Conceived and designed the experiments: REC RW CD. Analyzed the data: REC RW CD. Contributed reagents/materials/analysis tools: REC MA RW MO CD. Wrote the first draft of the manuscript: REC CD. Contributed to the writing of the manuscript: REC RW CD.
The authors have declared that no competing interests exist.
Richard Cibulskis and colleagues present estimates of the worldwide incidence of malaria in 2009, together with a critique of different estimation methods, including those based on risk maps constructed from surveys of parasite prevalence, and those based on routine case reports compiled by health ministries.
Measuring progress towards Millennium Development Goal 6, including estimates of, and time trends in, the number of malaria cases, has relied on risk maps constructed from surveys of parasite prevalence, and on routine case reports compiled by health ministries. Here we present a critique of both methods, illustrated with national incidence estimates for 2009.
We compiled information on the number of cases reported by National Malaria Control Programs in 99 countries with ongoing malaria transmission. For 71 countries we estimated the total incidence of
Estimates of malaria incidence derived from routine surveillance data were typically lower than those derived from surveys of parasite prevalence. Carefully interpreted surveillance data can be used to monitor malaria trends in response to control efforts, and to highlight areas where malaria programs and health information systems need to be strengthened. As malaria incidence declines around the world, evaluation of control efforts will increasingly rely on robust systems of routine surveillance.
Malaria is a life-threatening disease caused by the Plasmodium parasite, which is transmitted to people through the bites of infected mosquitoes. According to latest estimates from the World Health Organization (WHO), in 2009, there were 225 million cases of malaria and an estimated 781,000 deaths worldwide—most deaths occurring among children living in the WHO African Region (mainly sub-Saharan Africa). Knowing the burden of malaria in any country is an essential component of public health planning and accurately estimating the global burden is essential to monitor progress towards the United Nations' Millennium Development Goals.
Currently, there are generally two approaches used to estimate malaria incidence:
One method uses routine surveillance reports of malaria cases compiled by national health ministries, which are analyzed to take into account some deficincies in data collection, such as incomplete reporting by health facilities, the potential for overdiagnosis of malaria among patients with fever, and the use of private health facilities or none at all. The second method uses population-based surveys of Plasmodium prevalence and case incidence from selected locations in malaria endemic areas and then uses this information to generate risk maps and to estimate the case incidence of malaria per 1,000 population, for all of the world's malaria endemic regions. The Malaria Atlas Project—a database of malaria epidemiology based on medical intelligence and satellite-derived climate data—uses this second method.
In order for malaria epidemiology to be as accurate as possible, an evaluation of the strengths and weaknesses of both methods is necessary. In this study, the researchers analyzed the merits of the estimates calculated by using the different approaches, to highlight areas in which both methods need to be improved to provide better assessments of malaria control.
The researchers estimated the number of malaria cases in 2009, for each of the 99 countries with ongoing malaria transmission using a combination of the two methods. The researchers used the first method for 56 malaria endemic countries outside the WHO African Region, and for nine African countries which had the quality of data necessary to calculate estimates using the researchers statistical model—which the researchers devised to take the upper and lower limits of case detection into account. The researchers used the second method for 34 countries in the African Region to classify malaria risk into low-transmission and high-transmission categories, and then to derive incidence rates for populations from observational studies conducted in populations in which there were no malaria control activities. For both methods, the researchers conducted a statistical analysis to determine the range of uncertainty.
The researchers found that using a combination of methods there was a combined total of 225 million malaria cases, in the 99 countries malaria endemic countries—the majority of cases (78%) were in the WHO African region, followed by the Southeast Asian (15%) and Eastern Mediterranean regions. In Africa, there were 214 cases per 1,000 population, compared with 23 per 1,000 in the Eastern Mediterranean region, and 19 per 1,000 in the Southeast Asia region. Sixteen countries accounted for 80% of all estimated cases globally—all but two countries were in the African region. The researchers found that despite the differences between methods 1 and 2, the ratio of the upper and lower limit for country estimates was approximately the same.
