs 60.1 %, p < 0.001). There was also a difference in gender proportion between the participants and non-participants in the age subgroups: among the participants, males accounted for 53.7 % (36/67) in the youngest group, 49.7 % (195/392) in the second youngest group, and 40.6 % (167/411) in the oldest group. In contrast, among the non-participants, males accounted for 51.5 % (102/198) in the youngest group, 45.3 % (82/181) in the second youngest group, and 54.0 % (309/572) in the oldest group.
Table 3
Differences in demographic characteristics between participants and non-participants
Demographic characteristics
Total (n = 1821)
Participants (n = 870)
Non-participants (n = 951)
P valuea
n
%
n
%
n
%
Gender
Female
928
51.0
470
54.0
458
48.2
0.012
Male
893
49.0
400
46.0
493
51.8
Age (years)
<5
265
14.6
67
7.7
198
20.8
<0.001
5–14
573
31.5
392
45.1
181
19.0
≥15
983
54.0
411
47.1
572
60.1
aChi square test
Household characteristics
Most households (89.4 %) possessed at least one bed net (Table 4). According to the person-per-net ratio, 53.0 % of the households were considered to have a sufficient number of bed nets to cover all members of the household. Among the households with at least one infected person, 57.5 % of the households were considered to have a sufficient number of bed nets. The mean number of household members was 6.5.
Table 4
Characteristics of the households
Characteristics
Total (n = 236)
Households with at least one positive participant (n = 40)
Households without a positive participant (n = 196)
n
%
n
%
n
%
Possession of bed net
Yes
211
89.4
35
89.7
176
89.3
No
4
1.7
0
0.0
4
2.0
Unknown
21
8.9
4
10.3
17
8.6
Person-per-net ratio
≤2.5
125
53.0
23
57.5
102
52.0
>2.5 and <4.0
50
21.2
9
22.5
41
20.9
≥4.0 or no net
39
16.5
4
10.0
35
17.9
Unknown
22
9.3
4
10.0
18
9.2
Number of members
<5
57
24.2
8
20.0
49
25.0
5–7
104
44.1
17
42.5
87
44.4
≥8
75
31.8
15
37.5
60
30.6
Mean (standard deviation)
6.5 (2.8)
6.8 (2.5)
6.4 (2.9)
Among the 40 households with at least one infected member, nine households with at least two infected members were responsible for 40.4 % of the total infections (Additional file 3). Among these nine households, four households (44.4 %) had a sufficient number of bed nets. In contrast, among the 27 households that had only one infected member and that provided bed net data, 19 households (70.4 %) had a sufficient number of bed nets. Although there was a tendency for the households with two or more infected members to be less likely to have a sufficient number of bed nets as compared to the households with only one infected member, the difference (44.4 vs 70.4 %) was not statistically significant (p = 0.235 by Fisher’s exact test). Among the nine households that had at least two infected members, the mean number of members (standard deviation) was 7.2 (2.6), whereas it was 6.7 (2.5) among the 31 households that had only one infected member. The difference (7.2 vs 6.7) was not statistically significant (p = 0.574 by t test).
Village characteristics
With the exception of one village, all of the villages had at least one malaria-positive participant (Table 5). The malaria positive rate, which ranged from 0 to 20.8 %, differed markedly among the villages (median: 6.0 %). The highest positive rate was found in the riverside villages. However, infected individuals were also found in hillside and roadside villages.
Table 5
Characteristics of the villages
Village name
Number of participants
Number of participants who tested positive
Positive rate (%)
Hillside
Kalouk Mai
46
1
2.2
Kalouk Kao
115
12
10.4
Roadside
Kangkai Nok
98
6
6.1
Adone
121
7
5.8
Alang Yai
93
7
7.5
Alay Yai
66
0
0.0
Kangpear Nok
104
1
1.0
Riverside
Kangpear Nai
70
6
8.6
Alang Noi
53
11
20.8
Dong Noi
104
1
1.0
Factors associated with malaria infection
Association with malaria infection status principally determined by PCR
The bivariate analysis showed that living in a household with at least one additional infected member was significantly associated with malaria infection (p < 0.001) (Table 6). No other variables were found to be associated with malaria infection. Consistent with the results of the bivariate analysis, the multivariate analysis showed that living in a household with at least one additional infected member was significantly associated with an increased risk of infection (adjusted odds ratio 24.33, 95 % confidence interval 10.15–58.32) (Table 7).
