Contemporary epidemiological overview of malaria in Madagascar: operational utility of reported routine case data for malaria control planning

The Malagasy context is one of protracted political instability, most recently in the aftermath of a political coup in 2009 which continues to bear severe economic and social impacts [1] (Fig. 1). A long-term lack of investment in the country’s infrastructure has had direct repercussions for malaria control efforts as well as the country’s broader developmental situation [2]. Madagascar has the seventh lowest per capita gross domestic product (GDP) globally [3] with an estimated 75.3 % of the population living under the national poverty line in 2010 [3]. This was estimated to have increased by more than 10 % since the 2009 crisis, with the World Bank estimating 92 % of the population to be living under $2 a day in 2013 [1]. The population lives predominantly in rural environments (66 %), and almost half (42.4 %) is younger than 15 years, and the median age is 18.4 years [3]. Malnutrition is rife, affecting over half of children, with 25 % of children reported to be severely malnourished [4], exacerbated by periodic famine outbreaks following severe drought [5] and failed harvests from locust infestation [1]. Childhood underweight is reported to be the primary overall risk factor of disease in the Malagasy population [6], increasing vulnerability to infections and development of severe sequelae. In 2012, top causes of consultations at health centres among under 5 year olds were, in descending order, acute respiratory infections, diarrhoea, digestive disorders, and uncomplicated malaria [7]. In the five to 14 years age bracket, uncomplicated malaria represented the second-most common cause of health centre consultations in 2012, while complicated malaria presented the top cause of district-level hospital mortality, at 10.1 % overall, and 21.6 % among under-fives [7]. All 23.9 million people across Madagascar’s 112 health districts are at risk of malaria exposure [8].

In view of this substantial disease burden, malaria represents a main priority for the Malagasy Ministry of Health (MoH) and associated international public health-focussed partners [9]. Intervention coverage is high, with some of the highest ownership rates of long-lasting insecticide-treated nets (LLINs) in the African region in 2013 [8, 10]. This was supplemented at the end of 2015 with the distribution of 12 million LLINs aiming at universal coverage of one net per two individuals across 92 of the country’s 112 health districts where seasonal or perennial transmission is considered to occur. In the lower transmission districts of the highlands, focalized indoor residual spraying of insecticide (IRS) has been deployed against the Anopheles vectors [11]. Nevertheless, increasing numbers of cases have been reported since 2010. This paper analyses the routine reported malaria rapid diagnostic test (RDT) data from 2010 to 2015 to evaluate trends of malaria transmission across Madagascar. This analysis provides a benchmark assessment of malaria endemicity ahead of the anticipated impact of the large-scale LLIN distribution campaign prior to the 2016 transmission season. December 2015 also represented the end of the Millennium Development Goals era (2000–2015) and Fig. 1 summarizes the malaria control activities that have contributed to the malaria-specific aspects of this initiative. This overview therefore comes at an opportune time to assess progress against the target of “reversing the incidence of malaria” in Madagascar [12, 13].

Transmission of malaria is heterogeneous across Madagascar [14]. The island, the fourth largest globally at over 1500 km in length and with a surface area of 587,000 sq km, similar to California or Sweden, is highly ecologically and climatically diverse [15, 16]. Reflecting these conditions, the epidemiology of malaria across the island shows distinct seasonal trends and transmission intensities between regions [17, 18] with different vector species and behaviours predominant in different areas [19, 20]. Madagascar has a stated aim to reduce the proportion of malaria-attributable fevers and mortality, and start transitioning towards pre-elimination status by end of 2017, the end of the country’s current National Strategic Plan [21] (Fig. 1). The development of spatially specific interventions informed by local transmission characteristics is therefore required to optimize the efficacy of strategies to combat malaria.