Using the combined methods, the incidence of malaria was estimated to be lower than previous estimates, particularly outside of Africa. Nevertheless the methods suggest that malaria surveillance systems currently miss the majority of cases, detecting less than 10% of those estimated to occur globally. Although the best assessment of malaria burden and trends should rely on a combination of surveillance and survey data, accurate surveillance is the ultimate goal for malaria control programs, especially as routine surveillance has advantages for estimating case incidence, spatially and through time. However, as the researchers have identified in this study, strengthening surveillance requires a critical evaluation of inherent errors and these errors must be adequately addressed in order to have confidence in estimates of malaria burden and trends, and therefore, the return on investments for malaria control programs.
Please access these Web sites via the online version of this summary at
This study is further discussed in a
The WHO provides information on malaria and produces the World Malaria Report each year, summarizing
More information is available on
Knowing the number of malaria cases that occur annually in any country is an essential component of planning national health services and evaluating their effectiveness. Reliable data from each endemic country are needed to assess progress globally towards the United Nations Millennium Development Goals. At present there are broadly two approaches to estimating malaria incidence country by country. One method uses routine surveillance reports of malaria cases compiled by health ministries, adjusted to take into account incomplete case detection by health facilities, the potential for overdiagnosis of malaria among patients with fevers, and the way patients use public and private health services
The most recent presentation of estimates made primarily by cartography (from MAP)
This study includes a critique of methods used to assess the scale of the malaria problem worldwide, illustrated with estimates derived by the two principal methods. Besides making some allowance for vector control, we do not attempt to explain the geographical and temporal distribution of malaria cases in terms of the characteristics of vectors, hosts, and environment; that would require additional data and further work.
The estimation methods used in this study are described briefly below and fully in
A case of malaria was defined as fever with
Upper and lower limits for the estimated number of cases,
Values of
The number of countries providing data at different administrative levels (from national level 0 down to subnational level 5) were: level 0, 13; level 1, 71; level 2, 19; level 3, 2; level 4, 0; level 5, 1. The total of 106 countries affected by malaria, includes the 99 with ongoing transmission, and seven in the WHO “prevention of reintroduction” phase. Where national data were incomplete, the whole country is marked as such on the map.
Parameter | Assumed Distribution | Description | ||||
Parameter derived from reported data | ||||||
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For each value of reporting completeness, |
Reported Value | Distribution | Minimum | Most Likely | Maximum |
80%+ | Triangular | 80% | 80% | 100% | ||
50–80% | Uniform | 50% | — | 80% | ||
<50% | Triangular | 0% | 50% | 50% | ||
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The uncertainty analysis aimed to reflect the variation of |
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Parameter | If parameter imputed | |||||
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The reporting rate was assumed to have uniform distribution with a range between 50% and 80%. | |||||
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If a country did not report a slide positivity rate, values of |
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If a relevant household survey was not available for a country, values of |
This method was used for 34 countries in sub-Saharan Africa where transmission is relatively homogenous and a broad categorization of malaria risk into either low transmission or high transmission is possible.
The annual incidence of malaria was estimated in two steps. First, populations in each country were classified as living at either high, low, or no risk of malaria. Malaria risk for each African country was defined according to climatic suitability, as per the Mapping Malaria Risk in Africa (MARA) project estimate for the year 2002
Age | High Transmission | Low Transmission | Southern Africa | ||||||
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Under-5s | 28 | 1.424 | 0.838–2.167 | 4 | 0.182 | 0.125–0.216 | 5 | 0.029 | 0.097–0.129 |
5–14 y | 19 | 0.587 | 0.383–0.977 |
|
0.182 |
0.125–0.216 |
|
0.029 | 0.097–0.129 |
≥15 y | 7 | 0.107 | 0.074–0.138 |
|
0.091 |
0.063–0.108 |
|
0.029 | 0.097–0.129 |
|
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Under-5s |
|
0.712 |
0.419–1.084 |
|
0.182 |
0.125–0.216 |
|
0.029 | 0.097–0.129 |
5–14 y |
|
0.587 |
0.383–0.977 |
|
0.182 |
0.125–0.216 |
|
0.029 | 0.097–0.129 |
≥15 y |
|
0.107 |
0.074–0.138 |
|
0.091 |
0.063–0.108 |
|
0.029 | 0.097–0.129 |
No observations available so assumed to be the same as that measured in children under 5 by Snow et al
No observations available so assumed to be half the rate of children 5–14 y by Snow et al
Estimated to be approximately half the rate of rural areas by Korenromp
Considered to be the same as in rural areas by Korenromp
IQR, interquartile range.
