Nov 12, 2009 - Additional file 1 - Updating the global spatial limits of Plasmodium ... incidence less than 0.1‰) or stable (annual incidence exceeding 0.1‰) .... The best average spatial resolution (ASR) was attained in the ..... Swaroop S, Gilroy AB, Uemura K (1966) Statistical methods in ...... 1km 5km 10km Description.
Additional file 1 - Updating the global spatial limits of Plasmodium falciparum malaria transmission for 2010
A1.1 Overview We have previously partitioned the task of generating a global endemicity map into two stages: the delineation of regions experiencing endemic transmission [1] and the subsequent prediction of endemicity within those regions based on data from parasite rate surveys [2]. In principle, the latter stage alone could generate a global map but reliance on PfPR data to resolve the outer fringes of areas at risk is suboptimal [3-5] because (i) parasite surveys are less commonly conducted in regions of very low prevalence towards the margins of the disease's range, where malaria rarely constitutes a major public health problem, and (ii) such surveys are inherently ill-suited to distinguishing low from zero risk as they become statistically underpowered to detect very low rates of infection in local populations [6-8]. Instead, our approach [1] has been to use alternative empirical data, augmented by biological suitability maps, to stratify the globe into areas considered risk-free or at-risk of unstable (characterised by annual incidence less than 0.1‰) or stable (annual incidence exceeding 0.1‰) transmission. The components used to generate these classifications are (i) an initial identification of those countries housing autochthonous transmission within their borders (the P. falciparum malaria endemic countries, PfMECs); (ii) sub-nationally reported incidence records from health management information systems (P. falciparum annual parasite incidence data, PfAPI); (iii) additional medical intelligence providing refined risk designations for specific regions such as islands or cities; (iv) exclusion of risk in areas where the local annual temperature regime cannot support transmission in an average year; and (v) further exclusion or downgrading of risk in areas where extreme aridity is likely to limit transmission. Each of these components has been completely updated to define new transmission limits for 2010. In this additional file we present these new data assemblies and provide details of each stage of data assembly and analysis.
A1.2 Updating the number of countries considered P. falciparum malaria endemic The first version of the P. falciparum spatial limits map was developed upon a template consisting of 87 PfMECs [1]. This list of countries was revised for the current iteration and two countries were excluded: Belize and Kyrgyzstan. Belize has not reported P. falciparum cases since 2007 [9] and Kyrgyzstan is classified by the latest travel and health guidelines consulted [10,11] as P. vivax endemic only, with rare imported P. falciparum cases. This left 85 PfMECs for consideration in 2010.
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A1.3 Updating national risk extents with P. falciparum annual parasite incidence data PfAPI Data Processing The PfAPI data by country were obtained from various sources (Table A1.1). The format in which these data were made available varied considerably between countries. Ideally, the data would be available by administrative unit and by year, with each record presenting the estimated population for the administrative unit and the number of confirmed, autochthonous malaria cases by the two main human malaria parasite species (P. falciparum and P. vivax), which would allow an estimation of species-specific API. The PfAPI values were also often provided directly from the source. These requirements were sometimes not fulfilled completely and a number of problems were faced during data entry. First, population data by administrative unit were sometimes unavailable, in which cases these data were sourced separately or extrapolated from recent years to estimate PfAPI. Second, not all API data were species-specific. In these cases, a parasite species ratio was inferred from alternative sources and applied to provide an estimate of species-specific API. For example, such a ratio was often available as a single national figure, in which case it was applied uniformly throughout the country. Third, although a differentiation between confirmed and suspected cases and between autochthonous and imported cases was often provided, in some cases it had to be assumed that the data referred to confirmed, autochthonous cases. Lastly, the annual blood examination and slide positivity rates were seldom reported and were not included in the database.
