Malaria hot spot along the foothills of Rakhine state, Myanmar: geospatial distribution of malaria cases in townships targeted for malaria elimination

Ownership and use of insecticide-treated nets in Myanmar: insights from a nationally representative demographic and health survey

BACKGROUND

Malaria poses a substantial public health threat in Myanmar, indicating the need for rigorous efforts to achieve elimination of the disease nationwide by 2030. The use of insecticide-treated nets (ITNs) forms part of a pivotal strategy for preventing transmission. This study explored the ownership and use of ITNs in Myanmar and identified factors associated with non-use of ITNs.

METHODS

Household datasets from the 2015-2016 Myanmar Demographic and Health Survey were utilised, which encompassed all household members except children under the age of five. Descriptive statistics and inferential tests, including simple and multiple logistics regression models and Pearson correlations, were employed for analysis. All analyses, taking the two-stage stratified cluster sampling design into account, used weighting factors and the "svyset" command in STATA. The ownership and use of bed nets were also visualised in QGIS maps.

RESULTS

Among the 46,507 participants, 22.3% (95% CI 20.0%, 24.5%) had access to ITNs, with only 15.3% (95% CI 13.7, 17.1%) sleeping under an ITN the night before the survey. Factors associated with the non-use of ITNs included age category (15-34 years-aOR: 1.17, 95% CI 1.01, 1.30; 50+ years-aOR: 1.19, 95% CI 1.06, 1.33), location (delta or lowland-aOR: 5.39, 95% CI 3.94, 7.38; hills-aOR: 1.80, 95% CI 1.20, 2.71; plains-aOR: 3.89, 95% CI 2.51, 6.03), urban residency (aOR: 1.63, 95% CI 1.22, 2.17), and wealth quintile (third-aOR: 1.38, 95% CI 1.08, 1.75; fourth-aOR: 1.65, 95% CI 1.23, 2.23; fifth-aOR: 1.47, 95% CI 1.02, 2.13). A coherent distribution of the ownership and use of ITNs was seen across all states/regions, and a strong correlation existed between the ownership and use of ITNs (r: 0.9795, 95% CI 0.9377, 0.9933, alpha < 0.001).

CONCLUSIONS

This study identified relatively low percentages of ITN ownership and use, indicating the need to increase the distribution of ITNs to achieve the target of at least one ITN per every two people. Strengthening the use of ITNs requires targeted health promotion interventions, especially among relatively affluent individuals residing in delta or lowland areas, hills, and plains.

Geo-ecological, shoreline dynamic, and flooding impacts of Cyclonic Storm Mocha: A geospatial analysis

This research comprehensively assesses the aftermath of Cyclonic Storm Mocha, focusing on the coastal zones of Rakhine State and the Chittagong Division, spanning Myanmar and Bangladesh. The investigation emphasizes the impacts on coastal ecology, shoreline dynamics, flooding patterns, and meteorological variations. Employed were multiple vegetation indices-Normalized Difference Vegetation Index (NDVI), Enhanced Vegetation Index (EVI), Modified Vegetation Condition Index (mVCI), Disaster Vegetation Damage Index (DVDI), and Fractional Vegetation Cover (FVC)-to evaluate ecological consequences. The Digital Shoreline Assessment System (DSAS) aided in determining shoreline alterations pre- and post-cyclone. Soil exposure and flood extents were scrutinized using the Bare Soil Index (BSI) and Modified Normalized Difference Water Index (MNDWI), respectively. Additionally, the study encompassed an analysis of microclimatic variables, comparing meteorological data across pre- and post-cyclone periods. Findings indicate significant ecological impacts: an estimated 8985.46 km2 of dense vegetation (NDVI >0.6) was adversely affected. Post-cyclone, there was a discernible reduction in EVI values. The mean mVCI shifted negatively from -0.18 to -0.33, and the mean FVC decreased from 0.39 to 0.33. The DVDI underscored considerable vegetation damage in various areas, underscoring the cyclone's extensive impact. Meteorological analysis revealed a 245 % increase in rainfall (20.22 mm on May 14, 2023 compared to the May average of 5.86 mm), and significant increases in relative humidity (14 %) and wind speed (205 %). Erosion was observed along 74.60 % of the studied shoreline. These insights are pivotal for developing comprehensive strategies aimed at the rehabilitation and conservation of critical coastal ecosystems. They provide vital data for emergency response initiatives and offer resources for entities engaged in enhancing coastal resilience and protecting local community livelihoods.

Reactive surveillance and response strategies for malaria elimination in Myanmar: a literature review

Myanmar, a country in Greater Mekong Sub-region, aims to eliminate malaria by 2030. To achieve malaria elimination, Myanmar adopted a reactive surveillance and response strategy of malaria case notification within 1 day and case investigation, foci investigation and response activities within 7 days. A literature review was conducted to gain a better understanding of how the reactive surveillance and response strategies are being implemented in Myanmar including enablers and barriers to their implementation. Only two assessments of the completeness and timeliness of reactive surveillance and response strategy in Myanmar have been published to date. The proportion of positive cases notified within one day was 27.9% and the proportion of positive cases investigated within 7 days as recommended by the national guidelines varied from 32.5 to 91.8% under different settings in reported studies. Strong collaboration between the National Malaria Control Programme and implementing partners, and adequate human resource and financial support contributed to a successful and timely implementation of reactive surveillance and response strategy. Documented enablers for successful implementation of reactive surveillance and response strategy included frontline health workers having good knowledge of reactive surveillance and response activities and availability of Basic Health Staff for timely implementation of foci response activities. Barriers for implementation of reactive surveillance and response activities were also identified, including shortage of human resources especially in hard-to-reach settings, limited mobile phone network services and internet coverage leading to delays in timely notification of malaria cases, lengthy and complex case investigation forms and different reporting systems between Basic Health Staff and volunteers.

