Effectiveness was reduced by delays and other factors.
Quantitative data on the onset and evolution of malaria epidemics are scarce. We review case studies from recent African Plasmodium falciparum epidemics (Kisii and Gucha Districts, Kenya, 1999; Kayanza Province, Burundi, 2000–2001; Aweil East, southern Sudan, 2003; Gutten and Damot Gale, Ethiopia, 2003–2004). We highlight possible epidemic risk factors and review delays in epidemic detection and response (up to 20 weeks), essentially due to poor case reporting and analysis or low use of public facilities. Epidemics lasted 15–36 weeks, and patients' age profiles suggested departures from classical notions of epidemic malaria everywhere but Burundi. Although emergency interventions were mounted to expand inpatient and outpatient treatment access, we believe their effects were lessened because of delays, insufficient evaluation of disease burden, lack of evidence on how to increase treatment coverage in emergencies, and use of ineffective drugs.
Research on malaria epidemics mostly concerns long-range forecasting, early warning, and early detection (improved understanding of the role of temperature, rainfall, and El Niño–Southern Oscillation events [
Recently, Médecins Sans Frontières (MSF) intervened in several
We reviewed MSF program reports; unpublished assessments (
Four interventions (
| Characteristic/ determinant | Kisii/Gucha, Kenya | Kayanza, Burundi | Aweil East, southern Sudan | Gutten, Ethiopia | Damot Gale, Ethiopia |
|---|---|---|---|---|---|
| Epidemic period (no. weeks) | May–August 1999 (15) | September 2000–May 2001 (36) | June–November 2003 (22) | July 2003–February 2004 (33) | July 2003–January 2004 (30) |
| Population | 956,000 | 578,000 | 307,000 | 44,000 | 287,000 |
| Altitude (m) | 1,200–2,200 | 1,400–1,750 | 430 | 1,700 | 1,600–2,100 |
| Malaria vectors | Anopheles funestus (constant), A. gambiae sensu lato (seasonal) | A. arabiensis (95%), A. funestus (5%) | Not available (A. gambiae sensu lato presumed) | A. arabiensis | A. arabiensis |
| Malaria species (nonepidemic months) | Plasmodium falciparum (>90%) | P. falciparum (>90%) | P. falciparum (>95%) | P. falciparum (≈25%), P. vivax (≈75%) | P. falciparum (≈60%), P. vivax (≈40%) |
| Temperature anomalies | Above average in 3 preepidemic months | None apparent | Maximum LST strongly below average during epidemic | None apparent | None apparent |
| Rainfall anomalies | Heavy rainfall in preepidemic rainy season after drought in previous rainy season | Heavy rainfall 5 and 3 months before epidemic, drought 2 years before epidemic but not in preepidemic year | Below average rainfall in 3 preepidemic years, above average in 2 preepidemic months | Below average rainfall in 2 preepidemic and epidemic years but heavy rainfall in preepidemic month | Below average rainfall in 2 preepidemic and epidemic years but heavy rainfall in 3 preepidemic months |
| Land pattern changes | None reported | Creation of rice paddies and fish ponds | Widespread flooding | Creation of water ponds | None reported |
| Political instability | None | Armed conflict | Tenuous ceasefire | Inactive insurgency | Inactive insurgency |
| Population movement | None | Forced relocation | Seminomadic, returnees from north Sudan | Government resettlement schemes | Government resettlement schemes |
| Global acute malnutrition† | Not available | 10%–15% | 25% | Not available (probably >5%) | 28% |
| Drug resistance (in vivo failure rates) | CQ 24%–87% (neighboring districts), SP 10% ( | CQ 100%, SP 54.2%, CQ+SP 42.0% ( | CQ 63%, SP 3% ( | SP 78.0% ( | SP 68.1% (neighboring zone) ( |
*LST, land surface temperature; CQ, chloroquine; SP, sulfadoxine-pyrimethamine. †Among children <5 y of age; malnutrition rates >15% denote a serious situation; values are provided for 2 months before the epidemic. ‡Percentages refer to the frequency of single Pfcrt mutations and triple Dhfr mutations in the P. falciparum genome of outpatients sampled in Aweil East. These mutations are predictive of in vivo CQ and SP failure rates, respectively.
In Burundi's northern Kayanza Province, a 3-year time series up to September 2000 showed constant monthly caseloads of ≈10,000 outpatients/month. In 2000, MSF operated 7 of the province's 22 outpatient facilities. The September 2000–May 2001 epidemic, the largest ever recorded in Burundi, affected 9 of 16 provinces, and 3.5 million cases were reported (
The Ethiopian highlands experience 2 moderate transmission seasons every year (after rains in March through April and August through September). Epidemics occur in 5- to 8-year cycles; >1 million cases were recorded in 1998 (
Finally, malaria is considered endemic in low-altitude Aweil East County (Bahr el Ghazal state, southern Sudan), although no data are available. Most cases occur from July through January after spring rains.