Table 6
Bivariate analysis of associations between malaria infection status principally determined by PCR and predictive factors
Predictive factors
Positive rate (%)
P valuea
Individual level factors
Age (years)
<5
1.5
0.273
5–14
6.4
≥15
6.3
Gender
Female
5.1
0.240
Male
7.0
Working forest at night
No
6.0
0.984
Yes
6.1
Fever episode in the last 2 weeks
No
6.0
1.000
Yes
5.6
Household level factors
Person-per-net ratio
≤2.5
6.2
0.243
>2.5 and <4.0
7.2
≥4.0 or no net
3.2
Unknown
6.7
Number of household members
<5
7.6
0.689
5–7
5.4
≥8
6.0
Household with at least one additional infected member
No
3.8
<0.001
Yes
45.6
aChi square test or Fisher’s exact test
Table 7
Multivariate analysis of associations between malaria infection status principally determined by PCR and predictive factors
Predictor variables
Adjusted odds ratioa
Adjusted 95 % confidence interval
P value
Individual level variables
Age (years)
<5
1.00
Reference
5–14
5.17
0.59–45.10
0.137
≥15
4.42
0.51–38.52
0.179
Gender
Female
1.00
Reference
Male
1.14
0.60–2.17
0.689
Working forest at midnight
No
1.00
Reference
Yes
1.62
0.33–8.09
0.555
Fever episode in the last 2 weeks
No
1.00
Reference
Yes
1.32
0.39–4.46
0.656
Household level variables
Person-per-net ratio
≤2.5
1.00
Reference
>2.5 and <4.0
0.62
0.26–1.49
0.287
≥4.0 or no net
0.59
0.19–1.83
0.360
Number of household members
<5
1.00
Reference
5–7
0.67
0.27–1.68
0.390
≥8
0.68
0.24–1.92
0.469
Household with at least one additional infected member
No
1.00
Reference
Yes
24.33
10.15–58.32
<0.001
aAdjusted for age, gender, working in forest, fever episode, person per net ratio, number of household members, and at least one additional infected member
Association with malaria infection status determined by rapid diagnostic tests
The bivariate analysis showed that living in a household with at least one additional infected member was significantly associated with malaria infection (p < 0.001 by Fisher’s exact test) (Table 8). No other variables were found to be significantly associated with malaria infection. However, there was a tendency for the positive participants to be more likely to have reported a fever episode as compared to the negative participants (19.0 vs 7.9 %, p = 0.084 by Fisher’s exact test). Consistent with the results of the bivariate analysis, the multivariate analysis showed that living in a household with at least one additional infected member was significantly associated with an increased risk of infection (adjusted odds ratio 56.69, 95 % confidence interval 9.38–342.61) (Table 9).
Table 8
Bivariate analysis of associations between malaria infection status determined by rapid diagnostic tests and predictive factors
Predictive factors
Positive (n = 21)
Negative (n = 849)
Positive rate (%)
P valuea
n
%
n
%
Individual level factors
Age (years)
<15
14
66.7
445
52.4
3.1
0.196
≥15
7
33.3
404
47.6
1.7
Gender
Female
11
52.4
459
54.1
2.3
0.879
Male
10
47.6
390
45.9
2.5
Working forest at night
No
20
95.2
817
96.2
2.4
0.560
Yes
1
4.8
32
3.8
3.0
Fever episode in the last 2 weeks
No
17
81.0
782
92.1
2.1
0.084
Yes
4
19.0
67
7.9
5.6
Household level factors
Person-per-net ratio
≤2.5
8
38.1
412
48.3
1.9
0.230
>2.5 and <4.0
9
42.9
227
26.7
3.8
≥4.0 or no net
2
9.5
154
18.1
1.3
Unknown
2
9.5
58
6.8
3.3
Number of household members
<5
0
0.0
119
14.0
0.0
0.171
5–7
11
52.4
376
44.3
2.8
≥8
10
47.6
354
41.7
2.7
Household with at least one additional infected member
No
16
76.2
839
98.8
1.9
<0.001
Yes
5
23.8
10
1.2
33.3
aChi square test or Fisher’s exact test
Table 9
Multivariate analysis of associations between malaria infection status determined by rapid diagnostic tests and predictive factors
Predictor variables
Adjusted odds ratioa
Adjusted 95 % confidence interval
P value
Individual level variables
Age (years)
<15
1.00
Reference
≥15
0.52
0.18–1.46
0.211
Gender
Female
1.00
Reference
Male
1.01
0.38–2.69
0.990
Working forest at midnight
No
1.00
Reference
Yes
2.27
0.26–20.09
0.461
Fever episode in the last 2 weeks
No
1.00
Reference
Yes
2.54
0.73–8.87
0.143
Household level variables
Person-per-net ratio
≤2.5
1.00
Reference
>2.5 and <4.0
1.01
0.29–3.52
0.986
≥4.0 or no net
0.62
0.13–3.02
0.558
Number of household members
<8
1.00
Reference
≥8
0.38
0.10–1.42
0.152
Household with at least one additional infected member
No
1.00
Reference
Yes
56.69
9.38–342.61
<0.001
aAdjusted for age, gender, working in forest, fever episode, person-per-net ratio, number of household members, and at least one additional infected member
Discussion
