# Forest malaria in Cambodia: the occupational and spatial clustering of Plasmodium vivax and Plasmodium falciparum infection risk in a cross-sectional survey in Mondulkiri province, Cambodia

Mirco Sandfort, Amélie Vantaux, Saorin Kim, Thomas Obadia, Anaïs Pepey, Soazic S. Gardais, Nimol Khim, Dysoley Lek, Michael T White, Leanne J Robinson, et al.

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## To cite this version:

Mirco Sandfort, Amélie Vantaux, Saorin Kim, Thomas Obadia, Anaïs Pepey, et al., Forest malaria in Cambodia: the occupational and spatial clustering of Plasmodium vivax and Plasmodium falciparum infection risk in a cross-sectional survey in Mondulkiri province, Cambodia. Malaria Journal, 2020, 19 (1), pp.413. 10.1186/s12936-020-03482-4. pasteur-03023631

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# Forest malaria in Cambodia: the occupational and spatial clustering of *Plasmodium vivax* and *Plasmodium falciparum* infection risk in a cross-sectional survey in Mondulkiri province, Cambodia

Mirco Sandfort<fnref n="1" />,²* , Amélie Vantaux³, Saorin Kim³, Thomas Obadia<fnref n="1" />,⁴, Anaïs Pepey³, Soazic Gardais<fnref n="1" />, Nimol Khim³, Dysoley Lek⁵,⁶, Michael White<fnref n="1" />,⁷, Leanne J. Robinson⁷,⁸,⁹, Benoit Witkowski³† and Ivo Mueller<fnref n="1" />,⁷,⁸†

**Abstract**

**Background:** After a marked reduction in malaria burden in Cambodia over the last decades, case numbers increased again in 2017–2018. In light of the national goal of malaria elimination by 2025, remaining pockets of high risk need to be well defined and strategies well-tailored to identify and target the persisting burden cost-effectively. This study presents species-specific prevalence estimates and risk stratification for a remote area in Cambodia.

**Methods:** A cross-sectional survey was conducted in 17 villages in the high-incidence province Mondulkiri in the dry season (December 2017 to April 2018). 4200 randomly selected participants (2–80 years old) were tested for *Plasmodium* infection by PCR. Risk of infection was associated with questionnaire-derived covariates and spatially stratified based on household GPS coordinates.

**Results:** The prevalence of PCR-detectable *Plasmodium* infection was 8.3% (349/4200) and was more than twice as high for *Plasmodium vivax* (6.4%, 268) than for *Plasmodium falciparum* (3.0%, 125, *p* < 0.001). 97.8% (262/268) of *P. vivax* and 92.8% (116/125, *p* < 0.05) of *P. falciparum* infections were neither accompanied by symptoms at the time of the interview nor detected by microscopy or RDT. Recent travels to forest sites (aOR 2.17, *p* < 0.01) and forest work (aOR 2.88, *p* < 0.001) were particularly strong risk factors and risk profiles for both species were similar. Large village-level differences in prevalence of *Plasmodium* infection were observed, ranging from 0.6% outside the forest to 40.4% inside. Residing in villages at the forest fringe or inside the forest compared to outside was associated with risk of infection (aOR 2.14 and 12.47, *p* < 0.001). Villages inside the forest formed spatial hotspots of infection despite adjustment for the other risk factors.

**Conclusions:** Persisting pockets of high malaria risk were detected in forested areas and in sub-populations engaging in forest-related activities. High levels of asymptomatic infections suggest the need of better case detection plans and the predominance of *P. vivax* the implementation of radical cure. In villages inside the forest, within-village

<footnote>*Correspondence: msandfor@pasteur.fr</footnote>

<footnote>†Benoit Witkowski and Ivo Mueller contributed equally to the work</footnote>

<footnote n="1">¹ Malaria: Parasites and Hosts Unit, Institut Pasteur, Paris, France
Full list of author information is available at the end of the article</footnote>
exposure was indicated in addition to risk due to forest activities. Village-level stratification of targeted interventions based on forest proximity could render the elimination efforts more cost-effective and successful.

**Keywords:** Forest, Occupational risk, Spatial, Vivax, Hotspots, Cambodia, Greater Mekong Subregion

**Background**

In the last two decades, Cambodia has seen a marked decrease in numbers of reported malaria cases from approximately 140,000 cases in 1999 to 60,000 in 2018 [1]. Likewise, mortality by malaria steadily reduced, with zero deaths reported for the first time in 2018. Similarly remarkable reductions have been observed in the other countries of the Greater Mekong Subregion [2]. Since 2014, these countries have even pursued the goal of elimination of all malaria by 2030 [3].

This ambitious aim however faces several obstacles. After an all-time low of approximately 20,000 reported cases in 2016, numbers have increased again in 2017–2018 [1, 2]. While the annual case numbers increased for both *Plasmodium falciparum* and *Plasmodium vivax* in 2017, they declined for *P. falciparum* in 2018 but increased further for *P. vivax* [1, 3]. Consequently, *P. vivax* now accounts for almost three-quarters of all malaria cases in Cambodia [2, 4].

The national malaria control programme (NMCP) is based on mass distribution campaigns of long-lasting insecticidal nets (LLIN), diagnosis by light microscopy (LM) and rapid diagnostic tests (RDT) in the public health sector, RDT-based testing by village/mobile malaria workers (VMW/MMW), and treatment of blood stage infections with artemisinin-based combination therapy (ACT) [5]. The spread of multi-drug resistant *P. falciparum* malaria in Western Cambodia [6] might explain the resurgence of *P. falciparum* cases in 2017 and the change of first-line treatment from dihydroartemisinin-piperaquine to artesunate-mefloquine their subsequent decline in 2018. For *P. vivax*, such resistance is not reported. However, this species has dormant liver-stages called hypnozoites that escape acute blood stage treatment and cause relapsing infections. Therefore, although LLINs do reduce *P. vivax* transmission and ACT is effective against blood stage *P. vivax* infection, neither intervention targets the hypnozoite reservoir. Hence, there is a less marked effect of these interventions on *P. vivax* than *P. falciparum*.