Spatio-temporal trends of malaria incidence from 2011 to 2017 and environmental predictors of malaria transmission in Myanmar

BACKGROUND

Myanmar bears the heaviest malaria burden in the Greater Mekong Subregion (GMS). This study assessed the spatio-temporal dynamics and environmental predictors of Plasmodium falciparum and Plasmodium vivax malaria in Myanmar.

METHODS

Monthly reports of malaria cases at primary health centers during 2011-2017 were analyzed to describe malaria distribution across Myanmar at the township and state/region levels by spatial autocorrelation (Moran index) and spatio-temporal clustering. Negative binomial generalized additive models identified environmental predictors for falciparum and vivax malaria, respectively.

RESULTS

From 2011 to 2017, there was an apparent reduction in malaria incidence in Myanmar. Malaria incidence peaked in June each year. There were significant spatial autocorrelation and clustering with extreme spatial heterogeneity in malaria cases and test positivity across the nation (P < 0.05). Areas with higher malaria incidence were concentrated along international borders. Primary clusters of P. falciparum persisted in western townships, while clusters of P. vivax shifted geographically over the study period. The primary cluster was detected from January 2011 to December 2013 and covered two states (Sagaing and Kachin). Annual malaria incidence was highest in townships with a mean elevation of 500‒600 m and a high variance in elevation (states with both high and low elevation). There was an apparent linear relationship between the mean normalized difference vegetative index and annual P. falciparum incidence (P < 0.05).

CONCLUSION

The decreasing trends reflect the significant achievement of malaria control efforts in Myanmar. Prioritizing the allocation of resources to high-risk areas identified in this study can achieve effective disease control.

Determining target populations for leprosy prophylactic interventions: a hotspot analysis in Indonesia

Background

Leprosy incidence remained at around 200,000 new cases globally for the last decade. Current strategies to reduce the number of new patients include early detection and providing post-exposure prophylaxis (PEP) to at-risk populations. Because leprosy is distributed unevenly, it is crucial to identify high-risk clusters of leprosy cases for targeting interventions. Geographic Information Systems (GIS) methodology can be used to optimize leprosy control activities by identifying clustering of leprosy cases and determining optimal target populations for PEP.

Methods

The geolocations of leprosy cases registered from 2014 to 2018 in Pasuruan and Pamekasan (Indonesia) were collected and tested for spatial autocorrelation with the Moran’s I statistic. We did a hotspot analysis using the Heatmap tool of QGIS to identify clusters of leprosy cases in both areas. Fifteen cluster settings were compared, varying the heatmap radius (i.e., 500 m, 1000 m, 1500 m, 2000 m, or 2500 m) and the density of clustering (low, moderate, and high). For each cluster setting, we calculated the number of cases in clusters, the size of the cluster (km²), and the total population targeted for PEP under various strategies.

Results

The distribution of cases was more focused in Pasuruan (Moran’s I = 0.44) than in Pamekasan (0.27). The proportion of total cases within identified clusters increased with heatmap radius and ranged from 3% to almost 100% in both areas. The proportion of the population in clusters targeted for PEP decreased with heatmap radius from > 100% to 5% in high and from 88 to 3% in moderate and low density clusters. We have developed an example of a practical guideline to determine optimal cluster settings based on a given PEP strategy, distribution of cases, resources available, and proportion of population targeted for PEP.

Conclusion

Policy and operational decisions related to leprosy control programs can be guided by a hotspot analysis which aid in identifying high-risk clusters and estimating the number of people targeted for prophylactic interventions.

Supplementary Information

The online version contains supplementary material available at 10.1186/s12879-022-07103-0.

Malaria Risk Map Using Spatial Multi-Criteria Decision Analysis along Yunnan Border During the Pre-elimination Period

In moving toward malaria elimination, finer scale malaria risk maps are required to identify hotspots for implementing surveillance–response activities, allocating resources, and preparing health facilities based on the needs and necessities at each specific area. This study aimed to demonstrate the use of multi-criteria decision analysis (MCDA) in conjunction with geographic information systems (GISs) to create a spatial model and risk maps by integrating satellite remote-sensing and malaria surveillance data from 18 counties of Yunnan Province along the China–Myanmar border. The MCDA composite and annual models and risk maps were created from the consensus among the experts who have been working and know situations in the study areas. The experts identified and provided relative factor weights for nine socioeconomic and disease ecology factors as a weighted linear combination model of the following: ([Forest coverage × 0.041] + [Cropland × 0.086] + [Water body × 0.175] + [Elevation × 0.297] + [Human population density × 0.043] + [Imported case × 0.258] + [Distance to road × 0.030] + [Distance to health facility × 0.033] + [Urbanization × 0.036]). The expert-based model had a good prediction capacity with a high area under curve. The study has demonstrated the novel integrated use of spatial MCDA which combines multiple environmental factors in estimating disease risk by using decision rules derived from existing knowledge or hypothesized understanding of the risk factors via diverse quantitative and qualitative criteria using both data-driven and qualitative indicators from the experts. The model and fine MCDA risk map developed in this study could assist in focusing the elimination efforts in the specifically identified locations with high risks.