Mapping the intensity of malaria transmission across the island has been done since the start of control efforts. Joncour et al. published district-level endemicity estimates according to splenic rates in the 1950s, identifying broad categories of malaria transmission [18]. These were updated by Mouchet et al. in the early 1990s, stratifying the country according to climatic and transmission patterns (Fig. 2a [22, 23]). These mapping sub-divisions have subsequently adapted to changing needs, aligning with contemporary administrative boundaries (Fig. 2b [21, 24, 25]), and recently been further simplified from Mouchet’s five zones to two broad zones representing high transmission (east and west coasts) and low transmission (highlands and arid south) by control programme agencies [11, 21, 25]. Here, the boundaries of these sub-national divisions are re-evaluated and used to describe the contemporary epidemiology of malaria in Madagascar more than a decade after the country started receiving substantial external funding to roll-out its control programme (Fig. 1).

Two methodological steps were followed to address the paper’s goal of assessing Malagasy routine health facility data and its application to programmatic planning. First, regional sub-divisions were reviewed and updated in accordance with contemporary transmission patterns. Second, routine health reports were examined within the updated sub-national stratifications to identify each area’s key epidemiological characteristics which might impact on intervention planning. The data sources accessed in this study are summarized in the Additional file 1.

An updated sub-national stratification of Madagascar is proposed which reflects contemporary transmission intensity. Within these limits, the epidemiology of malaria is described as evidenced by routinely reported health facility data made available to the NMCP. From this evidence base, the National Programme formulates control policies, oversees epidemiological monitoring, and assesses policy impacts. The eight ecozones may not necessarily each require bespoke control schemes, but these allow greater sub-national epidemiological insight, and therefore flexibility in targeting of interventions based on local needs. These sub-divisions are not static, but instead must be periodically updated to reflect shifts in the current patterns of transmission, though with a frequency that does not prevent temporal trends being assessed in the short term. The narrow time window presented here is the product of decades of malaria control, so the epidemiology described cannot be interpreted as representing the fundamental niche of malaria in Madagascar.

Malaria is a disease of increasing public health concern in Madagascar. Since 2010, the number of reported RDT-confirmed cases has quadrupled across the country. When adjusted for numbers of tests performed (thus adjusting for (i) reporting rates, (ii) the unavailability of diagnostics, and (iii) the scale-up of access to diagnostic testing through community health worker programmes [42]), the RDT positivity rate increased by 50 % between 2010 and 2015, reaching 50 % slide positivity in 2015. Despite this evidence of recent resurgences, the incidence of malaria in Madagascar remains among the lowest of the sub-Saharan African region, with high levels of intervention coverage, particularly of LLINs [8, 14]. This juncture therefore represents an important opportunity to re-evaluate strategies and reverse the recent negative trends. For instance, while LLIN coverage may be high, treatment-seeking and adherence to recommended artemisinin combination therapy (ACT) policies appears weak [8, 43].

Although the majority of the malaria burden remains in the Southeast (42 % in 2015), malaria has increased across all areas in year-round significance, increasing in intensity during both high (December–April) and low transmission periods. The Central highlands seem to be the exception, but the epidemiology differed there, consisting primarily of imported cases (NMCP, pers. comm.), and with very low incidence compared to other ecozones. Outbreaks, defined in this context as unexpectedly high or rapid increases in transmission or evidence of autochthonous transmission in areas considered at low risk, appear to associate closely with overall burden trends in lower-transmission ecozones. A more detailed view of changes in case numbers at health facilities affected by outbreaks would allow more robust insights into the relative contribution of outbreak-driven transmission to overall caseloads across the country.

An important missing piece from this study’s narrative is the relative contribution of each Plasmodium species to the regional case burden. This detail is not recorded in the data. Prevalence surveys indicate a predominance of P. falciparum across most areas, but regional pockets of Plasmodium vivax and sporadic cases of Plasmodium ovale and Plasmodium malariae are also detected [24, 25, 29, 44, 45]. A longitudinal dataset on the contribution of each species to morbidity from routine reporting would improve the evidence-base for determining the value of introducing non-P. falciparum-specific intervention policies.