Because the incidence estimates were for 2002 populations or earlier, and those populations were not subject to malaria control measures, the estimates are adjusted downward for each country according to the expected impact of insecticide treated mosquito nets (ITNs) by 2009, and also to take account of lower incidence rates in urban areas
For both methods an estimate of
An underlying distribution was assumed for each of the parameters used in incidence estimation (
Methods 1 and 2 applied to 99 countries together produced a total estimate of 225 million malaria cases worldwide in 2009 (5th–95th centiles, 146–315 million) (
WHO Regions | Population (m) | Estimated Cases | ||||||
Best (000s) | Low (000s) | High (000s) | Best (per 1,000) | Low (per 1,000) | High (per 1,000) | |||
Africa | 821 | 175,969 | 109,591 | 248,178 | 214 | 133 | 302 | 98 |
Americas | 543 | 1,132 | 923 | 1,439 | 2 | 2 | 3 | 38 |
Eastern Mediterranean | 523 | 12,120 | 8,668 | 17,816 | 23 | 17 | 34 | 84 |
Europe | 272 | 0.64 | 0.54 | 0.76 | 0.0024 | 0.0020 | 0.0028 | 21 |
Southeast Asia | 1,783 | 33,817 | 24,993 | 45,903 | 19 | 14 | 26 | 58 |
Western Pacific | 1,638 | 2,257 | 1,910 | 2,618 | 1 | 1 | 2 | 79 |
World (99 countries) | 5,580 | 225,296 | 146,085 | 315,955 | 40 | 26 | 57 | 91 |
An estimated 91% or 205 million cases were due to
Despite the differences between methods 1 and 2, the ratio of 95th/5th centiles for country estimates was approximately the same (geometric mean 2.3 for method 1 and 2.2 for method 2).
Methods 1 and 2, together with national case reports, also yield estimates of the percentage of cases detected and confirmed by malaria control programs. We estimate that 8% of
Regions | Reported |
WHO 2009 | MAP 2007 | |||
Estimated |
Reported/Estimated (%) |
Reported (000s) | Estimated |
Reported/Estimated (%) |
||
Africa | 1,2799 | 172,975 | 7 (7) | 71,611 | 260,994 | 5 |
Americas | 145 | 426 | 34 (50) | 788 | 3,047 | 5 |
Eastern Mediterranean | 950 | 10,153 | 9 (8) | 8,449 | 13,875 | 7 |
Europe | 0 | 0 | (96) |
1.44 | 0 | — |
Southeast Asia | 1,518 | 19,588 | 8 (8) | 3,784 | 154,057 | 1 |
Western Pacific | 182 | 1,774 | 10 (11) | 1,946 | 18,959 | 1 |
World (99 countries) | 15,594 | 204,915 | 8 (8) | 86,579 | 450,932 | 3 |
Numbers in brackets are for
MAP estimates are compared with reported cases in 2009 because there has been an increase in case reporting since 2007.
