Discovering the cost of care: consumer, provider, and retailer surveys shed light on the determinants of malaria health-seeking behaviours

Challenges in diagnosis of clinical and subclinical Plasmodium falciparum infections in Ghana and feasibility of reactive interventions to shrink the subclinical reservoir

BACKGROUND

Reactive case detection (RCD) aims to reduce malaria transmission stemming from asymptomatic carriers. Symptomatic individuals diagnosed with malaria at a health centre are followed to their households, where members of the index case and neighbouring households are tested and treated for malaria. An RCD programme was tested in the Ashanti region of Ghana in order to study diagnostic accuracy in the hospital and household settings, assess the prevalence of subclinical infections and possible clustering in index case households, and identify operational challenges for future RCD programmes. Currently, transmission in this region is high, but reactive interventions might become an option once transmission is reduced.

METHODS

264 febrile individuals were enrolled at the Mankranso Government Hospital and tested for malaria using rapid diagnostic tests (RDT). From the pool of RDT-positive febrile index cases, 14 successful RCD follow-ups were conducted, and 233 individuals were enrolled from the index case, neighbour, and control households. The sensitivity of diagnostic tools for clinical and subclinical cases was compared, including RDT, expert microscopy by World Health Organization-certified microscopists, field microscopy, and qPCR.

RESULTS

Poor diagnosis and low receptivity to RCD-style follow-ups were major limitations to a successful and effective RCD programme. Field microscopy detected only 49% of clinical infections compared to RDT. 54% of individuals did not agree to a follow-up, and 66% of attempted follow-ups failed. The system effectiveness of RCD, calculated as the product of correctly diagnosed index cases, successful follow-ups, and proportion of asymptomatic infections detected by RDT, was very low at 4.0%.

CONCLUSIONS

Due to low system effectiveness and the endemic nature of the disease setting in which asymptomatic prevalence is high and infections are not clustered around index case households, RCD is currently not a feasible option for malaria control in this region. The operational challenges identified through this study may help inform future reactive intervention programme designs once transmission is reduced.

Treatment-seeking behavior for malaria among communities in Indonesia: A systematic review

Indonesia stands as one of the nine malaria-endemic countries in Southeast Asia with a total of 443,530 cases in 2022. Eastern Indonesia is listed as an area with high malaria endemicity and the Indonesian government has set a target of eliminating malaria by 2030. From 2010 to 2014, the number of malaria cases decreased but stagnated until 2020 and have continued to increase. Stagnation may occur as a result of many non-medical treatment-seeking behaviors. The aim of this systematic review was to provide a summary and overview of malaria treatment-seeking behavior among communities in several regions in Indonesia. The searches were conducted through four databases (Cochrane, PubMed, Google Scholar, and ScienceDirect) using medical subject headings (MeSH) "treatment-seeking behavior" OR "health-seeking behavior" AND "malaria" AND "Indonesia". This systematic review was limited to studies conducted in Indonesia that were published between 2013 and 2023 using either a quantitative or qualitative approach. Out of 2831 studies, a total of thirteen studies were included. The pattern of seeking malaria treatment varied between doing nothing or no action, self-treatment (purchasing drugs at pharmacies and consuming leftover medicines), traditional medicine, and medical treatment (public health facilities or malaria control clinics). Those behaviors are attributed to education level, socioeconomic level, occupation, distance from home to health facilities, geographical conditions, and people's perceptions of malaria and antimalarial medicines. There is still a range of malaria treatment-seeking behavior outside of recommended medical treatment in communities in several regions in Indonesia. The phenomenon of medical pluralism and syncretism requires approaches from various sectors in order to achieve a malaria-free Indonesia by 2030.