The recent trends described in this study stand in stark contrast to the country’s stated ambitions to start the transition towards malaria elimination. The country’s current National Strategic Plan (2013–2017) aims for pre-elimination status by end 2017, with zero malaria deaths and the reduction of test positivity rates to <5 % in 15 % of districts, and the halving of the positivity rate in the remaining districts relative to 2013 baseline data [21, 39]. Overall, however, 2013–2015 saw a 34 % increase in the positivity rate. Acknowledgement of these trends by the NMCP led to a revision of the National Strategic Plan in 2015, with adjustment of their initial goals now more aligned with achieving burden control than elimination [21]. The drive to control and eventually eliminate malaria from Madagascar receives strong financial backing from the international community, with disbursements in excess of $400 million since 2004 when collaborative efforts were galvanized and intensified [8, 11, 46]. In 2006–2010, resources for malaria control in Madagascar corresponded to higher than average per-capita funding across the African region [8, 47]. Since this time though, the country’s political crisis has severely impacted on the country’s socio-economic status and associated health system infrastructure and staffing, impeded external funding to the country thus preventing intervention roll-out (see the Global Fund disbursement delays in Fig. 1), and complicated the control programme management by preventing direct bilateral relationships between the MoH and donors [21].

The situation in Madagascar therefore reflects what has been widely reported from other areas, that withdrawal of core funding is closely associated with rapid and predictable resurgence of malaria [48, 49] (Fig. 1). Despite high coverage rates of interventions around 2013, interrupted funding led to delays in sustaining these levels. Periods of limited funding are when reliable epidemiological data become all the more important to inform efficient planning and optimize resource allocation. Such data require robust processing at all stages of the chain from the probability of patients seeking treatment at a health facility, through diagnosis, through data reporting to data management and evaluation at the central level. More than half the Malagasy population live further than 5 km (roughly corresponding to an hour’s walk) from their nearest health facility. This likely contributes substantially to the treatment-seeking rate that ranged in 2013 from 21.7 to 49.1 % across different parts of the country [24, 43, 50, 51], with many patients suffering fevers resorting to self-medication instead (including both pharmaceutical and traditional herbal remedies) [52]. Therefore, most cases of fever will never interact with the country’s formal health system. The routine HMIS had a largely consistent reporting rate from health facilities across 2010–2014 at approximately 60 %, though rising to 75 % in 2015 (Fig. 4a), meaning that almost half of health centre reports were not recorded in the NMCP’s databases for most of the time period reviewed. Further reinforcement of the incompleteness of the datasets is the disparity between distributed diagnostic kits and the test results reported (Fig. 4b). The received outbreak surveillance data are also incomplete, evidenced by nearly half of reports not being month-specific (Fig. 7).

Madagascar has a well-documented history of devastating epidemics, with the earliest reported outbreak around 1887 coinciding with the establishment of rice farming [21]. Outbreaks both in Madagascar and globally have primarily occurred in the context of socio-economic and political upheavals [48] when there has been a breakdown of the control programme structure, including surveillance and reporting channels. Implicit in the programmatic concept of an outbreak is that the event triggers an emergency response from the responsible public health institution to curtail the exacerbation of the situation. An efficient surveillance system will allow outbreaks to be anticipated and intervention resources to be mobilized ahead of transmission developing out of control.

