In this study, we used an array of analytic approaches to explore spatial and temporal patterns of malaria API and test positivity based on P. falciparum and P. vivax data aggregated at the township or state/region level from 2011 to 2017 in Myanmar. The NMCP categorizes geographic locations based on reported case incidence levels (API), with three overarching categories that have corresponding public health approaches: less than 1 per 1000 people per year (pre-elimination), 1 to less than 10 per 1000 people per year (moderate transmission), and above 10 per 1000 people per year (high transmission) [21]. Our findings showed that more than 50% of townships reached malaria pre-elimination by 2017.
The reported P. falciparum API maintained a continuous decline throughout the study period, which may be mainly related to increased access to diagnoses and treatment with artemisinin combination therapies [22]. However, the reported P. vivax API malaria showed a more gradual decline, probably associated with the intrinsic biological features of the P. vivax, including relapses induced by hypnozoites [23], missed diagnosis because of the low density of infection, lower accuracy from widely used rapid diagnostic tests [24], the early production of gametocytes favoring continuous transmission, and the high proportion of asymptomatic infections [25].
Some patterns with vivax malaria may be related to the increased roll-out of rapid diagnostic tests that detect both falciparum and vivax malaria (the first rapid diagnostic tests were only capable of detecting falciparum malaria). This roll-out corresponds to the initial increase in vivax cases up to 2013 when they began to decrease (apparent in Fig. 1) and could be related to an increased capacity for diagnosing vivax malaria. A few studies have also shown that vivax relapses often follow the treatment of falciparum malaria [26, 27]. It is, therefore, possible that widespread increased treatment of falciparum malaria partially drove the short increase of reported vivax cases prior to their decline in 2013. Complete cure of vivax malaria is hindered by the risk of hemolysis in G6PD deficient (G6PDd) individuals post primaquine and other 8-aminoquinoline antimalarials. This region has a high prevalence of G6PD deficiency along the China-Myanmar border (16.9%) and Thailand-Myanmar border (13.7%) [28, 29], as well as in western and central Myanmar (10% and 6.8%, respectively) [30, 31]. The need for PQ to radically cure vivax malaria, coupled with the risk of treating G6PDd patients and the difficulties in diagnosing G6PD deficiency in field settings, remains a challenge for adequately addressing this malaria species.
The apparent declines in the API and test positivity of P. falciparum and P. vivax from 2011 to 2017 in Myanmar may be illustrative of the impact of combined efforts by governmental and non-governmental organizations to eliminate malaria in the nation [32, 33]. Previous studies showed significant associations between declining reported cases and the following factors: long-lasting insecticidal net (LLIN)/ insecticide-treated bed net (ITN) distribution, indoor residual spraying (IRS), the concentration of village health workers, amount of health worker training, development of volunteers, socioeconomic status, and improved ACT availability [32, 34–39].
Our analyses also showed malaria to peak and cluster temporally from approximately May and into November. This corresponds with the rainy season in Myanmar, and the pattern has been well-described in several other studies [40, 41]. A second peak was apparent in some high burden states/regions (especially Rakhine and Chin states). This pattern has likewise been described in other locations but has not been fully explained. It may correspond to differences in human-mosquito exposure that are related to seasonal agriculture (the second peak often corresponds with rice harvesting season [42, 43], with changes in mosquito abundance [44], or other factors such as decreased mosquito net use after the rains have ceased [45].
Malaria exhibited significant spatial clustering during the study period as well. We found clustering of P. falciparum in the western and northern parts of Chin State, Kachin State, and Sagaing Region. From the Poisson model, we found that primary and secondary clusters of P. falciparum also persisted in the northern and western regions of Myanmar. Although the sizes and locations of P. falciparum clusters became gradually smaller over time, the cluster locations were relatively stable in the western and northern regions [32, 46]. A recent study found P. falciparum was the predominant species accounting for more than 80% of all infections in Paletwa Township of Chin State [43]. Conversely, primary and secondary clusters of P. vivax malaria changed over the seven-year study period. The maps of incidence and test positivity demonstrate that high-burden areas of P. vivax malaria tend to migrate west to east. In the last three years of the study period, P. vivax clustered in eastern Kachin State bordering China and southern Shan State and Kayah State bordering Thailand. Previous studies showed P. vivax is the predominant species along many of these international borders [25, 47, 48].
Spatial patterns in malaria can be largely driven by environmental factors that vary across landscapes [49, 50]. Forested areas have long been associated with malaria in Southeast Asia [51, 52]. The hilly and mountainous areas along the international borders have had less economic development, at least partially, as a result of long-standing conflict in these areas. Changes in environmental attributes (such as forest cover) and other socioeconomic factors can lead to changes in the burden of malaria [53].
Our analyses showed that malaria cases were highest in townships with a mid-level elevation (mean elevation of approximately 500–600 meters) and that falciparum cases were associated with high levels of vegetation (measured using EVI). The latter corresponds to the well-known macro associations between forests and falciparum malaria. While malaria elimination efforts have increased in the last decades in Myanmar, deforestation has also increased [54], and both may impact malaria transmission.
This study has a few limitations. First, the data might not be accurate for some years since the data were mainly derived from government health centers (BHS) and the program’s village malaria volunteers (VMV). With regard to the temporal trends, it is noteworthy that the coverage of health care facilities has improved drastically in Myanmar over the last several years. For example, many community-based health clinics were set up in Kayin State (beginning around 2014 and 2015). The increase in malaria diagnosis and surveillance at first might give an impression of increased cases when in fact it is the result of increased diagnosis. We control for the influence of testing through the test positivity metric and by including the number of tests in our regressions. However, we cannot control for missing data (either through problems with surveillance systems or from a sparsity of clinics in some regions).
A second limitation was the deficiency of monthly data, which were only available at the state/region level from 2011 to 2016. More granular data (i.e., at the village or village tract level) would be superior. Lastly, the coup in 2021 and COVID-19 pandemic (beginning in 2020) have disrupted many malaria control and elimination efforts. From passive case surveillance in Laiza town in Kachin State along the China-Myanmar border, malaria cases declined from 2016 to 2019 but increased rapidly beginning in 2020 (data not published). This suggests that local governments or organizations should resume malaria surveillance and implementation of interventions as much as possible. Meanwhile, the bordering countries should pay attention to the importation of malaria cases.