No cases of
The overall proportions of cases detected depend on each of the elements of Model 1, and there were differences among regions in the importance of each element, and in the availability of data in 2009 (
WHO Regions | Suspected (000s) | Unconfirmed, |
Examined (Microscopy and RDT, 000s) | Examined/Suspected (%) |
Confirmed, |
Confirmed/Examined ( |
Reporting Completeness ( |
Patients Seeking Treatment (1- |
Patients Treated in Public Health Facilities ( |
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Mean | Lower | Upper | Mean | Lower | Upper | Mean | Lower | Upper | Mean | Lower | Upper | |||||||
Africa | 9 | 86691 | 57293 | 29399 | 34 | 13165 | 23 | 4 | 53 | 80 | 72 | 87 | 63 | 58 | 68 | 41 | 36 | 46 |
Americas | 21 | 6872 | 0 | 6872 | 100 | 562 | 6 | 3 | 12 | 77 | 72 | 82 | 80 | 65 | 95 | 69 | 54 | 84 |
Eastern Mediterranean | 9 | 18082 | 6505 | 11578 | 64 | 1018 | 10 | 3 | 21 | 65 | 52 | 79 | 75 | 45 | 106 | 56 | 25 | 86 |
Europe | 6 | 2207 | 0 | 2207 | 100 | 0.45 | 0 | 0 | 0 | 83 | 76 | 91 | 100 | 100 | 100 | 99 | 99 | 99 |
Southeast Asia | 10 | 111105 | 643 | 110462 | 99 | 2404 | 17 | 5 | 36 | 78 | 71 | 85 | 62 | 52 | 71 | 17 | 7 | 26 |
Western Pacific | 10 | 11703 | 1405 | 10297 | 88 | 247 | 21 | 8 | 42 | 87 | 87 | 87 | 71 | 56 | 87 | 40 | 24 | 56 |
World | 65 | 236661 | 65845 | 170816 | 72 | 17396 | 12 | 8 | 30 | 78 | 75 | 81 | 71 | 64 | 77 | 50 | 44 | 57 |
Only considers countries for which method 1 was applied.
The application of these methods for all years from 2000 to 2009 suggests that the number of cases increased worldwide until 2005 and has been falling slowly since then (
Cases | 2000 | 2001 | 2002 | 2003 | 2004 | 2005 | 2006 | 2007 | 2008 | 2009 | Decline Percent/Year |
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Africa | 173 | 178 | 181 | 185 | 187 | 188 | 187 | 186 | 181 | 176 | 0.2 |
Americas | 2.8 | 2.3 | 2.2 | 2.1 | 1.9 | 1.9 | 1.7 | 1.5 | 1.1 | 1.1 | −9.9 |
Eastern Mediterranean | 15 | 16 | 17 | 16 | 14 | 12 | 12 | 12 | 13 | 12 | −3.6 |
Europe | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | −49.6 |
Southeast Asia | 38 | 38 | 35 | 35 | 37 | 39 | 34 | 32 | 34 | 34 | −1.4 |
Western Pacific | 2.8 | 2.5 | 2.2 | 2.5 | 2.8 | 2.3 | 2.5 | 2.1 | 1.9 | 2.3 | −2.6 |
World | 233 | 236 | 237 | 241 | 243 | 244 | 238 | 233 | 231 | 225 | −0.4 |
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Africa | 264 | 263 | 262 | 261 | 258 | 253 | 245 | 237 | 225 | 214 | −2.2 |
Americas | 5.9 | 4.8 | 4.4 | 4.1 | 3.8 | 3.7 | 3.2 | 2.7 | 2.0 | 2.1 | −11.2 |
Eastern Mediterranean | 45 | 44 | 46 | 44 | 39 | 31 | 32 | 30 | 32 | 29 | −5.8 |
Europe | 0.41 | 0.29 | 0.23 | 0.18 | 0.11 | 0.06 | 0.03 | 0.02 | 0.01 | 0.01 | −50.7 |
Southeast Asia | 24 | 24 | 22 | 21 | 22 | 23 | 20 | 18 | 19 | 19 | −2.9 |
Western Pacific | 1.8 | 1.6 | 1.4 | 1.6 | 1.8 | 1.4 | 1.6 | 1.3 | 1.1 | 1.4 | −3.4 |
World | 50 | 50 | 49 | 49 | 49 | 48 | 47 | 45 | 44 | 42 | −1.8 |
Our estimate of malaria case incidence for the African region is 176 (110–248) million cases in 2009 of which 173 million were estimated to be due to infection with
Although our interpretation of method 2 and that of MAP are based on the same principles, MAP's estimates
The potential weakness of surveillance-based estimates lies in the quality of the data that are used to measure five key variables: reporting completeness (
As weaknesses in surveillance are recognized and addressed, estimates will be improved, and the ability of national control programs to monitor progress and manage resources will be strengthened. In Southeast Asia, for example, it is clear that national malaria control programs need to work more closely with private providers to ensure appropriate diagnosis and treatment and accurate monitoring. Diagnostic accuracy will improve as parasitological diagnosis of malaria, including use of RDTs, is made more widely available and malaria control programs follow international guidance
