Malaria micro-stratification using routine surveillance data in Western Kenya

High prevalence and risk of malaria among asymptomatic individuals from villages with high prevalence of artemisinin partial resistance in Kyerwa district of Kagera region, north-western Tanzania

BACKGROUND

Although Tanzania adopted and has been implementing effective interventions to control and eventually eliminate malaria, the disease is still a leading public health problem, and the country experiences heterogeneous transmission. Recent studies reported the emergence of parasites with artemisinin partial resistance (ART-R) in Kagera region with high prevalence (> 10.0%) in two districts of Karagwe and Kyerwa. This study assessed the prevalence and predictors/risk of malaria infections among asymptomatic individuals living in a hyperendemic area where ART-R has emerged in Kyerwa District of Kagera region, north-western Tanzania.

METHODS

This was a community-based cross-sectional survey which was conducted in July and August 2023 and involved individuals aged ≥ 6 months from five villages in Kyerwa district. Demographic, anthropometric, clinical, parasitological, type of house inhabited and socio-economic status (SES) data were collected using electronic capture tools run on Open Data Kit (ODK) software. Predictors/risks of malaria infections were determined by univariate and multivariate logistic regression, and the results were presented as crude (cORs) and adjusted odds ratios (aORs), with 95% confidence intervals (CIs).

RESULTS

Overall, 4454 individuals were tested using rapid diagnostic tests (RDTs), and 1979 (44.4%) had positive results. The prevalence of malaria infections ranged from 14.4% to 68.5% and varied significantly among the villages (p < 0.001). The prevalence and odds of infections were significantly higher in males (aOR = 1.28, 95% CI 1.08 -1.51, p = 0.003), school children (aged 5-≤10 years (aOR = 3.88, 95% CI 3.07-4.91, p < 0.001) and 10-≤15 years (aOR = 4.06, 95% CI 3.22-5.13, p < 0.001)) and among individuals who were not using bed nets (aOR = 1.22, 95% CI 1.03-1.46, p = 0.024). The odds of malaria infections were also higher in individuals with lower SES (aOR = 1.42, 95% CI 1.17-1.72, p < 0.001), and living in houses without windows (aOR = 2.08, 95% CI 1.46-2.96, p < 0.001), partially open (aOR = 1.33, 95% CI 1.11-1.58, p = 0.002) or fully open windows (aOR = 1.30, 95%CI 1.05-1.61, p = 0.015).

CONCLUSION

The five villages had a high prevalence of malaria infections and heterogeneity at micro-geographic levels. Groups with higher odds of malaria infections included school children, males, and individuals with low SES, living in poorly constructed houses or non-bed net users. These are important baseline data from an area with high prevalence of parasites with ART-R and will be useful in planning interventions for these groups, and in future studies to monitor the trends and potential spread of such parasites, and in designing a response to ART-R.

Malaria Stratification Mapping in Thailand to Support Prevention of Reestablishment

Thailand aims to eliminate malaria by 2026, with 46 of the country's 77 provinces already verified as malaria free. However, these provinces remain susceptible to the reestablishment of indigenous transmission that would threaten the national goal. Thus, the country is prioritizing national and subnational prevention of reestablishment (POR) planning while considering the spatial heterogeneity of the remaining malaria caseload. To support POR efforts, a novel nonmodeling method produced a malaria stratification map at the tambon (subdistrict) level, incorporating malaria case data, demographic data, and environmental factors. The stratification analysis categorized 7,425 tambons into the following four risk strata: Local Transmission (2.9%), At Risk for Transmission (3.1%), High Risk for Reintroduction (2.9%), and Low Risk for Reintroduction (91.1%). The stratification map will support the national program to target malaria interventions in remaining hotspots and mitigate the risk of transmission in malaria-free areas.

Adherence to national malaria treatment guidelines in private drug outlets: a cross-sectional survey in the malaria-endemic Kisumu County, Kenya

BACKGROUND

Malaria prevalence in Kenya is 6%, with a three-fold higher prevalence in western Kenya. Adherence to malaria treatment guidelines improves care for suspected malaria cases and can reduce unnecessary anti-malarial use. Data on adherence to guidelines in retail drug outlets (DOs) is limited, yet approximately 50% of people with fever access treatment first in these outlets. This study assessed adherence to the national malaria treatment guidelines among DOs in a high transmission area of Western Kenya.