Findings on possible epidemic determinants are summarized in
No early warnings were issued. In Kisii, the alert came from the media in epidemic week 5 (when the district hospital was overwhelmed with malaria cases). MSF issued alerts in Kayanza (doubling of fever cases in epidemic week 2, early exhaustion of antimalarial stocks), Aweil East (quadrupled inpatient and outpatient malaria after epidemic week 1), and Damot Gale (increased proportion of
Trends in inpatient malaria caseload and positivity among malnourished children admitted to feeding centers in Damot Gale, Ethiopia, 2003–2004. MSF, Médecins Sans Frontières.
Formal epidemic declaration was hampered by missing data. Time series for historical comparisons were available in Kisii (12 years) and Kayanza (3 years), where, however, authorities initially suspected a typhus outbreak, until the 80% seroprevalence detected among febrile patients (epidemic week 7) pointed to
In Ethiopia, a malaria-specific surveillance system aimed for early outbreak detection at both the village (positivity >25% detected among slides collected by field workers in the community was considered an outbreak and theoretically led to village-level mass treatment and vector control) and
Interventions occurred 3–20 weeks late (
| Factor | Kisii/Gucha, Kenya | Kayanza, Burundi | Aweil East, southern Sudan | Gutten, Ethiopia | Damot Gale, Ethiopia | |
|---|---|---|---|---|---|---|
| Delay of intervention (wks) | 7 | 7 | 3 | 20 | ||
| Inpatient care | ||||||
| Expansion in bed capacity | From 310 to 510 beds | From 65 to 125 beds | From ≈80 to ≈120 beds | From 2 to ≈100 beds | From 12 to >100 beds | |
| Treatment | IM/IV quinine, IM artemether | IM/IV quinine | IM artemether | IV quinine | IV/IR quinine | |
| Diagnosis | Presumptive | Blood slide | RDT | RDT | RDT | |
| Fixed outpatient care | ||||||
| Increase in capacity | 2 additional OPDs | Increased capacity in 5 OPDs, 2 additional OPDs | Conversion of nutritional centers, 2 additional OPDs | 1 additional OPD | Supervision and drug supply to 5 OPDs | |
| Treatment | SP | CQ+SP | AS+SP | Quinine (IR if vomited) | SP, quinine | |
| Diagnosis | Presumptive | Presumptive | RDT | RDT | RDT | |
| Mobile clinics | ||||||
| Number | 3 | 6 | 14 | 5 | Not available | |
| Catchment population | 302,000 | Not available | 144,000 | 44,000 | 73,000 | |
| Sites visited | 45 | 10 | 43 | 5 | 14 | |
| Days per site per week (wks of operation) | 0.2–0.3 (7) | 1.2 (22) | 1–2 (15) | 2 (13) | 0.2–0.5 (4) | |
| Treatment | SP, AS+SP (73.4% of cases) | CQ+SP | AS+SP, artemether for severe cases | Quinine | Quinine | |
| Diagnosis | Presumptive | Presumptive | Presumptive | RDT | RDT | |
*IM, intramuscular; IV, intravenous; IR, intrarectal; RDT, rapid diagnostic test; OPD, outpatient department; SP, sulfadoxine-pyrimethamine; CQ, chloroquine; AS, artesunate.