Combining school-catchment area models with geostatistical models for analysing school survey data from low-resource settings: Inferential benefits and limitations

School-based sampling has been used to inform targeted responses for malaria and neglected tropical diseases. Standard geostatistical methods for mapping disease prevalence use the school location to model spatial correlation, which is questionable since exposure to the disease is more likely to occur in the residential location. In this paper, we propose to overcome the limitations of standard geostatistical methods by introducing a modelling framework that accounts for the uncertainty in the location of the residence of the students. By using cost distance and cost allocation models to define spatial accessibility and in absence of any information on the travel mode of students to school, we consider three school catchment area models that assume walking only, walking and bicycling and, walking and motorized transport. We illustrate the use of this approach using two case studies of malaria in Kenya and compare it with the standard approach that uses the school locations to build geostatistical models. We argue that the proposed modelling framework presents several inferential benefits, such as the ability to combine data from multiple surveys some of which may also record the residence location, and to deal with ecological bias when estimating the effects of malaria risk factors. However, our results show that invalid assumptions on the modes of travel to school can worsen the predictive performance of geostatistical models. Future research in this area should focus on collecting information on the modes of transportation to school which can then be used to better parametrize the catchment area models.

Costs and barriers faced by households seeking malaria treatment in the Upper River Region, The Gambia

BACKGROUND

Malaria transmission in The Gambia decreased substantially over the last 20 years thanks to the scale-up of control interventions. However, malaria prevalence is still relatively high in eastern Gambia and represents both a health and a financial burden for households. This study aims to quantify the out-of-pocket costs and productivity losses of seeking malaria treatment at household level.

METHODS

A household survey was carried out through in-person interviews. Respondents were asked about malaria prevention methods, their treatment-seeking behaviour, and any costs incurred for transport, services, food, and/or overnight stays. A bottom-up costing approach was used to calculate the unit cost of treatment and a tobit regression approach to investigate cost drivers.

RESULTS

The survey included 864 respondents, mainly subsistence farmers. Most respondents (87%) considered malaria to be a problem affecting their ability to perform their regular duties. Respondents preferred going to a health facility for treatment. The primary reason for not going was related to costs; 70% of respondents incurred costs for seeking health care, with a median of £3.62 (IQR: £1.73 to £6.10). The primary driver of cost was living in one of the villages that are off the main road and/or far from health facilities. 66% reported productivity loss of 5 working days on average during a malaria episode of them or their child.

CONCLUSIONS

Although malaria prevalence is decreasing and treatment is provided free of charge, households seeking treatment are confronted with out-of-pocket expenditures and lost working days; particularly in remote villages.

Connected diagnostics to improve accurate diagnosis, treatment, and conditional payment of malaria services in Kenya

Background

In sub-Saharan Africa, the material and human capacity to diagnose patients reporting with fever to healthcare providers is largely insufficient. Febrile patients are typically treated presumptively with antimalarials and/or antibiotics. Such over-prescription can lead to drug resistance and involves unnecessary costs to the health system. International funding for malaria is currently not sufficient to control malaria. Transition to domestic funding is challenged by UHC efforts and recent COVID-19 outbreak. Herewith we present a digital approach to improve efficiencies in diagnosis and treatment of malaria in endemic Kisumu, Kenya: Connected Diagnostics. The objective of this study is to evaluate the feasibility, user experience and clinical performance of this approach in Kisumu.

Methods

Our intervention was performed Oct 2017–Dec 2018 across five private providers in Kisumu. Patients were enrolled on M-TIBA platform, diagnostic test results digitized, and only positive patients were digitally entitled to malaria treatment. Data on socio-demographics, healthcare transactions and medical outcomes were analysed using standard descriptive quantitative statistics. Provider perspectives were gathered by 19 semi-structured interviews.

Results

In total 11,689 febrile patients were digitally tested through five private providers. Malaria positivity ranged from 7.4 to 30.2% between providers, significantly more amongst the poor (p < 0.05). Prescription of antimalarials was substantially aberrant from National Guidelines, with 28% over-prescription (4.6–63.3% per provider) and prescription of branded versus generic antimalarials differing amongst facilities and correlating with the socioeconomic status of clients. Challenges were encountered transitioning from microscopy to RDT.