The only licensed drug with hypnozoiticidal effect is primaquine, typically administered at 0.25 mg/kg daily over 14 days in Cambodia. The roll-out of *P. vivax* radical cure is part of the national treatment guidelines but has not yet been implemented because of concerns over the drug's potential haemolytic effect in patients with G6PD deficiency [7, 8]. In summary, the control and elimination

efforts of the NMCP are successfully targeting clinical *P. falciparum* malaria (understandably in the context of drug-resistant *P. falciparum* [6] in the country) but to a lesser extent to *P. vivax*.

Another obstacle to elimination is the high prevalence of asymptomatic infections [9–12]. Neither health facilities nor VMWs reach these asymptomatic carriers as they do not seek diagnosis or treatment. Although of lower infectivity [13, 14], they remain however a potential source of onward transmission.

Alongside the sustained reduction in countrywide incidence, malaria has also become increasingly fragmented, affecting fewer, usually remote provinces. The highest incidence of malaria in Cambodia and across the Greater Mekong Subregion is now particularly found in the North-Eastern provinces such as Ratanakiri and Mondulkiri [1, 3]. These settings are characterized by a high proportion of ethnic minorities and agricultural or wood-logging activities as the main income sources. Exposure to *Anopheline* mosquitoes and *Plasmodium* in Cambodia is understood to primarily occur in the forest rather than peri-domestically [15–17].

Together with the Western province Pursat, the highest incidence per province in 2018 was reported for Mondulkiri [1]. However, previous studies on population-level burden and risk of malaria in Cambodia focused on the country's West and North [12, 16–21] or the Eastern province of Ratanakiri [10, 11, 15, 20, 22, 23]. Here, using a cross-sectional survey conducted in a rural area of Mondulkiri in the dry season in 2018, estimates of the prevalence of *Plasmodium* infection are shown and key risk factors identified.

**Methods**

**Study area and census**

Seventeen villages were selected in the Kaev Seima district, Mondulkiri province, in the Kingdom of Cambodia. The rainy season in Cambodia normally runs from June to October with the high transmission period from June through December [1, 4, 24]. Approx. two-thirds of the population in Mondulkiri is comprised of national ethnic minorities, with the Phnong ethnic group comprising the largest proportion [25, 26]. In November–December 2017, a census was conducted by visiting each household in the 17 villages and collecting basic demographics of household participants such as age and gender. A person's household was defined as location of main residence in
the village according to adult members of the household or the village head. GPS coordinates were collected using Garmin® GPSMAP® 64s devices. When no adult household member could be found, demographic information was obtained from the village head's registry book.

## Cross-sectional survey

Based on the census, a random selection of households was drawn oversampling small villages to ensure sufficient coverage (Additional file 1: Table ST1). Selected households were visited from mid-December 2017 until mid-April 2018 and all household members aged 2–80 years who had resided in the study area for at least 3 months were invited to participate in the survey. Upon informed consent, a questionnaire on household variables was administered to the head of household or another adult household member by trained interviewers. A questionnaire on individual-level variables was administered to each consenting household member. Children were interviewed assisted by a parent (or rarely another caregiver in the absence of the parents). Data were collected on tablets and run through automated data quality checks within days after the interview. In case of missing data or discrepancies, the field team was informed for immediate resolution if possible. Fingerprick blood samples were collected as thick and thin film slides and in K⁺EDTA-microtainers. Participants were screened for symptoms, i.e. feeling sick or feverish on the day of interview, having felt feverish over the preceding two days, or having an axillary body temperature of at least 37.5 °C. Upon any indication, the participant was administered a standard malaria RDT (Malaria Ag P.f/P.v, Standard Diagnostics Inc., South Korea) and referred to a local health care provider for treatment if positive.

## Detection of infection

The microtainer blood samples collected at the interview site were stored in 4 °C ice boxes. At a field laboratory, they were separated into plasma and cell pellet and frozen at -20 °C. Following transport to the main laboratory at Institut Pasteur of Cambodia in Phnom Penh, cell pellets were stored at -20 °C and plasma at -80 °C. Infections with any of the four human *Plasmodium* parasites were determined by real-time PCR [27]. In case of a positive genus-specific result, qPCR specific for *P. falciparum*, *P. vivax*, *Plasmodium malariae*, and *Plasmodium ovale* followed. All positive and a random selection of 10% negative samples were assessed by independent double LM readings of asexual and sexual stages and parasite densities calculated. No discrepancy in parasite densities of above 30% occurred. Parasites were counted per approximately 500 leukocytes and densities were inferred assuming 8000 leukocytes per microlitre blood.

## Descriptive analyses

Villages were classified based on the forest cover in a 750 m radius around the households, computed from the land cover analysis in [28] (Additional file 1: Figure S1). Villages with ≥ 50% of households with ≥ 10% forest cover in their vicinity were considered “inside the forest”, with ≥ 30% of households with ≥ 5% forest cover as “at the forest fringe”, or “outside the forest” otherwise. Because of very low sample sizes, the two small, neighbouring villages Beng (11 individuals) and Gaty (95 individuals) were analysed as one. Population prevalence was estimated based on post-sampling weights assigned to each participant according to their representation by village, gender, and 10-year age bins compared to the census population (raw numbers of positive survey samples accompany the estimates in brackets). Categorical covariates were compared using the chi-squared test or the Fisher's exact test when low strata sizes required it.

## Risk factor analyses

The association of covariates with infection by *P. vivax*, *P. falciparum*, or all four species was assessed by mixed-effects logistic regression with random intercepts per household and village. Those covariates that were statistically significantly associated with *Plasmodium* infection at two-tailed α=5% in univariate regressions were included in the multivariate model. The villages' proximity to the forest, work-unrelated overnight travels (incl. to forest sites), and work in the deep forest were considered part of the multivariate model a priori in order to assess the association of forest exposure with risk of infection. Having slept outdoors or under a bed net last night were kept as part of the model based on causal reasoning or prior knowledge due to their association with infection risk in previous publications, e.g. [11]. Gender and age were retained in the model as proxies for risk-related behaviour and thus potential confounders. The other covariates comprised potential proxies for socioeconomic status or further exposure variables and their subset significantly associated with infection risk was assessed by backwards variable selection. Akaike Information Criterion (AIC) was used to assess model fit. If collinearities and interactions were identified, covariates were retained comparing fit of the respective models by AIC. Statistical significance in the multivariate regressions was calculated per covariate by likelihood ratio tests. Spatial hotspots were identified by a purely spatial scan statistic via a discrete Poisson model with maximally a third of the population in a scanning window, adjusted for all covariates of the final multivariate model except for the villages' forest proximity [29].
## Software

All questionnaires were applied on tablets using the REDCap (Research Electronic Data Capture) software hosted at Institut Pasteur in Paris [30, 31]. Data quality control as well as all descriptive and analytical statistics were performed in R 3.6.3 [32]. The SaTScan™ software version 9.6 was used for the spatial scan statistic [33].