All interventions included inpatient components with blood transfusion. Conversion of existing MSF nutritional structures enabled expansion of care in Aweil East and Damot Gale. To reach isolated communities, mobile clinics, consisting of teams of nurses or nursing assistants working with simple treatment algorithms, were established at each site. However, this intervention occurred late (10 weeks late in Kisii and Gucha, 7 in Kayanza, 8 in Aweil East, 13 in Gutten, and 27 in Damot Gale) and, apart from in Kayanza, after the epidemic peak (
| Characteristic | Kisii/Gucha, Kenya | Kayanza, Burundi | Aweil East, southern Sudan | Gutten, Ethiopia | Damot Gale, Ethiopia | ||
|---|---|---|---|---|---|---|---|
| Uncomplicated cases | |||||||
| Fixed outpatient centers | |||||||
| All ages | 13,127* | 272,459 | 15,239 | 15,928† | – | ||
| Age <5 y (%) | 2,426 (18.5) | Not available | 7,257 (47.6) | 4,758‡ (29.9) | – | ||
| Mobile clinics | |||||||
| All ages | 29,769 | 46,541 | 34,749 | 7,258 | 467 | ||
| Age <5 y (%) | 5,376 (18.1) | Not available | 17,338 (49.9) | 1,405 (19.4) | 145 (31.0) | ||
| Complicated cases | |||||||
| All ages | 9,773§ | 3,953¶ | 875# | 330** | 1,291 | ||
| Age <5 y (%) | 5078 (52.0) | 761 (19.3) | 683 (78.1) | 175 (53.0) | 595 (46.1) | ||
| No. deaths (CFR [%]) | 397 (4.1) | 108 (2.7) | 50 (5.7) | 34 (10.3) | 62 (4.8) | ||
| No. deaths <5 y (CFR [%]) | 164 (3.2) | 31 (4.1) | 39 (5.7) | 15 (8.6) | 38 (6.4) | ||
| Minimal attack rate (%)†† | 22.2 (complicated, <5 only; 12/15 weeks) | 86.5 (36/36 weeks) | 41.2 (<5 only; 22/22 weeks) | 53.4 (15/33 weeks) | Not available | ||
| P. falciparum prevalence at epidemic peak (%) | 38–49 (community survey) | 80 (random sample in OPD queue) | 52–64 (random sample in OPD‡‡ queue) | Not available | 60 (random sample by community workers) | ||
*Includes data from 3 government clinics (Masimba, Kenyenya, and Etago) for which age breakdown was available. †Includes 2,061 patients treated with intrarectal quinine in inpatient department. ‡Includes 1,773 patients <5 years of age treated with intrarectal quinine in inpatient department. §Includes data from Kisii, Keumbu, and Ogembo hospitals, supported by Médecins Sans Frontières and other agencies but operated by the government. ¶Excludes patients treated in the Kayanza government hospital (data not available). #Excludes 110 severe cases treated by mobile clinics (no age breakdown or outcome available). **Includes only hospitalized patients who met a strict definition of severe malaria, which probably explains the considerably higher case-fatality ratio (CFR) noted in Gutten. ††Ratio of weeks refers to the number of epidemic weeks from which the attack rate was calculated divided by the total number of epidemic weeks. ‡‡OPD, outpatient department.
Artemisinin-based combination therapy (ACT) was deployed in Aweil East and in mobile clinics in Kenya (
In Burundi, Sudan, and Ethiopia, surveillance data were analyzed weekly. In Kayanza, RDT testing was carried out every 2–3 weeks among outpatients to monitor epidemic trends. In Aweil East and Gutten, an automated surveillance spreadsheet generated key indicators and graphs (caseload, proportionate morbidity and mortality, case-fatality, RDT confirmation of diagnosis).
The Kisii and Gucha epidemic followed a historical pattern of short dramatic peaks (
Trends in outpatient malaria caseload in Kisii Hospital outpatient department, Kenya, 1995–1999. Data for December 1997 are missing because of a nursing staff strike.
The Kayanza epidemic lasted 36 weeks and roughly followed a normal distribution (
Trends in outpatient malaria caseload in Kayanza Province, Burundi, 1999–2001. MSF, Médecins Sans Frontières.
In Aweil East, a peak was reached by epidemic week 2, and a steady decline followed, which reflected percentage of confirmed malaria cases among women who came to the clinic for antenatal visits (
Trends in outpatient caseload and proportionate malaria among pregnant women attending antenatal consultations in Aweil East, southern Sudan, 2002–2003. MSF, Médecins Sans Frontières.
In Ethiopia, the epidemic's evolution can partly be reconstructed by plotting available microscopy results from the Gutten government clinic, which yields a normal distribution (
Trends in outpatient malaria caseload and slide positivity in Gutten, Ethiopia, 2003–2004. MSF, Médecins Sans Frontières.
Among uncomplicated cases, the proportion of patients <5 years of age exceeded the expected levels of 15% to 20% in southern Sudan and Ethiopia but not Kenya, where only presumptive diagnosis was used (
In sub-Saharan Africa, malaria epidemics arise suddenly in mostly remote, disadvantaged settings without effective alert systems. Our case studies show that large-scale interventions can be organized in such epidemics, and that these interventions can considerably increase diagnostic and treatment output. Both preparedness and control, however, were seriously deficient. Epidemic detection was late everywhere, and additional delays occurred before external intervention to support overwhelmed local health structures.
Experiences in Kisii, Gucha, Kayanza, Gutten, and Damot Gale probably reflect conditions in neighboring regions affected by the corresponding epidemics, although scarcity of published records makes comparisons difficult. This analysis relies on programmatic data, the limitations of which are apparent.