As expected from the patient records of the Xepon District Hospital, the present community-based study showed that P. falciparum mono-infection accounted for the majority of infections (78.8 %). However, the results of the present study also showed a difference in the proportion of P. falciparum and P. vivax infections in the patient records and the present study: P. vivax infections including co-infections with P. falciparum, which were rarely reported from the hospital in 2012 (1.8 %) and 2013 (7.9 %), accounted for 21.2 % of the infections that were detected in the present study. This suggests that the burden of vivax malaria is underestimated in the district.
Consistent with the findings of a previous study conducted in Savannakhet province [9], the present study found that most of the infections (63.3 %) were asymptomatic and sub-microscopic. This finding highlighted the importance of tackling asymptomatic infections or “hidden malaria” in malaria elimination efforts in the malaria-prone districts of the Lao PDR. Thus far, no specific strategy for identifying asymptomatic parasite carriers has been implemented in the Lao PDR. Therefore, the introduction of active case detection for asymptomatic infections is recommended.
The results of the present study indicated that infections were highly clustered at the household level and that infections were unevenly distributed across households. A considerable number of studies have reported household clustering in countries other than the Lao PDR [17–19]. One possible explanation for the household clustering is that household members share the same environmental risk factors including the proximity of housing location to breeding sites [20] and housing type [21]. Another possible explanation is that household members share the same risk behaviors such as not using a bed net [9, 20] and working in the forest [10]. The findings of the present study suggest the implementation of household-based strategies to achieve effective active case detection. A reactive case detection strategy that is linked to facility-based passive case detection can be an option for actively detecting cases in a setting where infections are highly clustered within households and the transmission intensity is low [22]. As reported elsewhere [23, 24], malaria patients who are identified through facility-based passive case detection can be used as an index case. Subsequently, individuals living with the index case are screened.
For such reactive case detection to be implemented in Xepon district or other similar settings in the Lao PDR, RDT would be used as a screening tool. Although the present study showed that RDT detected only 36.7 % of the true positives confirmed by PCR in the study site and malaria elimination cannot be achieved by screening with the current generation of RDTs [25], the use of PCR for screening is not currently feasible in the rural districts of the Lao PDR. To increase the detection rate of secondary infections by screening with RDT, the following strategies should be incorporated. First, screening should focus on the households of index cases that do not possess a sufficient number of bed nets. In the present study, the households with multiple cases of infection were less likely to have a sufficient number of bed nets than were the households with a single case, although the difference was not statistically significant, possibly because of the small sample size (n = 36). Second, screening should focus on the households of index cases in which a household member has a history of fever/malaria. The present study found that two-week recall of a fever episode was reported only from the participants who had a RDT detectable infection. The present study also found that the RDT-positive participants were more likely to have reported a fever episode than were the RDT-negative participants (19.0 vs 7.9 %), although the difference was not significant. A previous study conducted in Savannakhet province reported that malaria-related symptoms in the past one year and a history of clinical malaria were predictive factors for the PCR-diagnosed malaria infections [9]. A Cambodian study [23] that assessed a reactive case detection approach using index cases found through passive case detection at a health facility or through a village malaria worker reported a significant association between the secondary infections detected by RDT and measured fever (>37.2 °C) and previous malaria. Finally, screening should focus on the households of index cases with an environmental risk factor. Another study conducted in the Lao PDR showed that the number of Anopheles mosquitoes that entered people’s homes differed significantly according to the type of house, the presence of cattle, and the presence of wood smoke from cooking fires [21].