## Results

### Census, survey representativeness and survey population

From among the 10,053 individuals in 2351 households identified in the census, the survey recruited 4200 participants from 1147 households and oversampled smaller villages on average (Additional file 1: Table ST1). A map of the survey households and the categorization of villages by proximity to the forest is shown in Fig. 1a. Mean age was 26 years in both the census and the survey and with 51% (5135/10,053) and 53% (2231/4200) women, respectively, women were slightly oversampled in the survey. In men, ages of 16 to 40 years were mildly underrepresented (Fig. 1b). The main income source for the survey households was by large majority farming (89.8%, 1030/1147). In terms of mobility, three-quarters (75.9%, 3188/4200) of the survey participants reported work-unrelated trips to forest or field sites in the last month (8.5%, 358/4200) or any work trip in the last two

months (75.8%, 3184/4200). By vast majority, these trips were short and frequent: In only 4.4% (140/3188) of the instances, stays for longer than a week were reported and among those who reported any work trip, 89.4% (2847/3184) went for work at least once a week.

### Plasmodium vivax predominated over other human malaria parasites

Infection by *Plasmodium* parasite was detected by PCR in 8.3% (349/4200) of participants. The proportion of samples positive for *P. vivax* was 6.4% (268/4200) compared to 3.0% (125/4200) for *P. falciparum* (*p* < 0.001, Table 1). *Plasmodium vivax* was found in 77% (268/349) of all infections compared to 36% (125/349) for *P. falciparum*. Four samples were positive for *P. malariae* mono-infections and none for *P. ovale*. Speciation by PCR was unsuccessful in 4 samples which were analysed as negative. Extrapolation to the entire Kaev Seima population yielded an estimated prevalence of 8.9% for *Plasmodium* infection, 6.8% for *P. vivax*, and 3.3% for *P. falciparum*. Estimated prevalence was highly heterogeneous across the villages, ranging from 0.6% (3/594) to 36.3% (54/152) and from 0% (0/594) to 25.1% (27/106) for *P. vivax* and *P. falciparum*, respectively (both *p* < 0.001, Table 1). Village-level prevalence of *P. vivax* infection was on average higher in villages inside the forest compared with those

<figure><img src="image_4.png" /></figure>

<figure><img src="image_5.png" /></figure>

<figure><img src="image_6.png" /><figcaption>**Fig. 1** Geographic and demographic description of survey population. **a** Map of survey household locations, coloured by village in shades of blue, purple, or green if village is in category "outside forest", "forest fringe", or "inside forest", respectively. Background Landsat-8 image courtesy of the U.S. Geological Survey. **b** Representativeness of age distribution in women and men by overlaying distribution in census (bars) by distribution in survey (points)</figcaption></figure>
<table><caption>**Table 1** Prevalence of PCR-detected infections</caption><thead><tr><th rowspan="2"></th><th rowspan="2">N</th><th colspan="4">PCR positivity</th></tr><tr><th>Plasmodium</th><th>P. vivax</th><th>P. falciparum</th><th>Co-infections</th></tr></thead><tbody><tr><td>Proportion (n) of survey samples</td><td>4200</td><td>8.3% (349)</td><td>6.4% (268)</td><td>3.0% (125)</td><td>1.1% (48)</td></tr><tr><td>Extrapolated population prevalence</td><td>10,053</td><td>8.9%</td><td>6.8%</td><td>3.3%</td><td>1.3%</td></tr><tr><td>Extrapolated prevalence per village</td><td></td><td></td><td></td><td></td><td></td></tr><tr><td>Outside forest</td><td></td><td></td><td></td><td></td><td></td></tr><tr><td>Trapaingphiae</td><td>594</td><td>0.6% (3)</td><td>0.6% (3)</td><td>0% (0)</td><td>0% (0)</td></tr><tr><td>Chhnaeng</td><td>550</td><td>2.7% (12)</td><td>1.9% (8)</td><td>0.9% (4)</td><td>0.3% (1)</td></tr><tr><td>Oham</td><td>333</td><td>8.0% (20)</td><td>6.2% (16)</td><td>2.9% (7)</td><td>1.1% (3)</td></tr><tr><td>Ohrana</td><td>470</td><td>5.7% (25)</td><td>4.2% (18)</td><td>1.8% (8)</td><td>0.2% (1)</td></tr><tr><td>Sraepreas</td><td>228</td><td>6.5% (13)</td><td>5.6% (11)</td><td>1.7% (3)</td><td>0.8% (1)</td></tr><tr><td>Sraektum</td><td>296</td><td>13.1% (36)</td><td>8.6% (24)</td><td>4.8% (13)</td><td>0.8% (2)</td></tr><tr><td>Lapakhe</td><td>71</td><td>10.6% (4)</td><td>6.7% (3)</td><td>7.8% (2)</td><td>3.9% (1)</td></tr><tr><td>Ohkaunpreas</td><td>317</td><td>6.9% (21)</td><td>5.9% (18)</td><td>1.7% (5)</td><td>0.7% (2)</td></tr><tr><td>Trapaingtouk</td><td>201</td><td>1.9% (3)</td><td>1.2% (2)</td><td>0.6% (1)</td><td>0% (0)</td></tr><tr><td>Forest fringe</td><td></td><td></td><td></td><td></td><td></td></tr><tr><td>Poucha</td><td>239</td><td>9.7% (24)</td><td>6.7% (17)</td><td>4.3% (10)</td><td>1.4% (3)</td></tr><tr><td>Sraeampilkroam</td><td>218</td><td>11.0% (23)</td><td>7.2% (15)</td><td>3.8% (8)</td><td>0.6% (1)</td></tr><tr><td>Sraeampilleu</td><td>168</td><td>10.6% (17)</td><td>8.2% (13)</td><td>2.4% (4)</td><td>0% (0)</td></tr><tr><td>Inside forest</td><td></td><td></td><td></td><td></td><td></td></tr><tr><td>Beng-Gaty</td><td>106</td><td>37.9% (40)</td><td>30.9% (32)</td><td>25.1% (27)</td><td>18.1% (19)</td></tr><tr><td>Ohchra</td><td>152</td><td>40.4% (60)</td><td>36.3% (54)</td><td>10.0% (15)</td><td>5.9% (9)</td></tr><tr><td>Sraelvy</td><td>154</td><td>18.7% (28)</td><td>15.5% (23)</td><td>3.9% (6)</td><td>1.3% (2)</td></tr><tr><td>Ohtrone</td><td>103</td><td>19.7% (20)</td><td>10.4% (11)</td><td>12.4% (12)</td><td>3.1% (3)</td></tr></tbody></table>