Conclusion

We provide full proof-of-concept of innovative Connected Diagnostics to use digitized malaria diagnostics to earmark digital entitlements for correct malaria treatment of patients. This approach has large cost-saving and quality improvement potential.

Supplementary Information

The online version contains supplementary material available at 10.1186/s12911-021-01600-z.

Modelling geographical accessibility to urban centres in Kenya in 2019

BACKGROUND

Access to major services, often located in urban centres, is key to the realisation of numerous Sustainable Development Goals (SDGs). In Kenya, there are no up-to-date and localised estimates of spatial access to urban centres. We estimate the travel time to urban centres and identify marginalised populations for prioritisation and targeting.

METHODS

Urban centres were mapped from the 2019 Kenya population census and combined with spatial databases of road networks, elevation, land use and travel barriers within a cost-friction algorithm to compute travel time. Seven travel scenarios were considered: i) walking only (least optimistic), ii) bicycle only, iii) motorcycle only, iv) vehicle only (most optimistic), v) walking followed by motorcycle transport, vi) walking followed by vehicle transport, and vii) walking followed by motorcycle and then vehicle transport (most pragmatic). Mean travel time, and proportion of the population within 1-hour and 2-hours of the urban centres were summarized at sub-national units (counties) used for devolved planning. Inequities were explored and correlations between the proportion of the population within 1-hour of an urban centre and ten SDG indicators were computed.

RESULTS

A total of 307 urban centres were digitised. Nationally, the mean travel time was 4.5-hours for the walking-only scenario, 1.0-hours for the vehicle only (most optimistic) scenario and 1.5-hours for the walking-motorcycle-vehicle (most pragmatic) scenario. Forty-five per cent (21.3 million people) and 87% (41.6 million people) of Kenya's population resided within 1-hour of the nearest urban centre for the least optimistic and most pragmatic scenarios respectively. Over 3.2 million people were considered marginalised or living outside the 2-hour threshold in the pragmatic scenario, 16.0 million Kenyans for walking only, and 2.2 million for the most optimistic scenario. County-level spatial access was highly heterogeneous ranging between 8%-100% and 32%-100% of people within the 1-hour threshold for the least and most optimistic scenarios, respectively. Counties in northern and eastern parts of Kenya were generally most marginalised. The correlation coefficients for nine SDG indicators ranged between 0.45 to 0.78 and were statistically significant.

CONCLUSION

Travel time to urban centres in Kenya is heterogeneous. Therefore, marginalised populations should be prioritised during resource allocation and policies should be formulated to enhance equitable access to public services and opportunities in urban areas.

Methods of Measuring Spatial Accessibility to Health Care in Uganda

Ensuring everyone has access to health care regardless of demographic, geographic and social economic status is a key component of universal health coverage. In sub-Saharan Africa, where populations are often sparsely distributed and services scarcely available, reducing distances or travel time to facilities is key in ensuring access to health care. This chapter traces the key concepts in measuring spatial accessibility by reviewing six methods—Provider-to-population ratio, Euclidean distance, gravity models, kernel density, network analysis and cost distance analysis—that can be used to model spatial accessibility. The advantages and disadvantages of using each of these models are also laid out, with the aim of choosing a model that can be used to capture spatial access. Using an example from Uganda, a cost distance analysis is used to model travel time to the nearest primary health care facility. The model adjusts for differences in land use, weather patterns and elevation while also excluding barriers such as water bodies and protected areas in the analysis. Results show that the proportion of population within 1-h travel times for the 13 regions in the country varies from 64.6% to 96.7% in the dry period and from 61.1% to 96.3% in the wet period. The model proposed can thus be used to highlight disparities in spatial accessibility, but as we demonstrate, care needs to be taken in accurate assembly of data and interpreting results in the context of the limitations.