The Cambodian study [23] showed, however, that the reactive case detection approach identified very few secondary infections. Among the members of the index cases, the positive rate of the secondary infections was 0.3 % by RDT and 1.3 % by PCR. The study recommended that the reactive case detection approach is not appropriate for very low transmission settings in which exposure to malaria occurs away from the community and there is a high level of treatment-seeking from the private sector. Given this recommendation, the reactive case detection approach could be appropriate in the study district of Xepon because one half of the infections occurred in children, who accounted for 52.8 % of the participants, and a substantial proportion of the infections might have occurred within the village or on the fringe of the village. The possibility of such local transmission around the village can be supported by the findings of another study conducted in three villages in Xepon and Nong districts, Savannakhet province [10]. The study showed that many Anopheles dirus mosquitoes, which are a main vector in the Lao PDR, were captured in indoor light traps. Although treatment seeking from the private sector is also high in Xepon district, in which approximately 44 % of the malaria cases were reported from the private sector in 2013/14, the private sector has been an integral part of the National Malaria Control Programme in the Lao PDR including Xepon district. Most importantly, there was a marked difference in the transmission intensity between the present study and the Cambodian study (positive rate by RDT: 2.4 vs 0.5 %).
A number of factors that were previously reported as risk factors from the Lao PDR and/or neighboring countries were not identified as risk factors in the present study site. Although not using a bed net was reported as a risk factor for malaria infection from Savannakhet province [9], almost all of the participants (99.2 %) in the present study reported use of a bed net on the night before they were surveyed. Likewise, although sleeping away from home was reported as a risk factor for malaria infection from Attapeu province, Lao PDR [20], very few of the present participants (0.9 %) did this. Additionally, working in the forest at night, which is a known risk factor in Vietnam and Thailand [26, 27], was not common in the present study site. Because the present study failed to include more than one half of the population in the study site, it could possibly identify these previously reported risk factors if most of the population were included. For example, because the present study found that only 53.0 % of the households owned a sufficient number of bed nets to fully cover all members, some of the non-participants might not have used a bed net.
A major limitation of this study is the low participation rate: only 47.8 % of the population in the study villages participated. As Table 3 shows, compared to the participants, the non-participants were more likely to be male, children under the age of five, and adults (especially adult males). A possible reason for the lower participation rate among the children is that their guardians are unwilling to expose their young children to blood collection. A possible reason for the lower participation rate of the adults is that they worked outside the village during the period of data collection. Because of this limitation, the results of the present study could underestimate the risk of malaria infection among young children and adults while overestimating the risk among older children.
This study has two other limitations. First, because the data collection was confined to half of the population in the 10 villages from the HDSS area, the present sample was not representative of the population of Xepon district. Second, the survey took place between August and September (the rainy season) and, therefore, did not take seasonal malaria epidemiology into account or seasonal variation in the frequency of risk factors such as sleeping away from home, working in the forest at night, and not sleeping under a bed net. However, sleeping away from home is more frequently reported in the rainy season than in the dry season in the Lao PDR [15].
Conclusions
The present community-based study showed that P. falciparum infections were common but that infections involving P. vivax, which was rarely reported from the district healthcare facility, accounted for 21 % of the malaria infections. This suggested that the burden of P. vivax infection is underestimated. Most (63 %) of the infections were asymptomatic and sub-microscopic and were highly clustered within households. To eliminate malaria in Xepon district and other similar settings in the Lao PDR, the National Malaria Control Programme should consider household-based control strategies. The findings of the present study suggest the use of a reactive case detection strategy targeting the household members of index cases.
Abbreviations
Lao PDR:
The Lao People’s Democratic Republic
RDT:
rapid diagnostic test
ACT:
artemisinin-based combination therapy
PCR:
polymerase chain reaction
HDSS:
health and demographic surveillance system
DNA:
deoxyribonucleic acid
Declarations
Authors’ contributions
TP and DN were the principal investigators and drafted the manuscript with the help of MI and SK. MI, PP, FN and JK contributed to data collection. MN contributed to the laboratory procedures and manuscript development. PP, JK, KM and TM contributed to the conception of the study. MI, BH, PTB, SK and TM contributed to the data analysis and reviewed the manuscript. All authors read and approved the final manuscript.