As proportions of positive blood samples or as census population-level estimates by extrapolation via post-sampling weights (raw numbers of positive samples in brackets). 4 positive *P. malariae* samples omitted

at the forest fringe and outside the forest (medians 23.2, 7.2, 5.6% respectively, Fig. 2; similarly for *P. falciparum* in Additional file 1: Fig. S2).

### Plasmodium vivax infections were least detectable by the health care system

Of all PCR-detected infections, 83.2% (223/268) were sub-microscopic for *P. vivax* compared to 74.4% (93/125) for *P. falciparum* (*p*≈0.056, Table 2). *Plasmodium vivax* infections coincided less often with reported or measured symptoms at the interview than those by *P. falciparum*, with 6.7% (18/268) and 16.8% (21/125), respectively (*p*<0.01). Only 2.2% (6/268) of all *P. vivax* infections were symptomatic and also positive by RDT or LM (i.e. detectable through the Cambodian health care system), less often than for *P. falciparum* (7.2%, 9/125, *p*<0.05). Asexual parasite stages were detected in all LM⁺ samples for *P. vivax* (37/37) and in most (81.5%, 22/27) for *P. falciparum* (Table 3). In LM⁺ samples, the geometric mean parasite densities were 149.7 parasites/µL and 431.3 parasites/µL (t-test: *p*≈0.07), respectively. While gametocytes were found in only one (2.7%, 1/37) *P. vivax* LM⁺ sample, more than a third (37.0%, 10/27) of LM⁺ samples for *P. falciparum* were gametocyte-positive with a geometric mean of 51.1 gametocytes/µL.

### Prevalence was highest in men of working age

Estimated prevalence was more than twice as high in men as in women, i.e. 10.4% (188/1969) vs. 3.6% (80/2231) for *P. vivax* (*p*<0.001), 4.8% (83/1969) vs. 1.9% (42/2231) for *P. falciparum* (*p*<0.001), and 13.3% (239/1969) vs. 5.0% (110/2231) regardless of species (*p*<0.001). The patterns across age were similarly heterogeneous for both species (Additional file 1: Figure S3). While there was no difference in genus-wide prevalence across age in women, men showed an elevated risk at working age (*p*<0.001), regardless of the proximity of the village to the forest (Fig. 3). A fitted interaction term of gender and age was significant in the strata of villages outside the forest (*p*<0.001), but not across those villages at the forest fringe or inside the forest.

### Risk profiles for *P. vivax* and *P. falciparum* infections were similar

Risk of infection was associated with individual covariates for both *P. vivax* and *P. falciparum* in a highly similar
<figure><img src="image_7.png" /><figcaption>**Fig. 2** Prevalence of *P. vivax* infection per village (as boxplots by proximity of villages to the forest in **a** and as squares in **b**). Household locations of survey participants in the map background, transparently coloured by village in shades of blue, purple, or green if village is in category "outside forest", "forest fringe", or "inside forest", respectively. Significance asterisk for Kruskal-Wallis test of differences in village-level prevalence</figcaption></figure>

<table><caption>**Table 2** Detectability of infections through the public health care system</caption><thead><tr><th rowspan="2">Measure</th><th colspan="2">PCR positivity</th></tr><tr><th><i>P. vivax</i></th><th><i>P. falciparum</i></th></tr></thead><tbody><tr><td>LM<sup>+</sup></td><td>16.8% (45/268)</td><td>25.6% (32/125)</td></tr><tr><td>Symptoms at interview</td><td>6.7% (18/268)</td><td>16.8% (21/125)</td></tr><tr><td>Symptoms &amp; LM<sup>+</sup></td><td>2.2% (6/268)</td><td>4.8% (6/125)</td></tr><tr><td>Symptoms &amp; RDT<sup>+</sup></td><td>0.4% (1*/268)</td><td>4.0% (5/125)</td></tr><tr><td>Symptoms &amp; RDT/LM<sup>+</sup></td><td>2.2% (6/268)</td><td>7.2% (9/125)</td></tr></tbody></table>

Proportion (N) of PCR-positive samples that were also detectable by LM, inspection of symptoms, or by testing of symptomatic individuals with LM or RDTs

fashion (Table 4 for behavioural variables, full list of variables with odds ratios in Additional file 1: Table ST2). For both species, prevalence was associated with work-unrelated overnight travels and highest if those occurred to forest sites (22.4%, 60/268, and 14.9%, 40/268, for *P. vivax* and *P. falciparum*, respectively) and lowest for urban destinations (1.7%, 1/59, and 0%, 0/59, for both *p*<0.001). Infections of both species were also more prevalent among those who reported work trips to sites in the nearby and deep forest (assessed separately). In particular, a significantly higher prevalence was observed in participants who reported work trips into the deep forest (25.5%, 37/145, and 18.6%, 27/145) compared to those who did not (5.7%, 231/4055, and 2.4%, 98/4055,