METHODS

In a cross-sectional survey of DOs in Kisumu Central and Seme sub-counties in 2021, DO staff were interviewed using structured questionnaires to assess outlet characteristics (location, testing services), staff demographics (age, sex, training), and health system context (supervision, inspection). Mystery shoppers (research assistants disguised as clients) observed malaria management practices and recorded observations on a standardized tool. Adherence was defined as dispensing artemether-lumefantrine (AL) to patients with a confirmed positive test, accompanied by appropriate medication counseling. Logistic regression was used to test for association between adherence to guidelines and DO-related factors.

RESULTS

None of the 70 DOs assessed had a copy of the guidelines, and 60 (85.7%) were in an urban setting. Staff adhered to the guidelines in 14 (20%) outlets. The odds of adherence were higher among staff who had a bachelor's degree {odds ratio (OR) 6.0, 95% confidence interval (95% CI) 1.66-21.74}, those trained on malaria rapid diagnostic test (RDT) {OR 4.4, 95% CI 1.29-15.04}, and those who asked about patient's symptoms {OR 3.6, 95% CI 1.08-12.25}. DOs that had higher odds of adherence included those with functional thermometers {OR 5.3, 95% CI 1.46-19.14}, those recently inspected (within three months) by Pharmacy and Poisons Board (PPB) {OR 9.4, 95% CI 2.55-34.67}, and those with all basic infrastructure {OR 3.9, 95% CI 1.01-15.00}. On logistic regression analysis, recent PPB inspection {adjusted OR (AOR) 4.6, 95% CI 1.03-20.77} and malaria RDT-trained staff (aOR 4.5, 95% CI 1.02-19.84) were independently associated with adherence.

CONCLUSION

Most outlets didn't adhere to malaria guidelines. Regular interaction with regulatory bodies could improve adherence. Ministry of Health should enhance private sector engagement and train DOs on RDT use.

Spatio-temporal modelling of routine health facility data for malaria risk micro-stratification in mainland Tanzania

As malaria transmission declines, the need to monitor the heterogeneity of malaria risk at finer scales becomes critical to guide community-based targeted interventions. Although routine health facility (HF) data can provide epidemiological evidence at high spatial and temporal resolution, its incomplete nature of information can result in lower administrative units without empirical data. To overcome geographic sparsity of data and its representativeness, geo-spatial models can leverage routine information to predict risk in un-represented areas as well as estimate uncertainty of predictions. Here, a Bayesian spatio-temporal model was applied on malaria test positivity rate (TPR) data for the period 2017-2019 to predict risks at the ward level, the lowest decision-making unit in mainland Tanzania. To quantify the associated uncertainty, the probability of malaria TPR exceeding programmatic threshold was estimated. Results showed a marked spatial heterogeneity in malaria TPR across wards. 17.7 million people resided in areas where malaria TPR was high (≥ 30; 90% certainty) in the North-West and South-East parts of Tanzania. Approximately 11.7 million people lived in areas where malaria TPR was very low (< 5%; 90% certainty). HF data can be used to identify different epidemiological strata and guide malaria interventions at micro-planning units in Tanzania. These data, however, are imperfect in many settings in Africa and often require application of geo-spatial modelling techniques for estimation.

Malaria Burden Stratification in Malawi- A report of a consultative workshop to inform the 2023-2030 Malawi Malaria Strategic Plan

Background: Malawi's National Malaria Control Programme (NMCP) is developing a new strategic plan for 2023-2030 to combat malaria and recognizes that a blanket approach to malaria interventions is no longer feasible. To inform this new strategy, the NMCP set up a task force comprising 18 members from various sectors, which convened a meeting to stratify the malaria burden in Malawi and recommend interventions for each stratum. Methods: The burden stratification workshop took place from November 29 to December 2, 2022, in Blantyre, Malawi, and collated essential data on malaria burden indicators, such as incidence, prevalence, and mortality. Workshop participants reviewed the malaria burden and intervention coverage data to describe the current status and identified the districts as a appropriate administrative level for stratification and action. Two scenarios were developed for the stratification, based on composites of three variables. Scenario 1 included incidence, prevalence, and under-five all-cause mortality, while Scenario 2 included total malaria cases, prevalence, and under-five all-cause mortality counts. The task force developed four burden strata (highest, high, moderate, and low) for each scenario, resulting in a final list of districts assigned to each stratum. Results: The task force concluded with 10 districts in the highest-burden stratum (Nkhotakota, Salima, Mchinji, Dowa, Ntchisi, Mwanza, Likoma, Lilongwe, Kasungu and Mangochi) 11 districts in the high burden stratum (Chitipa, Rumphi, Nkhata Bay, Dedza, Ntcheu, Neno, Thyolo, Nsanje, Zomba, Mzimba and Mulanje) and seven districts in the moderate burden stratum (Karonga, Chikwawa, Balaka, Machinga, Phalombe, Blantyre, and Chiradzulu). There were no districts in the low-burden stratum. Conclusion: The next steps for the NMCP are to review context-specific issues driving malaria transmission and recommend interventions for each stratum. Overall, this burden stratification workshop provides a critical foundation for developing a successful malaria strategic plan for Malawi.