Spatial access inequities and childhood immunisation uptake in Kenya

BACKGROUND

Poor access to immunisation services remains a major barrier to achieving equity and expanding vaccination coverage in many sub-Saharan African countries. In Kenya, the extent to which spatial access affects immunisation coverage is not well understood. The aim of this study was to quantify spatial accessibility to immunising health facilities and determine its influence on immunisation uptake in Kenya while controlling for potential confounders.

METHODS

Spatial databases of immunising facilities, road network, land use and elevation were used within a cost friction algorithim to estimate the travel time to immunising health facilities. Two travel scenarios were evaluated; (1) Walking only and (2) Optimistic scenario combining walking and motorized transport. Mean travel time to health facilities and proportions of the total population living within 1-h to the nearest immunising health facility were computed. Data from a nationally representative cross-sectional survey (KDHS 2014), was used to estimate the effect of mean travel time at survey cluster units for both fully immunised status and third dose of diphtheria-tetanus-pertussis (DPT3) vaccine using multi-level logistic regression models.

RESULTS

Nationally, the mean travel time to immunising health facilities was 63 and 40 min using the walking and the optimistic travel scenarios respectively. Seventy five percent of the total population were within one-hour of walking to an immunising health facility while 93% were within one-hour considering the optimistic scenario. There were substantial variations across the country with 62%(29/47) and 34%(16/47) of the counties with < 90% of the population within one-hour from an immunising health facility using scenarios 1 and 2 respectively. Travel times > 1-h were significantly associated with low immunisation coverage in the univariate analysis for both fully immunised status and DPT3 vaccine. Children living more than 2-h were significantly less likely to be fully immunised [AOR:0.56(0.33-0.94) and receive DPT3 [AOR:0.51(0.21-0.92) after controlling for household wealth, mother's highest education level, parity and urban/rural residence.

CONCLUSION

Travel time to immunising health facilities is a barrier to uptake of childhood vaccines in regions with suboptimal accessibility (> 2-h). Strategies that address access barriers in the hardest to reach communities are needed to enhance equitable access to immunisation services in Kenya.

The impact of accessibility and service quality on the frequency of patient visits to the primary diabetes care provider: results from a cross-sectional survey performed in six European countries

Background

Visits to the primary diabetes care provider play a central role in diabetes care. Therefore, patients should attend their primary diabetes care providers whenever a visit is necessary. Parameters that might affect whether this condition is fulfilled include accessibility (in terms of travel distance and travel time to the practice), as well as aspects of service quality (for example in-practice waiting time and quality of the provider’s communication with the patient). The relationships of these variables with the frequency of visits to the primary diabetes care provider are investigated.

Methods

The investigation is performed with questionnaire data of 1086 type 2 diabetes patients from study regions in England (213), Finland (135), Germany (218), Greece (153), the Netherlands (296) and Spain (71). Data were collected between October 2011 and March 2012. Data were analysed using log-linear Poisson regression models with self-reported numbers of visits in a year to the primary diabetes care provider as the criterion variable. Predictor variables of the core model were: country; gender; age; education; stage of diabetes; heart problems; previous stroke; problems with lower extremities; problems with sight; kidney problems; travel distance and travel time; in-practice waiting time; and quality of communication. To test region-specific characteristics, the interaction between the latter four predictor variables and study region was also investigated.

Results

When study regions are merged, travel distance and in-practice waiting time have a negative effect, travel time no effect and quality of communication a positive effect on visit frequency (with the latter effect being by far largest). When region specific effects are considered, there are strong interaction effects shown for travel distance, in-practice waiting time and quality of communication. For travel distance, as well as for in-practice waiting time, there are region-specific effects in opposite directions. For quality of communication, there are only differences in the strength with which visit frequency increases with this variable.

Conclusions

The impact of quality of communication on visit frequency is the largest and is stable across all study regions. Hence, increasing quality of communication seems to be the best approach for increasing visit frequency.