<table><caption>**Table 3** Densities of asexual and sexual stages in LM<sup>+</sup> samples</caption><thead><tr><th rowspan="2"></th><th colspan="2">LM positivity</th></tr><tr><th><i>P. vivax</i></th><th><i>P. falciparum</i></th></tr></thead><tbody><tr><td><strong>Asexual parasitaemia</strong></td><td></td><td></td></tr><tr><td>Proportion (N) of LM<sup>+</sup> samples with detected asexual parasites</td><td>100% (37/37)</td><td>81.5% (22/27)</td></tr><tr><td>Geometric mean [μl blood]</td><td>149.7</td><td>431.3</td></tr><tr><td>Range [μl blood]</td><td>15.4–22,674.7</td><td>14.3–41,724.6</td></tr><tr><td><strong>Gametocytaemia</strong></td><td></td><td></td></tr><tr><td>Proportion (N) of LM<sup>+</sup> samples with gametocytes</td><td>2.7% (1/37)</td><td>37.0% (10/27)</td></tr><tr><td>Geometric mean [μl blood]</td><td>31.9</td><td>51.1</td></tr><tr><td>Range [μl blood]</td><td>31.9–31.9</td><td>15.6–175.0</td></tr></tbody></table>

Geometric mean and range always given from among samples with non-zero densities

<footnote>*Actually RDT<sup>+</sup> for *P. falciparum* in a co-infection with *P. vivax* as by PCR</footnote>
<figure><img src="image_8.png" /></figure>

for *P. vivax* and *P. falciparum*, respectively, *p* < 0.001). Once clustering at household and village-level was taken into account in univariate mixed-effects logistic regression, work in the nearby forest was no longer significantly associated with risk of infection though (Additional file 1: Table ST2). There were fewer infections among those who reported the use of standard protection measures such as sleeping under a bed net (6.1%, 236/3880, and 2.7%, 104/3880, vs. 10.0%, 32/320, and 6.6%, 21/320, if no net was used, *p* < 0.01 and *p* < 0.001 for *P. vivax* and *P. falciparum*, respectively). A higher risk in men, at working age, in villages at the forest fringe or inside the forest, and with indicators of lower socio-economic status was also found similarly for both species (Additional file 1: Table ST2).

## Working in and travelling to the forest were strong risk factors of infection

Behavioural covariates related to activities in the forest were statistically significant in the multivariate model of *Plasmodium* infection (Table 5). Having travelled to forest sites (nearby or deep forest) or having worked in the deep forest independently increased the odds of infection two to three-fold (adjusted odds ratio, aOR, 2.17, *p* < 0.01 and aOR 2.88, *p* < 0.001, respectively). Risk of infection was not significantly attributed to the other behavioural covariates that were included in the model a priori, namely having slept outdoors (aOR 1.99, *p* ≈ 0.08) and under a bed net (aOR 0.99, *p* ≈ 0.96). Risk of infection was increased in males (aOR 3.06, *p* < 0.001) and working age (aOR 7.84 in 21–25 years old compared to children, *p* < 0.001). A fitted interaction term of gender and age

improved the model (AIC 1786 vs. 1802 without interaction, *p* < 0.001, Additional file 1: Table ST3). Other covariates linked higher socio-economic status with lower odds of infection, such as living in a house with a roof built of relatively high-quality material (aOR 0.50, *p* < 0.01). The covariate on recent information on malaria via TV (aOR 0.37, *p* < 0.01) most likely also acts as a proxy for higher socio-economic status, i.e. being able to afford a TV in the first place.

## Descriptive characterization of the risk factors forest work and forest travels

Work-unrelated overnight travels to forest sites was a predominantly male domain (reported by 10.7%, 211/1969, of the men vs. 2.6%, 57/2231, of the women, *p* < 0.001) and particularly frequent in male adolescents (with 24.3%, 70/289, highest in 21–30 years old males, *p* < 0.001). Forest work was independently assessed for sites of the nearby and deep forest. Only for the latter, a statistically significant association with risk was observed. As for travels, they were reported more frequently by men than by women (6.9%, 135/1969, vs. 0.4%, 10/2231, *p* < 0.001) and most often by 21–30 years old males (17.6%, 51/289, *p* < 0.001). However, working in the deep forest occurred in both men aged between 8 and 60 years and in 8–43 years old women. Forest travels were reported in 3–62 years old men and 3–63 years old women. Both kind of trips occur usually frequently and of short duration. In 81% (217/268) of the times, the travels to forest sites lasted less than a week. Work trips into the deep forest occurred in 75% (108/145) of the times at least weekly. When going for work several times
<table><caption>Table 4 Univariate association of infection risk and behavioural covariates</caption><thead><tr><th rowspan="2">Covariate</th><th rowspan="2">N</th><th colspan="2">P. vivax</th><th colspan="2">P. falciparum</th></tr><tr><th>Prevalence</th><th>p-value</th><th>Prevalence</th><th>p-value</th></tr></thead><tbody><tr><td colspan="6"><em>Work-unrelated travels overnight in the last month to...</em></td></tr><tr><td>None</td><td>3465</td><td>4.9% (171)</td><td>&lt;0.001</td><td>2.0% (70)</td><td>&lt;0.001</td></tr><tr><td>Field sites</td><td>90</td><td>3.3% (3)</td><td></td><td>4.4% (4)</td><td></td></tr><tr><td>Forest sites</td><td>268</td><td>22.4% (60)</td><td></td><td>14.9% (40)</td><td></td></tr><tr><td>A village</td><td>177</td><td>13.6% (24)</td><td></td><td>5.6% (10)</td><td></td></tr><tr><td>A city</td><td>59</td><td>1.7% (1)</td><td></td><td>0% (0)</td><td></td></tr><tr><td>Unspecified</td><td>123</td><td>7.3% (9)</td><td></td><td>0.8% (1)</td><td></td></tr><tr><td colspan="6"><em>Work in the last two months in...</em></td></tr><tr><td colspan="6"><em>...deep forest</em></td></tr><tr><td>No</td><td>4055</td><td>5.7% (231)</td><td>&lt;0.001</td><td>2.4% (98)</td><td>&lt;0.001</td></tr><tr><td>Yes</td><td>145</td><td>25.5% (37)</td><td></td><td>18.6% (27)</td><td></td></tr><tr><td colspan="6"><em>...cassava field</em></td></tr><tr><td>No</td><td>2115</td><td>5.6% (118)</td><td>&lt;0.05</td><td>2.9% (62)</td><td>n.s</td></tr><tr><td>Yes</td><td>2085</td><td>7.2% (150)</td><td></td><td>3.0% (63)</td><td></td></tr><tr><td colspan="6"><em>No work</em></td></tr><tr><td>No</td><td>3186</td><td>7.9% (253)</td><td>&lt;0.001</td><td>3.7% (119)</td><td>&lt;0.001</td></tr><tr><td>Yes</td><td>1014</td><td>1.5% (15)</td><td></td><td>0.6% (6)</td><td></td></tr><tr><td colspan="6"><em>Slept outdoors last night</em></td></tr><tr><td>Indoors</td><td>4124</td><td>6.2% (256)</td><td>&lt;0.01</td><td>2.9% (118)</td><td>&lt;0.01</td></tr><tr><td>Outdoors</td><td>76</td><td>15.8% (12)</td><td></td><td>9.2% (7)</td><td></td></tr><tr><td colspan="6"><em>Sprays repellent usually at bedtime</em></td></tr><tr><td>No</td><td>3668</td><td>6.7% (247)</td><td>&lt;0.05</td><td>3.2% (116)</td><td>&lt;0.10</td></tr><tr><td>Yes</td><td>524</td><td>4.0% (21)</td><td></td><td>1.7% (9)</td><td></td></tr><tr><td colspan="6"><em>Slept under net last night</em></td></tr><tr><td>No</td><td>320</td><td>10.0% (32)</td><td>&lt;0.01</td><td>6.6% (21)</td><td>&lt;0.001</td></tr><tr><td>Yes</td><td>3880</td><td>6.1% (236)</td><td></td><td>2.7% (104)</td><td></td></tr></tbody></table>