The use of routine health facility data for micro-stratification of malaria risk in mainland Tanzania

Background

Current efforts to estimate the spatially diverse malaria burden in malaria-endemic countries largely involve the use of epidemiological modelling methods for describing temporal and spatial heterogeneity using sparse interpolated prevalence data from periodic cross-sectional surveys. However, more malaria-endemic countries are beginning to consider local routine data for this purpose. Nevertheless, routine information from health facilities (HFs) remains widely under-utilized despite improved data quality, including increased access to diagnostic testing and the adoption of the electronic District Health Information System (DHIS2). This paper describes the process undertaken in mainland Tanzania using routine data to develop a high-resolution, micro-stratification risk map to guide future malaria control efforts.

Methods

Combinations of various routine malariometric indicators collected from 7098 HFs were assembled across 3065 wards of mainland Tanzania for the period 2017–2019. The reported council-level prevalence classification in school children aged 5–16 years (PfPR5–16) was used as a benchmark to define four malaria risk groups. These groups were subsequently used to derive cut-offs for the routine indicators by minimizing misclassifications and maximizing overall agreement. The derived-cutoffs were converted into numbered scores and summed across the three indicators to allocate wards into their overall risk stratum.

Results

Of 3065 wards, 353 were assigned to the very low strata (10.5% of the total ward population), 717 to the low strata (28.6% of the population), 525 to the moderate strata (16.2% of the population), and 1470 to the high strata (39.8% of the population). The resulting micro-stratification revealed malaria risk heterogeneity within 80 councils and identified wards that would benefit from community-level focal interventions, such as community-case management, indoor residual spraying and larviciding.

Conclusion

The micro-stratification approach employed is simple and pragmatic, with potential to be easily adopted by the malaria programme in Tanzania. It makes use of available routine data that are rich in spatial resolution and that can be readily accessed allowing for a stratification of malaria risk below the council level. Such a framework is optimal for supporting evidence-based, decentralized malaria control planning, thereby improving the effectiveness and allocation efficiency of malaria control interventions.

Geographic accessibility to public and private health facilities in Kenya in 2021: An updated geocoded inventory and spatial analysis

Objectives

To achieve universal health coverage, adequate geographic access to quality healthcare services is vital and should be characterized periodically to support planning. However, in Kenya, previous assessments of geographic accessibility have relied on public health facility lists only, assembled several years ago. Here, for the first time we assemble a geocoded list of public and private health facilities in 2021 and make use of this updated list to interrogate geographical accessibility to all health providers.

Methods

Existing health provider lists in Kenya were accessed, merged, cleaned, harmonized, and assigned a unique geospatial location. The resultant master list was combined with road network, land use, topography, travel barriers and healthcare-seeking behavior within a geospatial framework to estimate travel time to the nearest (i) private, (ii) public, and (iii) both (public and private-PP) health facilities through a travel scenario involving walking, bicycling and motorized transport. The proportion of the population within 1 h and outside 2-h was computed at 300 × 300 spatial resolution and aggregated at subnational units used for decision-making. Areas with a high disease prevalence for common infections that were outside 1-h catchment (dual burden) were also identified to guide prioritization.

Results

The combined database contained 13,579 health facilities, both in the public (55.5%) and private-for-profit sector (44.5%) in 2021. The private health facilities' distribution was skewed toward the urban counties. Nationally, average travel time to the nearest health facility was 130, 254, and 128 min while the population within 1-h was 89.4, 80.5, and 89.6% for the public, private and PP health facility, respectively. The population outside 2-h were 6% for public and PP and 11% for the private sector. Mean travel time across counties was heterogeneous, while the population within 1-h ranged between 38 and 100% in both the public sector and PP. Counties in northwest and southeast Kenya had a dual burden.