The reporting system in Madagascar was described in 2015 by the US President’s Malaria Initiative as “extremely weak”, hindered in part by a critical shortage of health staff at all levels, with human resources unevenly distributed across the country [11]. To further investigate this, a nationally representative review of the health reporting system was commissioned in 2015 to qualitatively and quantitatively assess the system’s functionality (thus supplanting the need for proxy indictors of completeness). This included detailed interviews with staff at all levels of the reporting pyramid, alongside comparisons of raw data to assess the completeness and accuracy of reporting from doctors’ records up to the central database. Preliminary results suggest that both the quantity and quality of reported health facility data were weak. Reporting timeliness was particularly affected, with only 8 of 45 districts reviewed in January 2015 reporting within the MoH’s defined schedule [31]. Reporting completeness varied across the country, being highest in the central highlands, and lowest in the tropical coastal areas and desert south. A further concern identified by the surveys related to the lack of health professionals to make use of the data and identify trends specific to their local catchment population: interpretation of the data was principally limited to the central level, and not performed in situ. The investigators’ perspectives of the NMCP’s data in particular, was that the existence of a database was a strength of their epidemiological monitoring unit. Weaknesses of the data included the complexity of the reporting system, its fragmented nature and the difficulty of assimilating the data for analysis, logic checks are not routinely performed on the data, and confusion existed regarding some of the calculations used to derive the metrics reported [31]. Surveys actively assessing stocks of diagnostic kits in health centres suggest that the passively reported data on stock-out events that were available for review here (Fig. 4c) underestimate the true incidence of stock-out events [49]. This indicates that the true rate of under-diagnosis due to diagnostic stock-outs and knock-on impact on reported case numbers may be even greater than suggested. Stakeholders are aware of these limitations to the reporting system, and a concerted effort to address them is ongoing.

Given the need for annual case estimates to assess both national- and global-level trends in malaria incidence and to monitor progress towards pre-defined milestones, the World Health Organization’s (WHO) Global Malaria Programme and collaborators have developed methods to account for weaknesses in routine HMIS data [8, 14, 53, 54]. Two main approaches exist to estimate malaria incidence [53], each best suited to different countries according to their programme control phase and the strength of their disease surveillance system. Method 1 is favoured by the WHO for countries outside Africa, where routine reporting is considered more reliable. This method uses parameters to adjust MoH routine HMIS data to account for (i) incomplete case detection by health facilities; (ii) the likelihood of over-diagnosis of malaria among patients with fevers; and, (iii) public/private treatment-seeking preferences [53, 55]. Method 2 uses a cartographic approach based on population prevalence surveys to develop spatial and temporal models of the incidence of clinical disease; this has been the main approach followed by MAP to derive global endemicity maps of P. falciparum and P. vivax transmission [14, 56, 57], and is used by the WHO to estimate case numbers from most sub-Saharan African countries where routine reporting is considered too weak for Method 1 estimation. A subset of low-transmission African countries is also considered to have sufficiently strong routine reporting data to allow support for Method 1-based estimates. The implication being that these countries have a sufficiently functional health system to have robust surveillance and reporting systems in place within the context of a functional and effective malaria control programme. The sub-Saharan African countries included in this category are Botswana, Cabo Verde, Eritrea, Namibia, South Africa, Swaziland, and Zimbabwe (all of which are classified as being “on track for >75 % decrease in incidence 2000–2015”), and Madagascar (“less than 50 % change in incidence projected 2000–2015”) [8]. Nevertheless, the WHO lists Madagascar among the eight sub-Saharan African low-transmission countries with sufficiently accurate HMIS data to permit convincing estimates to be made from simple adjustment of the numbers of reported cases. This study explores the routine HMIS data available to the Malagasy NMCP which underpins this perhaps contradictory classification. Of the subset of African Method 1 countries, Madagascar had the highest endemicity in 2015 [8, 14] and among the lowest access to the formal health system [43]. The magnitude of the uncertainty in the reported case numbers is evident by the WHO’s case estimate for Madagascar in 2013. Although 387,045 cases were reported by Madagascar to the World Malaria Report for 2013, once adjusted using Method 1, the estimated case number for 2013 was 1.2 million (0.75–2.10 million) [8], therefore corresponding to more than three times the reported RDT-confirmed numbers. Given these acknowledged uncertainties and the recent resurgence of transmission with variable sub-national patterns, a re-consideration of the method applied to case estimation in Madagascar would be worthwhile. Regular nationally representative Malaria Indicator Surveys and other prevalence surveys take place, which provide a rich source of malariometric data complimentary to the HMIS data.