Prevalence (n) of PCR-positivity across the strata of those behavioural variables that were statistically significant in univariate logistic regression. "n.s.": Not significant

a month or once a month, the reported duration was also most often less than a week (83%, 15/18, and 53%, 8/15, of the times, respectively). A net was used during overnight work trips into the forest in only 25% (28/113) of the instances. While equally few participants from villages outside the forest, at the forest fringe, and inside the forest reported work in the deep forest (3.4%, 105/3060, 2.9%, 18/625, and 4.3%, 22/515, respectively, *p*≈0.44), travelling to forest sites was reported more often in forest fringe and forest villages (8.6%, 54/625, and 11.5%, 59/515, respectively, compared to 5.1%, 155/3060, outside the forest, *p* < 0.001).

## Residing in a village inside the forest was an independent spatial risk factor

Living in a village inside the forest remained associated with risk of infection when adjusting for the significant

<table><caption>Table 5 Risk factors after multivariate mixed-effects logistic regression for *Plasmodium* infection as detected by PCR</caption><thead><tr><th>Covariate</th><th>N</th><th>aOR</th><th>95% CI</th><th>p</th></tr></thead><tbody><tr><td>Gender</td><td></td><td></td><td></td><td></td></tr><tr><td>Female</td><td>2231</td><td>Reference</td><td></td><td>&lt;0.001</td></tr><tr><td>Male</td><td>1969</td><td>3.06</td><td>[2.26-4.13]</td><td></td></tr><tr><td>Age [years]</td><td></td><td></td><td></td><td></td></tr><tr><td>2-10</td><td>1054</td><td>Reference</td><td></td><td>&lt;0.001</td></tr><tr><td>11-15</td><td>527</td><td>4.51</td><td>[2.52-8.06]</td><td></td></tr><tr><td>16-20</td><td>424</td><td>7.47</td><td>[4.14-13.47]</td><td></td></tr><tr><td>21-25</td><td>335</td><td>7.84</td><td>[4.20-14.63]</td><td></td></tr><tr><td>26-30</td><td>347</td><td>8.08</td><td>[4.36-14.94]</td><td></td></tr><tr><td>31-35</td><td>309</td><td>7.71</td><td>[4.05-14.70]</td><td></td></tr><tr><td>36-40</td><td>289</td><td>7.15</td><td>[3.76-13.58]</td><td></td></tr><tr><td>41-45</td><td>244</td><td>5.33</td><td>[2.67-10.65]</td><td></td></tr><tr><td>46-50</td><td>197</td><td>4.16</td><td>[1.92-8.99]</td><td></td></tr><tr><td>51-80</td><td>474</td><td>2.68</td><td>[1.36-5.29]</td><td></td></tr><tr><td>Forest proximity of village</td><td></td><td></td><td></td><td></td></tr><tr><td>Outside forest</td><td>3060</td><td>Reference</td><td></td><td>&lt;0.001</td></tr><tr><td>Forest fringe</td><td>625</td><td>2.14</td><td>[1.02-4.52]</td><td></td></tr><tr><td>Inside forest</td><td>515</td><td>12.47</td><td>[6.29-24.71]</td><td></td></tr><tr><td>Material of roof</td><td></td><td></td><td></td><td></td></tr><tr><td>Grass/leaves</td><td>81</td><td>0.96</td><td>[0.26-3.55]</td><td>&lt;0.01</td></tr><tr><td>Tent</td><td>79</td><td>3.43</td><td>[1.36-8.64]</td><td></td></tr><tr><td>Corrugated iron</td><td>3301</td><td>Reference</td><td></td><td></td></tr><tr><td>Wood planks/cement/tiles</td><td>739</td><td>0.50</td><td>[0.30-0.84]</td><td></td></tr><tr><td>Household owns a toilet</td><td></td><td></td><td></td><td></td></tr><tr><td>No</td><td>2383</td><td>Reference</td><td></td><td>&lt;0.05</td></tr><tr><td>Yes</td><td>1816</td><td>0.69</td><td>[0.48-1.00]</td><td></td></tr><tr><td>Household owns buffaloes</td><td></td><td></td><td></td><td></td></tr><tr><td>No</td><td>3977</td><td>Reference</td><td></td><td>&lt;0.01</td></tr><tr><td>Yes</td><td>222</td><td>2.11</td><td>[1.21-3.69]</td><td></td></tr><tr><td>Household head had received information on malaria via TV in the past 3 months</td><td></td><td></td><td></td><td></td></tr><tr><td>No</td><td>3784</td><td>Reference</td><td></td><td>&lt;0.01</td></tr><tr><td>Yes</td><td>415</td><td>0.37</td><td>[0.16-0.81]</td><td></td></tr><tr><td>Insecticides had been sprayed inside the house in the past year</td><td></td><td></td><td></td><td></td></tr><tr><td>No</td><td>2935</td><td>Reference</td><td></td><td>&lt;0.01</td></tr><tr><td>Yes</td><td>1264</td><td>0.62</td><td>[0.43-0.89]</td><td></td></tr><tr><td>Slept outdoors last night</td><td></td><td></td><td></td><td></td></tr><tr><td>No</td><td>4124</td><td>Reference</td><td></td><td>0.08</td></tr><tr><td>Yes</td><td>76</td><td>1.99</td><td>[0.93-4.25]</td><td></td></tr><tr><td>Slept under net last night</td><td></td><td></td><td></td><td></td></tr><tr><td>No</td><td>320</td><td>Reference</td><td></td><td>0.96</td></tr><tr><td>Yes</td><td>3880</td><td>0.99</td><td>[0.61-1.59]</td><td></td></tr></tbody></table>