Conclusion

Continuous updating and geocoding of health facilities will facilitate an improved understanding of healthcare gaps for planning. Heterogeneities in geographical access continue to persist, with some areas having a dual burden and should be prioritized toward reducing health inequities and attaining universal health coverage.

Sub-national tailoring of malaria interventions in Mainland Tanzania: simulation of the impact of strata-specific intervention combinations using modelling

Background

To accelerate progress against malaria in high burden countries, a strategic reorientation of resources at the sub-national level is needed. This paper describes how mathematical modelling was used in mainland Tanzania to support the strategic revision that followed the mid-term review of the 2015–2020 national malaria strategic plan (NMSP) and the epidemiological risk stratification at the council level in 2018.

Methods

Intervention mixes, selected by the National Malaria Control Programme, were simulated for each malaria risk strata per council. Intervention mixes included combinations of insecticide-treated bed nets (ITN), indoor residual spraying, larval source management, and intermittent preventive therapies for school children (IPTsc). Effective case management was either based on estimates from the malaria indicator survey in 2016 or set to a hypothetical target of 85%. A previously calibrated mathematical model in OpenMalaria was used to compare intervention impact predictions for prevalence and incidence between 2016 and 2020, or 2022.

Results

For each malaria risk stratum four to ten intervention mixes were explored. In the low-risk and urban strata, the scenario without a ITN mass campaign in 2019, predicted high increase in prevalence by 2020 and 2022, while in the very-low strata the target prevalence of less than 1% was maintained at low pre-intervention transmission intensity and high case management. In the moderate and high strata, IPTsc in addition to existing vector control was predicted to reduce the incidence by an additional 15% and prevalence by 22%. In the high-risk strata, all interventions together reached a maximum reduction of 76%, with around 70% of that reduction attributable to high case management and ITNs. Overall, the simulated revised NMSP was predicted to achieve a slightly lower prevalence in 2020 compared to the 2015–2020 NMSP (5.3% vs 6.3%).

Conclusion

Modelling supported the choice of intervention per malaria risk strata by providing impact comparisons of various alternative intervention mixes to address specific questions relevant to the country. The use of a council-calibrated model, that reproduces local malaria trends, represents a useful tool for compiling available evidence into a single analytical platform, that complement other evidence, to aid national programmes with decision-making processes.

Supplementary Information

The online version contains supplementary material available at 10.1186/s12936-022-04099-5.

Stratification of malaria incidence in Papua New Guinea (2011-2019): Contribution towards a sub-national control policy

Malaria risk in Papua New Guinea (PNG) is highly heterogeneous, between and within geographical regions, which is operationally challenging for control. To enhance targeting of malaria interventions in PNG, we investigated risk factors and stratified malaria incidence at the level of health facility catchment areas. Catchment areas and populations of 808 health facilities were delineated using a travel-time accessibility approach and linked to reported malaria cases (2011-2019). Zonal statistics tools were used to calculate average altitude and air temperature in catchment areas before they were spatially joined with incidence rates. In addition, empirical Bayesian kriging (EBK) was employed to interpolate incidence risk strata across PNG. Malaria annual incidence rates are, on average, 186.3 per 1000 population in catchment areas up to 600 m, dropped to 98.8 at (800-1400) m, and to 24.1 cases above 1400 m altitude. In areas above the two altitudinal thresholds 600m and 1400m, the average annual temperature drops below 22°C and 17°C, respectively. EBK models show very low- to low-risk strata (<100 cases per 1000) in the Highlands, National Capital District and Bougainville. In contrast, patches of high-risk (>200 per 1000) strata are modelled mainly in Momase and Islands Regions. Besides, strata with moderate risk (100-200) predominate throughout the coastal areas. While 35.7% of the PNG population (estimated 3.33 million in 2019) lives in places at high or moderate risk of malaria, 52.2% (estimated 4.88 million) resides in very low-risk areas. In five provinces, relatively large proportions of populations (> 50%) inhabit high-risk areas: New Ireland, East and West New Britain, Sandaun and Milne Bay. Incidence maps show a contrast in malaria risk between coastal and inland areas influenced by altitude. However, the risk is highly variable in low-lying areas. Malaria interventions should be guided by sub-national risk levels in PNG.