PfAPI Data Summaries Table A1.1 summarizes PfAPI data characteristics for all PfMECs for which these were available. PfAPI data were not available for countries in the Africa+ region, with the exception of Djibouti, Namibia, Saudi Arabia, South Africa, Swaziland and Yemen. For Botswana, risk was constrained to northern districts based upon information from the travel and health guidelines consulted [10,11], assuming stable risk in malaria transmission areas. Expert opinion confirmed that in Cape Verde unstable risk of malaria is constrained to Santiago Island [12,13]. For other countries in this region, stable risk of P. falciparum transmission was assumed to be present throughout their territories. In total, PfAPI data were not available for 42 identified PfMECs, all in Africa+. The majority of the PfAPI data (n=43 countries) were obtained through personal communication with individuals and institutions linked to national malaria control activities in each country. These are cited in Table A1.1 and acknowledged on the MAP website (http://www.map.ox.ac.uk/acknowledgements/). The specific aim was to collate data for the four most recent years of reporting, ideally including 2009. For six countries the last year of reporting 2
available was 2009. For 21 countries, 2008 was the last year of reporting available, whilst 2007 and 2006 were the last years available for ten and five countries, respectively. For Colombia, risk data could not be obtained after 2005. In terms of the length of the period of reporting, one year of data was available for nine countries, two years for four countries, three years for six countries and four years for 24 countries (Table A1.1). A total of 15 countries reported at ADMIN1 level and 22 at ADMIN2 level. For southern China, Myanmar, Nepal and Peru, data were available at ADMIN3 level. In central and northern China data were available at ADMIN1 level. Data for Namibia and Venezuela were a mixture of ADMIN1 and ADMIN2 levels. The best average spatial resolution (ASR) was attained in the Dominican Republic (ASR = 17) and the poorest in Saudi Arabia (ASR = 385). In total, 13,449 administrative units in 43 countries were populated with PfAPI data (Table A1.1). The higher spatial resolution attained in many countries for this iteration of the limits map translated into a 53% increase in the total number of mapped administrative units compared to the 2007 version of the map [1].
Mapping PfAPI Data In order to map PfAPI data consistently, they were reconciled to the 2009 version of the Global Administrative Unit Layers (GAUL) data set, implemented by the Food and Agriculture Organization of the United Nations (FAO) within the EC FAO Food Security for Action Programme [14]. In some cases this reconciliation was not straightforward given problems with transliteration of administrative unit names or actual differences in national sub-divisions. In such cases, alternative sources and maps were used to guide adequate matching of PfAPI data. For some countries, digital boundary files of the administrative sub-divisions corresponding to PfAPI data were supplied. These countries were: Afghanistan, Indonesia, Myanmar, Papua New Guinea, Peru, Solomon Islands, South Africa and Vietnam. In these cases, coastlines remained the same as the supplied shape files whilst borders between countries were made congruent with those in the GAUL dataset. Classification of risk based on PfAPI data was done as described previously [1]. Areas of extremely low, unstable transmission of P. falciparum were assigned to administrative units reporting PfAPI of less than 0.1 cases per 1,000 population per annum (p.a.), and those reporting a PfAPI of ≥ 0.1 cases per 1,000 population p.a. were classified as being of stable transmission.
A1.4 Updating the biological masks of transmission exclusion For the previous iteration of the spatial limits map, two masks of risk exclusion/modulation were applied on the PfAPI data-defined limits of transmission: a temperature and an aridity mask 3
[1]. The methodology and data used to implement these masks have been updated and are described below.