demographic, socio-economic, and behavioural covariates (aOR 12.47, *p* < 0.001, Table 5). Households from all four villages inside the forest also formed hotspots of *Plasmodium* infection, i.e. purely spatial clusters of elevated risk of infection that cannot be explained by
<table><caption>**Table 5 (continued)**</caption><thead><tr><th>Covariate</th><th>N</th><th>aOR</th><th>95% CI</th><th>p</th></tr></thead><tbody><tr><td colspan="5">Work-unrelated travels overnight in the last month to...</td></tr><tr><td>None</td><td>3465</td><td>Reference</td><td></td><td>&lt;0.01</td></tr><tr><td>Field sites</td><td>90</td><td>0.63</td><td>[0.25–1.59]</td><td></td></tr><tr><td>Forest sites</td><td>268</td><td>2.17</td><td>[1.41–3.35]</td><td></td></tr><tr><td>A village</td><td>177</td><td>1.28</td><td>[0.75–2.17]</td><td></td></tr><tr><td>A city</td><td>59</td><td>0.27</td><td>[0.03–2.13]</td><td></td></tr><tr><td>Unspecified</td><td>123</td><td>1.23</td><td>[0.56–2.74]</td><td></td></tr><tr><td colspan="5">Work in the last two months in...</td></tr><tr><td colspan="5">...deep forest</td></tr><tr><td>No</td><td>4055</td><td>Reference</td><td></td><td>&lt;0.001</td></tr><tr><td>Yes</td><td>145</td><td>2.88</td><td>[1.69–4.93]</td><td></td></tr><tr><td colspan="5">...cassava field</td></tr><tr><td>No</td><td>2115</td><td>Reference</td><td></td><td>&lt;0.05</td></tr><tr><td>Yes</td><td>2085</td><td>1.37</td><td>[1.01–1.85]</td><td></td></tr></tbody></table>

<figure><img src="image_9.png" /><figcaption>**Fig. 4** Spatial clusters of *Plasmodium* infection (red-shaded circles) as detected by covariate-adjusted SaTScan analysis. White dots represent household locations in the survey, red dots for PCR-positive participants</figcaption></figure>

the other covariates (Fig. 4, Additional file 1: Tables ST4–ST5).

## Discussion

This is the first detailed study on the prevalence of *Plasmodium* infection and associated risk factors on population-level in Mondulkiri province in Cambodia. The overall prevalence of *Plasmodium* infection is consistent with that observed in the neighbouring province of Ratanakiri [10, 11, 22] or other endemic provinces in the West of the country [16].

*Plasmodium falciparum* has long been the predominant species countrywide [9, 34]. However, following a scale-up in the VMW programmes, a steady decrease in reported *P. falciparum* cases was observed in 2009–2011, while numbers of reported *P. vivax* cases increased [24]. In recent years, cases of both species were reported at an almost equal share [4] and in 2018 *P. vivax* accounted for approximately three quarters of reported cases [2]. That *P. vivax* started to predominate over *P. falciparum* in Cambodia even earlier is suggested by other studies in high-incidence provinces where molecular and serological diagnosis identified a higher prevalence of *P. vivax* than *P. falciparum* infections [11, 12, 15–19], consistent with the presented study in Mondulkiri.

7.2% of the *P. falciparum* infections could be identified by symptoms and a positive result by LM or RDTs, i.e. the diagnostics used by the health system, compared to only 2.2% for *P. vivax*. Low sensitivity of RDTs for *P. vivax* is a commonly reported problem [12, 35–38]. In addition, higher proportions of asymptomatic infections for *P. vivax* are regularly observed as the relapses can lead to higher levels of immunity and thus overall lower parasite densities [39, 40]. Data from studies using membrane feeding assays suggest that the densities of asexual stages and gametocytes in the asymptomatic, LM-positive samples in this survey are infectious to mosquitoes [14, 41–45]. It can thus not be ruled out that these subclinical infections contribute to the ongoing transmission in this area.

More asymptomatic infections, lower diagnostic sensitivity for *P. vivax*, high transmissibility, and the lack of radical cure by primaquine all might add up as possible explanations why the control efforts have been less successful against *P. vivax* than against *P. falciparum*. In order to address this high burden of *P. vivax*, Cambodia is now piloting the use of primaquine against *P. vivax* infections in four provinces. While an essential step, the effect of radical cure will be limited without point-of-care diagnostics that are more sensitive for *P. vivax* infections and ideally a test allowing the detection of hypnozoite carriage (irrespective of blood stage parasitaemia). New advances in the use of multiple antigens to detect antibodies as serological markers for recent exposure and thus for potential hypnozoite carriers are promising in this respect [46].

With as many as 40% of the inhabitants infected in the highest-prevalence village in this survey, it may be necessary to complement the current malaria control efforts by active or reactive case detection schemes [47, 48] to reduce the infected (and potentially infectious) reservoir and eventually reach the goal of malaria elimination. Targeting such resource-intensive interventions to high-risk groups will render them significantly more cost-efficient.
The similarity of the risk profiles for both *P. falciparum* and *P. vivax* presented here is encouraging as it suggests that such targeted interventions would appropriately address both of the two species.

This study emphasizes the predominant role of for-
est malaria transmission in explaining elevated risk of
infection in remote rural areas of Southeast Asia. Previ-
ous cross-sectional surveys have also identified an asso-
ciation of infection risk with either work in the forest
[15, 17, 49] or time spent therein [16, 50]. However, this
study is unique in retaining them both as statistically sig-
nificant risk factors in one multivariate model while also
adjusting for gender, age, socio-economic proxies, and
other behavioural covariates. While work in and travels
into the forest are most frequent in adolescent and adult
men who are usually considered the forest-related risk
group, this behaviour also occurred in women and from
children to participants in their sixties and thus actually
extends to a much broader range of the population.