There is also scope to improve the design and coverage of household surveys in order to assist the interpretation of surveillance data
In sum, estimates based on surveillance data might be too low or too high. Having made a checklist of the potential sources of bias (
There are two reasons prima facie why the higher estimates of case incidence derived from surveys
The details of case reporting from specific countries support the view that MAP estimates are too high. The Vector Borne Disease Control Programme (VBDCP) in India examined blood slides from 95.4 million suspected cases in 2009, approximately 8% of the population, yet detected only 844,000 slides positive for
Second, MAP's estimates of
There are three further aspects of method 2 that could overestimate incidence, either in our hands or with the more sophisticated approaches now used by MAP. First, the surveys of parasite prevalence and case incidence that determine the spatial distribution of malaria risk vary in method and purpose. The surveys were not designed to give unbiased estimates of the national prevalence of malaria infection. One potential problem is that parasite prevalence surveys have been carried out in areas of relatively high malaria incidence. In India, for example, most surveys have been done in the high incidence areas of Assam and Orissa
The second problem, related to the issues surrounding prevalence surveys, is that the procedure for delineating areas with stable malaria tends to overestimate the population at risk where the administrative unit is large. For China, MAP classified populations at the second administrative level (prefecture), noting whether the number of reported
Third, the latest risk map from MAP is intended to represent the situation in 2007
In addition to these possible sources of bias, other factors affect uncertainty (
Finally, our assessment of malaria trends in Africa, which takes into account only only the impact of ITNs and not other control measures, might underestimate the rate of decline. We have not taken into account the use of indoor residual spraying or the availability of more effective treatment with ACTs. Moreover, it is well known that factors other than vector control influence mosquito abundance, species composition, and human biting rates. These factors include urbanization, trends in rainfall, temperature, and humidity, changes in land use, and improved housing construction
Method 1, based on routine surveillance data, gives lower estimates of case incidence than method 2, based on population surveys, especially for non-African countries. The large discrepancies for some non-African countries, notably India, will only be resolved with further data and careful validation.
Although the best assessment of malaria burden and trends today must rely on a combination of surveillance and survey data, accurate surveillance is the ultimate goal for malaria control programs (expanding the database depicted in
(DOC)
We thank staff of WHO Regional Offices, Nathan Bakyaita, Rainier Escalada, Bayo Fatunmbi, Elkhan Gasimov, Etienne Minkoulou, Rakesh Rastogi, and Ghasem Zamani for their critical evaluation of the methodology, suggestions for improvement, and for collation of data and validation of results. We also thank WHO country offices and National Malaria Control programs for providing data, and comments on methods and results for individual countries. Colin Mathers assisted in the validation of methods and in aligning estimates of malaria cases with the Global Burden of Disease Project. Members of the malaria Monitoring and Evaluation Reference Group (Morbidity Task Force) assisted in developing the methods and interpretation of results.
insecticide treated mosquito net
Malaria Atlas Project
rapid diagnostic test
World Health Organization