If Madagascar is to remain within the Method 1 countries and have its case estimates derived from adjustment of the routine reporting data, the spatial heterogeneity of transmission characteristics and of reporting robustness discussed in this paper would be a valuable addition to the methodology instead of relying on a single set of parameters. The data adjustment parameters show widespread variation between ecozones, with treatment-seeking rates in the Fringe areas less than half that of the South (21.7 and 49.1 %, respectively) [24]. Similarly, health facility reporting rates differ between areas, and, although no correlation could be identified from the aggregated district-level data reviewed here, it is likely that some association between health centre isolation and reporting quality and timeliness exists. No single set of national-level summary adjustment parameters, as per the current WHO Method 1 protocol, could capture this heterogeneity. The sub-national variation in transmission intensity is reflected by factors such as age-specific incidence patterns (Fig. 3b). The Central highlands ecozone, where transmission is lowest, has no significant age-specific patterns with the whole population at similar risk of infection. In higher endemic areas, younger children suffer the highest burden of disease, likely due to the development of immunity in older age groups from repeated exposure in childhood. To adequately adjust health facility data from the HMIS, this spatial variability ought to be taken into account. In parallel to this, large numbers of prevalence surveys have been performed across the country as part of three completed MIS surveys from 2011, 2013, and April 2016 (results expected end 2016), among others [29]. These community-based surveys have shown the same increase in prevalence as the HMIS health-facility based case data. While these prevalence surveys are generally better standardized than HMIS data, they are much less comprehensive in space and time. Comparison of the estimates from these two independent methods (routine data-based and cartographic) and datasets would be valuable.

Madagascar is highly diverse, not least in terms of its malarial epidemiology, and accordingly requires spatially specific control planning. This paper presents an overview of the last 5 years of HMIS data, the period immediately following a political crisis and the nation’s subsequent exclusion from the international community until May 2014. Associated with these events, malaria control efforts have been hindered, resulting in rapid resurgences of transmission [49], with ecozones previously considered to be low transmission zones, such as the Southwest and South, now becoming stable endemic areas, reflected by differential patterns in age-specific incidence, for example.

No single approach for malaria control can be applied across the island, or even binary high/low transmission approach given the peculiarities of transmission in different ecozones. Regular epidemiological assessments are therefore recommended at the ecozone levels. The NMCP is frequently restricted to being reactive rather than proactive and unable to anticipate demands (e.g., in terms of anticipating commodity needs or anticipating outbreaks). Closer analysis of data trends in real-time, would be valuable to support the NMCP. For instance, it will be important to closely evaluate the 2016 data to assess the impact of the 2015–2016 LLIN distribution programme, and determine how rapidly the sharp increase of cases in 2015 can be reversed to shift the country in the direction of its elimination goals.

This study comes at the end of the Millennium Development Goal era (2000–2015), which aimed to have “halted by 2015 and begun to reverse the incidence of malaria” (Target 6.C) [12]. In Madagascar this was clearly being achieved from 2000 to 2009, though the reverse has occurred since 2010 (Figs. 1, 2, 3). Using the year 2000 as a baseline descriptor may not be a very pertinent approach for evaluating the current status of progress in malaria control. The situation in 2000 was one of essentially no financial or political commitment to large-scale intervention roll-out. Most reported cases would have been presumptively diagnosed based on symptoms (likely over-estimating case numbers), given that rapid field-based diagnostics were only introduced in 2007. More pertinent than noting a decrease in caseload since 2000 is the concerning increase in burden since 2010 when funding sources were substantially increased and malaria control was widespread across Madagascar.

While it is important to interpret the imperfect health facility data with caution [42, 58], the available data do provide detailed insight into the relative differences in epidemiology sub-nationally. However, given that between a quarter and half of health facility reports are missing from the NMCP’s database, the absolute number of cases reported is evidently a major underestimate. Approaches to quantify estimates of the absolute annual burden would be useful in attempting to deduce a more robust estimate of overall burden rather than relying on an incomplete database as is currently the case by the NMCP. Nevertheless, the HMIS data present a convincing case for the growing challenge of malaria across all areas of Madagascar.