On the relatively small spatial scale of 17 villages in an
area of 22 × 26 km², the prevalence ranged from less than
1% to above 40% in the dry season. The trend towards
higher prevalence in villages at the forest fringe and
inside the forest compared to those outside the forest is
in line with the widely accepted notion for forest-related
mosquito abundance and exposure in Cambodia. Other
studies have also identified a higher prevalence in villages
at close proximity to the forest [21, 49, 50]. It is appar-
ent that the association of risk with living near or inside
the forest was retained in this study even if adjusting for
demographic, socio-economic, occupational, and other
behavioural differences.

Not more than 60% of infections could be attributed
to the strong behavioural, forest-related risk factors. In
villages outside the forest or at the forest fringe, most
infections indeed occurred in working age men. This
predominance of this occupational risk group was much
lower in forest fringe villages and entirely lost in villages
situated inside the forest. Taking gender and age as prox-
ies for risk-related behaviour and occupational activities,
this could indicate that in villages outside the forest, risk
can be explained almost completely by such occupational
exposure. By contrast, for villages within the forest, the
more homogeneous presence of infection across all age
groups in both males and females indicates additional
exposure inside the village. This is in line with other stud-
ies in which household-level risk factors in forested areas
were important to explain infection risk [10] or infections
clustered in households over 2 years [51] in Ratanakiri,
the other Eastern province of Cambodia.

The current WHO report on malaria eradication calls for subnational stratification of intervention programmes [52, 53]. This study suggests that such a stratification of interventions could happen along a gradient of villages inside or outside the forest. Population-oriented interventions such as targeted mass drug administration or test and treat programmes with sensitive molecular or serological diagnostics [46, 54] (together with their costs and risk of overtreatment) could be justified in forest villages. By contrast, approaches targeted at risk groups only may be effective at lower costs for the NMCP and the local population in residential settings outside of the forest.

Conclusion

Despite a substantial reduction in malaria burden in
Cambodia over the last decades, this study demon-
strates that pockets of high malaria prevalence persist
in the country. Given that the vast majority of infections
were asymptomatic, the study strengthens the argument
to enhance malaria elimination efforts by measures of
(re-)active detection of also asymptomatic infections.
*Plasmodium vivax* infections were detected at a higher
prevalence than *P. falciparum* infections, more often
asymptomatic, and less detectable by RDT and LM. Con-
sequently, novel tools for the identification of hypnozo-
ite carriers could play a key role in the presented setting
as well as the roll-out of routine radical cure treatment.
The study corroborates the notion of forest malaria in
the Greater Mekong Subregion by demonstrating the
independent association of travels to the forest, of for-
est work, and of living in close proximity to forest with
malaria infection risk. However, the study also demon-
strates that infection risk is less confined to forest-goers
in sub-populations that already live in forested areas and
suggests within-village transmission therein. A focus of
interventions solely on forest-goers could thus be insuffi-
cient to reach the goal of nation-wide malaria elimination
in due time. In the light of the presented results, inter-
ventions could be targeted at whole villages inside the
forest, and targeted at stratified risk groups for villages
outside the forest.

**Supplementary information**

**Supplementary information** accompanies this paper at https://doi.org/10.1186/s12936-020-03482-4.

**Additional file** 1. Additional tables ST1–ST5 and additional figures S1–S3.
**Abbreviations**

ACT: Artemisinin-based combination therapy; AIC: Akaike Information Criterion; aOR: Adjusted odds ratio; G6PD: Glucose-6-phosphate dehydrogenase; GPS: Global positioning system; K⁺EDTA: Potassium ethylenediaminetetraacetic acid; LLIN: Long-lasting insecticidal net; LM: Light microscopy; MMW: Mobile malaria worker; N: Sample size; NMCP: National malaria control programme; PCR: Polymerase chain reaction; RDT: Rapid diagnostic test; VMW: Village malaria worker; WHO: World Health Organization.

**Acknowledgements**

The authors express their gratitude to the study participants, village heads, VMWs, local health facilities, and officials for making this study possible. We also thank the interviewers, technicians, and other study staff for their support.

**Authors' contributions**

AV, LR, BW, and IM conceived and designed the study and were overall responsible for the study. SK, AV, and BW implemented and managed the data collection, with support by SG, NK, and DL, and supervised the sample processing. SK conducted the data collection, supported by data management by MS and TO. TO, MS, SK, AV, LR, and IM designed the questionnaires. MW contributed to the statistical analysis. AP contributed the land cover analysis. MS analysed the data and wrote the first draft of the manuscript. AV, MW, TO, BW, LR, and IM contributed to data interpretation and writing. All authors read and approved the final manuscript.

**Funding**

This study is part of the International Centers of Excellence for Malaria Research programme "Understanding, tracking and eliminating malaria transmission in the Asia-Pacific Region", funded by the National Institutes of Health, MD, US (grant 1U19AI129392-01) and received additional funding by NHMRC (Australia, GNT1092789). IM is supported by an NHMRC Principal Research Fellowship (GNT1155075). LJR is supported by an NHMRC Career Development Fellowship (GNT1161627). MS is part of the PhD programme of the doctoral school ED393 Pierre Louis de santé publique and supported by the Sorbonne Université (contract n°2695/2017). AP is supported by the Pasteur Institute International Network PhD fellowship programme "Calmette & Yersin". The funding bodies had no role in the design of the study, the collection, analysis, and interpretation of data, and in writing the manuscript.

**Availability of data and materials**

The de-identified dataset analysed for this study is being made publicly available in ClinEpiDB repository, https://clinepidb.org/ce/app/. In the meantime, it is available from the corresponding author on reasonable request.

**Ethics approval and consent to participate**

This study was approved by the National Ethics Committee for Health Research, Ministry of Health, Kingdom of Cambodia (reference 239NECHR) and by the Institut Pasteur Institutional Review Board (reference 2017-03). Written informed consent to participate in the study was obtained from all participants (or their parent or legal guardian).

**Consent for publication**

Not applicable.

**Competing interests**

The authors declare that they have no competing interests.

**Author details**

Received: 24 June 2020 Accepted: 4 November 2020

Published online: 19 November 2020

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