Acknowledgements
The authors sincerely thank the study participants and the field surveyors for their contributions to this study. The authors also thank the anonymous reviewers for their valuable inputs.
Competing interests
The authors declare that they have no competing interests.
Availability of data and materials
The data supporting the conclusions of this paper are included within the paper. Raw data may be obtained from the corresponding author upon request.
Ethics approval and consent to participate
This study was approved by the National Ethics Committee for Health Research, Ministry of Health, Lao PDR (No. 137/NECHR) and the Ethics Review Committee for Epidemiological Study, University of the Ryukyus, Japan (No. 176). The participants knew that their participation would be voluntary and that all data obtained would be confidential. Verbal and written consent was obtained from all of the participants before conducting the survey. The guardians of child participants consented to their participation.
Funding
This study was partly supported by a JICA/AMED SATREPS project for the “Development of innovative research technique in genetic epidemiology of malaria and other parasitic diseases in the Lao PDR for containing their expanding endemicity.”
Open AccessThis article is distributed under the terms of the Creative Commons Attribution 4.0 International License (http://creativecommons.org/licenses/by/4.0/), which permits unrestricted use, distribution, and reproduction in any medium, provided you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license, and indicate if changes were made. The Creative Commons Public Domain Dedication waiver (http://creativecommons.org/publicdomain/zero/1.0/) applies to the data made available in this article, unless otherwise stated.
Authors’ Affiliations
(1)
Savannakhet Provincial Health Department
(2)
Department of Molecular and Cellular Parasitology, School of Medicine, Juntendo University
(3)
SATREPS Project for Parasitic Diseases
(4)
Department of Global Health, School of Health Sciences, University of the Ryukyus
(5)
Department of Tropical Medicine and Malaria, Research Institute, National Center for Global Health and Medicine
(6)
Graduate School of Tropical Medicine and Global Health, Nagasaki University
(7)
Center of Malariology, Parasitology and Entomology, Ministry of Health
(8)
Institut Pasteur du Laos
References
Lao Statistics Bureau. Vientiane capital: ministry of planning and investment, Lao PDR. 2016. http://www.lsb.gov.la/en/index.php. Accessed 18 Sept 2016.
Steering Committee for the Agricultural Census, Agricultural Census Office. Lao census of agriculture 2010/11 highlights. Vientiane: Agricultural Census Office; 2012.Google Scholar
Ministry of Health. National strategy for malaria control and pre-elimination 2011–2015. Vientiane: Ministry of Health; 2010.Google Scholar
WHO, UNICEF. The world malaria report 2015. Geneva: World Health Organization; 2015.Google Scholar
Delacollette C, D’Souza C, Christophel E, Thimasarn K, Abdur R, Bell D, et al. Malaria trends and challenges in the Greater Mekong Subregion. Southeast Asian J Trop Med Public Health. 2009;40:674–91.PubMedGoogle Scholar
Nonaka D, Laimanivong S, Kobayashi J, Chindavonsa K, Kano S, Vanisaveth V, et al. Is staying overnight in a farming hut a risk factor for malaria infection in a setting with insecticide-treated bed nets in rural Laos? Malar J. 2010;9:372.View ArticlePubMedPubMed CentralGoogle Scholar
Rattanaxay P, Phompida S, Kobayashi J. A review of malaria situation and its control in Lao PDR. In: Tongol-Rivera P, Kano S, editors. Asian parasitology. Vol 6, Malaria in Asia. Chiba: The Federation of Asian Parasitologists; 2005. p. 85–104.Google Scholar
Khaminsou N, Kritpetcharat O, Daduang J, Kritpetcharat P. A survey of malarial infection in endemic areas of Savannakhet province, Lao PDR and comparative diagnostic efficiencies of Giemsa staining, acridine orange staining, and semi-nested multiplex PCR. Parasitol Int. 2008;57:143–9.View ArticlePubMedGoogle Scholar