Gaps between Knowledge and Malaria Treatment Practices after Intensive Anti-Malaria Campaigns in Western Kenya: 2004-2016

Effective case management is central for malaria control, but not all of those affected by malaria have access to prompt, effective treatment. In Kenya, free malaria treatment has been implemented since 2006. However, questions remain regarding effective treatment. We conducted cross-sectional epidemiological and questionnaire surveys in four counties in western Kenya in 2004, 2010, and 2016, and antimalarial availability surveys in 2016. We found a significant decline in self-reported malaria cases and an improvement in knowledge of malaria prevention and treatment since 2004. Parasite prevalence declined significantly from 2004 to 2010; however, it has remained unchanged since then. Artemisinin-based combination therapies (ACTs) and sulfadoxine-pyrimethamine (SP) drugs were widely available everywhere. The proportion of ACT usage increased from none in 2004 to 48% and 69%, respectively, in 2010 and 2016, whereas SP drug usage declined from 88% in 2004 to 39% in 2010 and 27% in 2016. During the 2016 survey, non-intermittent preventive treatment in pregnancy use of SP was common (20.9% of all surveyed individual treatments). In 2004, 27.2% (168/617) of households sought hospital treatment alone, and this number increased to 50.6% in 2016. The key factors affecting treatment-seeking behavior were education level, wealth index, household size, and distance to hospitals. Our results indicated that gaps in malaria case management remain and out-of-policy treatment is still a concern.

Adaptive interventions for optimizing malaria control: an implementation study protocol for a block-cluster randomized, sequential multiple assignment trial

BACKGROUND

In the past two decades, the massive scale-up of long-lasting insecticidal nets (LLINs) and indoor residual spraying (IRS) has led to significant reductions in malaria mortality and morbidity. Nonetheless, the malaria burden remains high, and a dozen countries in Africa show a trend of increasing malaria incidence over the past several years. This underscores the need to improve the effectiveness of interventions by optimizing first-line intervention tools and integrating newly approved products into control programs. Because transmission settings and vector ecologies vary from place to place, malaria interventions should be adapted and readapted over time in response to evolving malaria risks. An adaptive approach based on local malaria epidemiology and vector ecology may lead to significant reductions in malaria incidence and transmission risk.

METHODS/DESIGN

This study will use a longitudinal block-cluster sequential multiple assignment randomized trial (SMART) design with longitudinal outcome measures for a period of 3 years to develop an adaptive intervention for malaria control in western Kenya, the first adaptive trial for malaria control. The primary outcome is clinical malaria incidence rate. This will be a two-stage trial with 36 clusters for the initial trial. At the beginning of stage 1, all clusters will be randomized with equal probability to either LLIN, piperonyl butoxide-treated LLIN (PBO Nets), or LLIN + IRS by block randomization based on their respective malaria risks. Intervention effectiveness will be evaluated with 12 months of follow-up monitoring. At the end of the 12-month follow-up, clusters will be assessed for "response" versus "non-response" to PBO Nets or LLIN + IRS based on the change in clinical malaria incidence rate and a pre-defined threshold value of cost-effectiveness set by the Ministry of Health. At the beginning of stage 2, if an intervention was effective in stage 1, then the intervention will be continued. Non-responders to stage 1 PBO Net treatment will be randomized equally to either PBO Nets + LSM (larval source management) or an intervention determined by an enhanced reinforcement learning method. Similarly, non-responders to stage 1 LLIN + IRS treatment will be randomized equally to either LLIN + IRS + LSM or PBO Nets + IRS. There will be an 18-month evaluation follow-up period for stage 2 interventions. We will monitor indoor and outdoor vector abundance using light traps. Clinical malaria will be monitored through active case surveillance. Cost-effectiveness of the interventions will be assessed using Q-learning.