Our analysis did not include controls (i.e., sites where no epidemics occurred). Nevertheless, remotely sensed climate data suggest rainfall abnormalities during key preepidemic periods: relative drought in the 2 or 3 preepidemic years (with the exception of Kayanza) and above-average rainfall 1–2 months before epidemic onset. No consistent temperature pattern emerged.
The full role of such abnormalities as epidemic determinants is unclear. Furthermore, although remotely sensed environmental variables provide relatively robust and accurate estimates (
Land cover changes in Aweil East (flooding) and in Kayanza (rice paddy creation) probably favored vector breeding. Malnutrition, displacement, and drug resistance may not in themselves cause epidemics, but in our settings these factors probably exacerbated the epidemics' magnitude, duration, and case-fatality ratios. The effects of past drought and malnutrition are difficult to extricate: they are related causally, and either could result in impaired immunity (respectively, through reduced exposure to infection and nutrient deficiencies).
To our knowledge, no entomologic data were collected during any of these epidemics, which limits the strength of our findings; the role of changes in vector species or breeding habitats could have had a major role, but these factors can only be imputed from observed land pattern or climate alterations. Future studies on malaria epidemics should include detailed entomologic profiling, even during the epidemics.
In short, we believe that, given available evidence, to predicate epidemic prevention activities solely on the basis of individual risk factors (meteorologic or other) would be imprudent. Instead, appropriate decision support systems should be built that integrate all relevant data (e.g., environmental variables, food security and nutritional status, drug efficacy, health coverage, vector characteristics, population at risk) into a risk profile for each epidemic-prone population, to be updated regularly; in such a scenario, warning flags (
Even without early warning, detecting epidemics within 2 weeks of onset should be possible (
Free treatment and steady drug supplies probably favored early detection in Aweil East. Conversely, in Ethiopia, facility use was too low to reflect the magnitude of the emergency, and irregular drug distributions confounded epidemiologic monitoring. User fee systems may have long-term benefits, but cost barriers hamper treatment access (
By the time interventions were implemented, their potential effects were reduced. Mobile clinics were deployed to expand health access and detect severe cases. Implementation of clinics understandably varied according to local conditions, but apart from in Aweil East, probably had limited impact. Mobile clinic programs should be designed on the basis of clearly identified catchment areas and set frequencies with which communities should be visited. Although various criteria were used in our case studies, we believe that actual access to healthcare should be a key indicator for selecting target populations. Rapid methods to assess antimalarial treatment coverage thus need to be developed. How frequently communities are visited determines both the improvement in treatment coverage and the probability of preventing progression to severe disease through prompt treatment, which is likely to increase exponentially with frequency of mobile team visits; we hypothesize that frequent visits to selected sites may be more efficient than infrequent visits to a wider area. Impact monitoring should be included in future mobile clinic interventions to adjust their strategy as the situation evolves, and they should be evaluated after the fact. More generally, alternative modes of rapidly decentralizing care, such as fixed temporary health posts or training of resident community health workers (possibly equipped with artesunate suppositories to treat severely ill patients), merit further exploration. Where no clear indications exist that local health structures can cope with a large malaria epidemic, mobile clinics or other temporary treatment programs should be implemented immediately.
Case-fatality ratio among patients with complicated cases was lower than current best estimates of 10% (
Kayanza excluded, the increased proportion of children <5 years of age among inpatients, as previously observed in Kenya (
Clinic-based attack rates approach 100% for all age groups when extrapolated to the entire epidemic period (Kayanza and Gutten) and are even more alarming among children <5 years of age in Kisii. Even after overdiagnosis from presumptive treatment is accounted for, these rates are likely to be gross underestimates. The vast gap in treatment coverage was evident in Aweil East, where large-scale deployment of mobile clinics greatly increased output, and in Ethiopia, where despite capturing only the declining phase of the epidemic, uninterrupted provision of free care with effective drugs resulted in far higher outpatient and inpatient department attendance. The true community incidence in these epidemics is probably much greater than represented by regular reporting systems and higher than current estimates of 0.5 episodes of malaria per person per epidemic (
Malaria epidemics create daunting medical emergencies. In addition to ongoing research on alert systems, much greater donor investment is necessary to prevent and control them. All 4 countries in this study are moving to ACT combinations for outpatient treatment, a major improvement that is still insufficient unless 1) simple but valid surveillance data are transmitted and analyzed on a weekly basis, maximizing the chance of early epidemic detection, and 2) treatment coverage of uncomplicated and complicated cases truly reflects community needs. Further research is needed on methods to rapidly estimate needs (incidence) and coverage and on strategies to efficiently expand treatment access. Arguably, focusing resources only on how to predict and respond to epidemics might lead policymakers to overlook basic problems with access to effective treatment and tools for prevention that are common to both epidemic and stable malaria settings and that probably merit similar solutions. Donors and policymakers should thus aim for a balanced approach: improved capacity for epidemic prediction and response is needed, but long-term improvements in access to proper care and vector control by all members of the community, even before epidemics strike, must not be neglected, as they could be the most relevant determinants of decreased epidemic severity.