Pongvongsa T, Ha H, Thanh L, Marchand RP, Nonaka D, Tojo B, et al. Joint malaria surveys lead towards improved cross-border cooperation between Savannakhet province, Laos and Quang Tri province, Vietnam. Malar J. 2012;11:262.View ArticlePubMedPubMed CentralGoogle Scholar
Nonaka D, Vongseththa K, Kobayashi J, Bounyadeth S, Kano S, Phompida S, et al. Public and private sector treatment of malaria in Lao PDR. Acta Trop. 2009;112:283–7.View ArticlePubMedGoogle Scholar
Ménard D, Khim N, Beghain J, Adegnika AA, Shafiul-Alam M, Amodu O, et al. A worldwide map of Plasmodium falciparum K13-propeller polymorphisms. N Engl J Med. 2016;374:2453–64.View ArticlePubMedGoogle Scholar
WHO. Strategy for malaria elimination in the greater mekong subregion (2015–2030). Geneva: World Health Organization; 2015.Google Scholar
Nishimoto F, Kaneko S, Jiang H, Moji K. Impending demographic transitions in rural Laos: projections based on data from the health and demographic surveillance system. Jpn J Health Hum Ecol. 2014;80:54–9 (in Japanese).View ArticleGoogle Scholar
Canier L, Khim N, Kim S, Sluydts V, Heng S, Dourng D, et al. An innovative tool for moving malaria PCR detection of parasite reservoir into the field. Malar J. 2013;12:405.View ArticlePubMedPubMed CentralGoogle Scholar
Nonaka D, Pongvongsa T, Nishimoto F, Nansounthavong P, Sato Y, Jiang H, et al. Households with insufficient bednets in a village with sufficient bednets: Evaluation of household bednet coverage using bednet distribution index in Xepon district, Lao PDR. Trop Med Health. 2015;43:95–100.View ArticlePubMedPubMed CentralGoogle Scholar
Greenwood BM. The microepidemiology of malaria and its importance to malaria control. Trans R Soc Trop Med Hyg. 1989;83(Suppl):25–9.View ArticlePubMedGoogle Scholar
Brooker S, Clarke S, Njagi JK, Polack S, Mugo B, Estambale B, et al. Spatial clustering of malaria and associated risk factors during an epidemic in a highland area of western Kenya. Trop Med Int Health. 2004;9:757–66.View ArticlePubMedGoogle Scholar
Rulisa S, Kateera F, Bizimana JP, Agaba S, Dukuzumuremyi J, Baas L, et al. Malaria prevalence, spatial clustering and risk factors in a low endemic area of Eastern Rwanda: a cross sectional study. PLoS One. 2013;8:e69443.View ArticlePubMedPubMed CentralGoogle Scholar
Vythilingam I, Sidavong B, Chan ST, Phonemixay T, Vanisaveth V, Sisoulad P, et al. Epidemiology of malaria in Attapeu Province, Lao PDR in relation to entomological parameters. Trans R Soc Trop Med Hyg. 2005;99:833–9.View ArticlePubMedGoogle Scholar
Hiscox A, Khammanithong P, Kaul S, Sananikhom P, Luthi R, Hill N, et al. Risk factors for mosquito house entry in the Lao PDR. PLoS One. 2013;8:e62769.View ArticlePubMedPubMed CentralGoogle Scholar
Sturrock HJ, Hsiang MS, Cohen JM, Smith DL, Greenhouse B, Bousema T, et al. Targeting asymptomatic malaria infections: active surveillance in control and elimination. PLoS Med. 2013;10:e1001467.View ArticlePubMedPubMed CentralGoogle Scholar
Hustedt J, Canavati SE, Rang C, Ashton RA, Khim N, Berne L, et al. Reactive case-detection of malaria in Pailin Province, Western Cambodia: lessons from a year-long evaluation in a pre-elimination setting. Malar J. 2016;15:132.View ArticlePubMedPubMed CentralGoogle Scholar
Littrell M, Sow GD, Ngom A, Ba M, Mboup BM, Dieye Y, et al. Case investigation and reactive case detection for malaria elimination in northern Senegal. Malar J. 2013;12:331.View ArticlePubMedPubMed CentralGoogle Scholar
von Seidlein L. The failure of screening and treating as a malaria elimination strategy. PLoS Med. 2014;11:e1001595.View ArticleGoogle Scholar
Erhart A, Ngo DT, Phan VK, Ta TT, Van Overmeir C, Speybroeck N, et al. Epidemiology of forest malaria in central Vietnam: a large scale cross-sectional survey. Malar J. 2005;4:58.View ArticlePubMedPubMed CentralGoogle Scholar
Chaveepojnkamjorn W, Pichainarong N. Behavioral factors and malaria infection among the migrant population, Chiang Rai province. J Med Assoc Thai. 2005;88:1293–301.PubMedGoogle Scholar
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