DISCUSSION

This novel adaptive intervention strategy will optimize existing malaria vector control tools while allowing for the integration of new control products and approaches in the future to find the most cost-effective malaria control strategies in different settings. Given the urgent global need for optimization of malaria control tools, this study can have far-reaching implications for malaria control and elimination.

TRIAL REGISTRATION

US National Institutes of Health, study ID NCT04182126 . Registered on 26 November 2019.

Routine data for malaria morbidity estimation in Africa: challenges and prospects

BACKGROUND

The burden of malaria in sub-Saharan Africa remains challenging to measure relying on epidemiological modelling to evaluate the impact of investments and providing an in-depth analysis of progress and trends in malaria response globally. In malaria-endemic countries of Africa, there is increasing use of routine surveillance data to define national strategic targets, estimate malaria case burdens and measure control progress to identify financing priorities. Existing research focuses mainly on the strengths of these data with less emphasis on existing challenges and opportunities presented.

CONCLUSION

Here we define the current imperfections common to routine malaria morbidity data at national levels and offer prospects into their future use to reflect changing disease burdens.

Caregiver responses and association with delayed care-seeking in children with uncomplicated and severe malaria

Background

Gaps remain in understanding the role of caregiver responses on time to seek appropriate care. The objective of this study was to describe caregiver responses to illness and the impact of these responses on time to seek appropriate care among children with malaria.

Methods

A case–control study of 325 children with severe (cases) and 325 children with uncomplicated (controls) malaria was conducted in Jinja, Uganda. Caregivers’ responses to their children’s illnesses and time to seek appropriate care were documented. Responses included staying at home, seeking care at drug shops, and seeking care at public health facilities classified into two types: (1) health facilities where caregiver initially sought care before enrollment, and (2) health facilities where children were provided appropriate care and enrolled in the study. Weighted Cox regression was used to determine risk factors for delays in time to seek appropriate care within 24 h of illness onset.

Results

Children staying home on self-medication was the most common initial response to illness among caregivers of controls (57.5%) and cases (42.4%, p < 0.001), followed by staying at home without medication (25.2%) and seeking care at drug shops (32.0%) for caregivers of controls and cases, respectively. Seeking care at drug shops was more common among caregivers of cases than of controls (32.0% vs. 12.3%; p < 0.001). However, compared to public health facilities, drug shops offered sub-optimal services with children less likely to have been examined (50.0% vs. 82.9%; p < 0.001) or referred to another facility (12.5% vs. 61.4%; p < 0.001). Upon adjustment for known risk factors for delay, initially seeking care at a drug shop (HR 0.37, p = 0.036) was associated with delay in seeking care at a health facility where appropriate care was provided. In contrast, those initially seeking care at public health facility before enrollment were more likely to subsequently seek care at another public health facility where appropriate care was provided (HR 5.55, p < 0.001).

Conclusion

Caregivers should be educated on the importance of promptly seeking care at a health facility where appropriate care can be provided. The role of drug shops in providing appropriate care to children with malaria needs to be reviewed.

Electronic supplementary material

The online version of this article (10.1186/s12936-018-2630-9) contains supplementary material, which is available to authorized users.

National and sub-national variation in patterns of febrile case management in sub-Saharan Africa

Given national healthcare coverage gaps, understanding treatment-seeking behaviour for fever is crucial for the management of childhood illness and to reduce deaths. Here, we conduct a modelling study triangulating household survey data for fever in children under the age of five years with georeferenced public health facility databases (n = 86,442 facilities) in 29 countries across sub-Saharan Africa, to estimate the probability of seeking treatment for fever at public facilities. A Bayesian item response theory framework is used to estimate this probability based on reported fever episodes, treatment choice, residence, and estimated travel-time to the nearest public-sector health facility. Findings show inter- and intra-country variation, with the likelihood of seeking treatment for fever less than 50% in 16 countries. Results highlight the need to invest in public healthcare and related databases. The variation in public sector use illustrates the need to include such modelling in future infectious disease burden estimation.