Because malaria epidemics are difficult to predict and multifactorial, setting up controlled studies to formally demonstrate the benefit of any single intervention will be difficult. Properly documenting the cost, feasibility, and output of these interventions and measuring the true extent of malaria epidemics are nevertheless crucial to inform the choice of future prevention and control strategies and must be included in the research agenda.
We characterized climatic and ecologic conditions during each epidemic by using meteorologic indexes obtained by remote sensing (station data were incomplete or unavailable). Dekadal (10-day) rainfall estimates for July 1981 through December 2005 and normalized difference vegetation indexes for July 1995 through December 2005 were obtained at 8-km spatial resolution from the Africa Data Dissemination Service (
In Kisii, estimated rainfall from September 1998 through January 1999 was 59% below the expected (536 mm), according to the 10-year rainfall estimate dataset. The long rains of March and April 1999 (1 month before epidemic onset) were unusually heavy (704 mm compared with a long-term average of 401 mm). Land-surface temperature for 1998 and 1999 generally fell within the normal range from 1995 through 2005, except during the preepidemic period, November 1998 through February 1999, when average maximum land-surface temperature (33.1°C) was 14% higher than the corresponding long-term average.
In Kayanza, unusually heavy rains occurred between the second dekad of February and the first dekad of April 2000 (460 mm vs. a 1995–2005 average of 304 mm), and an additional peak occurred during the last dekad of May (137 mm vs. 22 mm), 3 months before epidemic onset. Data from neighboring Ngozi Province suggested a rise in temperature (
In East Wollega Region, including Gutten Locality, 2002 and 2003 show similar drought patterns from the last dekad of April to the end of May (61 mm and 33 mm, respectively, compared with an average of 192 mm from 1995 through 2005), followed by above-average rainfall throughout June (preepidemic month), especially in 2003. Annual rainfall estimate averages for 2001 through 2003 (962 mm) seem markedly drier than preceding years (1996–2000, 1,431 mm). Drought conditions in the 2003 preepidemic months are also captured by mean normalized difference vegetation index data (0.38 from the last dekad of April to end of June 2003 vs. 0.5 average for the same period [1995–2005]). No land-surface temperature anomalies were apparent.
In Damot Gale, annual rainfall estimate averages from 2001 through 2003 (940 mm) were lower than for 1996 to 2000 (average 1,204 mm). In the preepidemic months of April, May, and June, rainfall estimate was 268 mm in 2002 (preepidemic year), compared with 376 mm in 2003 (epidemic year) and the 1996–2004 mean of 416 mm. land-surface temperature for this period seemed within normal range.
In Aweil East, 2000–2002 was relatively dry, particularly in 2002 when annual rainfall estimate was 40% below the long-term average (963 mm). Rainfall totals for 2003, however, were ≈20% higher, with a particularly rainy season leading up to May (preepidemic month). Land-surface temperature data suggest that temperatures in the second half of 2002 and first part of 2003 were within normal range, but during the epidemic period itself (June–November), mean maximum land-surface temperature (34.1°C) was 3.5°C below the period average.
All sites but Kenya were affected by conflict and displacement. Burundi's and southern Sudan's conflicts had lasted 7 and 20 years, respectively; Aweil East's population had been repeatedly displaced by militia incursions. Both Gutten and Damot Gale had received immigrants resettled from drought-stricken, nonmalarious areas.
Parasite resistance to first-line treatments was high in Kayanza (chloroquine [CQ]), Sudan (CQ), and Ethiopia (sulfadoxine-pyrimethamine [SP] at the peripheral level and the combination CQ+SP in better-equipped centers). SP was prescribed as first-line in Kenya and Ethiopia (primaquine coadministration was mandated by Ethiopian guidelines, but this drug was not supplied to Gutten and Damot Gale during the epidemic).
We thank MSF staff and local health authorities for their work on these malaria interventions and for sharing data with us.
This review was funded by Médecins Sans Frontières.
Mr Checchi is an infectious disease epidemiologist with interests in malaria, sleeping sickness, and public health in humanitarian emergencies. He was formerly with Epicentre, Paris, and is now studying for a PhD at the London School of Hygiene and Tropical Medicine.