Impacts of Antimalarial Drugs on Plasmodium falciparum Drug Resistance Markers, Western Kenya, 2003-2015

Antimalarial drug resistance has threatened global malaria control since chloroquine (CQ)-resistant Plasmodium falciparum emerged in Asia in the 1950s. Understanding the impacts of changing antimalarial drug policy on resistance is critical for resistance management. Plasmodium falciparum isolates were collected from 2003 to 2015 in western Kenya and analyzed for genetic markers associated with resistance to CQ (Pfcrt), sulfadoxine-pyrimethamine (SP) (Pfdhfr/Pfdhps), and artemether-lumefantrine (AL) (PfKelch13/Pfmdr1) antimalarials. In addition, household antimalarial drug use surveys were administered. Pfcrt 76T prevalence decreased from 76% to 6% from 2003 to 2015. Pfdhfr/Pfdhps quintuple mutants decreased from 70% in 2003 to 14% in 2008, but increased to near fixation by 2015. SP "super resistant" alleles Pfdhps 581G and 613S/T were not detected in the 2015 samples that were assessed. The Pfmdr1 N86-184F-D1246 haplotype associated with decreased lumefantrine susceptibility increased significantly from 4% in 2005 to 51% in 2015. No PfKelch13 mutations that have been previously associated with artemisinin resistance were detected in the study populations. The increase in Pfdhfr/Pfdhps quintuple mutants that associates with SP resistance may have resulted from the increased usage of SP for intermittent preventative therapy in pregnancy (IPTp) and for malaria treatment in the community. Prevalent Pfdhfr/Pfdhps mutations call for careful monitoring of SP resistance and effectiveness of the current IPTp program in Kenya. In addition, the commonly occurring Pfmdr1 N86-184F-D1246 haplotype associated with increased lumefantrine tolerance calls for surveillance of AL efficacy in Kenya, as well as consideration for a rotating artemisinin-combination therapy regimen.

Spatial models for the rational allocation of routinely distributed bed nets to public health facilities in Western Kenya

BACKGROUND

In high to moderate malaria transmission areas of Kenya, long-lasting insecticidal nets (LLINs) are provided free of charge to pregnant women and infants during routine antenatal care (ANC) and immunization respectively. Quantities of LLINs distributed to clinics are quantified based on a combination of monthly consumption data and population size of target counties. However, this approach has been shown to lead to stock-outs in targeted clinics. In this study, a novel LLINs need quantification approach for clinics in the routine distribution system was developed. The estimated need was then compared to the actual allocation to identify potential areas of LLIN over- or under-allocation in the high malaria transmission areas of Western Kenya.

METHODS

A geocoded database of public health facilities was developed and linked to monthly LLIN allocation. A network analysis approach was implemented using the location of all public clinics and topographic layers to model travel time. Estimated travel time, socio-economic and ANC attendance data were used to model clinic catchment areas and the probability of ANC service use within these catchments. These were used to define the number of catchment population who were likely to use these clinics for the year 2015 equivalent to LLIN need. Actual LLIN allocation was compared with the estimated need. Clinics were then classified based on whether allocation matched with the need, and if not, whether they were over or under-allocated.

RESULTS

888 (70%) public health facilities were allocated 591,880 LLINs in 2015. Approximately 682,377 (93%) pregnant women and infants were likely to have attended an LLIN clinic. 36% of the clinics had more LLIN than was needed (over-allocated) while 43% had received less (under-allocated). Increasing efficiency of allocation by diverting over supply of LLIN to clinics with less stock and fully covering 43 clinics that did not receive nets in 2015 would allow for complete matching of need with distribution.

CONCLUSION

The proposed spatial modelling framework presents a rationale for equitable allocation of routine LLINs and could be used for quantification of other maternal and child health commodities applicable in different settings. Western Kenya region received adequate LLINs for routine distribution in line with government of Kenya targets, however, the model shows important inefficiencies in the allocation of the LLINs at clinic level.