Temperature Mask In some regions, ambient temperature plays a key role in suppressing or precluding P. falciparum transmission via various effects on stages of the parasite and Anopheles vector life cycles - most importantly by modulating the duration of the extrinsic incubation period of the parasite within the vector and by affecting daily survival rates of the latter [15-19]. We have previously used monthly average temperature data in combination with a simple threshold rule to identify pixels where average monthly temperatures were likely to preclude transmission yearround [1]. For the current iteration we refined substantially the underlying biological model to evaluate temperature effects dynamically through time to generate for each pixel an index of temperature suitability proportional to vectorial capacity, an established biological metric of potential transmission intensity [20,21]. The refinements to the implementation of the temperature mask are detailed elsewhere [22]. In brief, synoptic mean, maximum, and minimum monthly temperature records from 30-arcsec (~1×1 km) spatial resolution climate surfaces [23] were converted to a continuous time series using spline interpolation. This represented the mean temperature profile across an average year. Diurnal variation [24] was incorporated by adding a sinusoidal component to the time series with a wavelength of 24 hours and the amplitude driven by the difference between the spline-smoothed monthly minimum and maximum values. Ambient temperature can limit or preclude malaria transmission via a number of influences on components of the transmission cycle. Although temperature effects have been described on the survival and emergence rates of mosquito larvae [25,26], and vector feeding rates [27,28], the limiting effects of temperature on transmission are most pronounced in the interaction between vector lifespan and the duration of sporogony: the extrinsic incubation period during which the parasite matures into the sporozoite life stage within the vector. For P. falciparum transmission to be biologically feasible, a cohort of anopheline vectors infected with the parasite must survive long enough for sporogony to complete within their lifetime. We modelled daily vector survival rate as a continuous function of local temperature regimes within each pixel using an established relationship drawn from a series of observational and modelling studies [16-18]. Maximum vector lifespan was defined as 31 days since estimates of the longevity of the main dominant vectors [19] indicate that 99% of anopheline vectors die in less than a month. The exceptions were areas that support the longer-lived Anopheles sergentii and An. superpictus, where 62 days were more appropriate [1]. Sporogony is also strongly dependent on ambient temperature, so the time required for its completion varies continuously as temperatures fluctuate across a year [15]. The dependence of sporogony duration on temperature is classically expressed using a simple temperature-sum model [29] in which sporogony occurs after a fixed number of degree4
days over a minimum temperature threshold for development. Widely used parameterisations from studies on Anopheles maculipennis [15,27] define a degree-day requirement for P. falciparum of 111, and a minimum temperature for development of 16°C. The interaction between vector life span and sporogony duration was modelled for each pixel based on an assumption of constant vector emergence and the continuous evaluation of the expressions for daily vector survival and accumulation of degree days towards sporogony. A system of difference equations was implemented that, in effect, simulated the emergence of successive vector cohorts throughout the year, their declining population size as a function of temperature, and whether any constituent vectors survived long enough to complete sporogony. Those pixels in which no window existed across the year for the completion of sporogony were classified as being at zero risk of transmission. The temperature mask resulting from this process is shown in Figure A1.1.
Aridity Mask A second driver of environmental suitability for P. falciparum transmission is the availability of moisture. Again, we modified for this iteration our earlier approach [1] to mapping those areas where extreme aridity is likely to prevent transmission by restricting vector survival and availability of oviposition sites [30,31]. A month-by-month classification rule based on threshold values of remotely-sensed vegetation index data [32] was replaced by the more straightforward use of pixels defined as 'bare areas' by the GlobCover land-cover classification product (ESA/ESA GlobCover Project, led by MEDIAS-France/POSTEL) [33]. This designation was considered a more parsimonious method of identifying areas devoid of any significant vegetation and, hence, unlikely to be associated with sufficient moisture to support Anopheles populations. GlobCover products are derived from data provided by the Medium Resolution Imaging Spectrometer (MERIS), on board the European Space Agency’s (ESA) ENVIronmental SATellite (ENVISAT), for the period between December 2004 and June 2006, and are available at a spatial resolution of 300 meters [33]. This layer was first resampled to a 1×1 km grid using a majority filter, and all pixels classified as “bare areas” by GlobCover were overlaid onto the PfAPI surface. The result is shown in Figure A1.2. The aridity mask was treated differently from the temperature mask to allow for the possibility of the adaptation of human and vector populations to arid environments [34,35]. A more conservative approach was taken whereby risk was down-regulated by one class. In other words, GlobCover’s bare areas defined originally as at stable risk by PfAPI were stepped down to unstable risk and those classified initially as unstable were classed as malaria free.
A1.5 Implementing the medical intelligence modifications For this 2010 iteration of the limits map, a medical intelligence layer was generated to further constrain risk in areas where malaria transmission is absent according to expert opinion. These 5
areas include cities, administrative areas and other sub-national territories. Their identification and the rules applied to modify risk of transmission are described below.
Urban Areas Urban areas are less malarious than the surrounding rural environments due to the distinct ecological conditions presented by man-made environments [36,37]. The extent to which transmission is reduced will vary according to the local Anopheles species. Urbanization has been shown to reduce malaria transmission, measured by the entomological inoculation rate, by an order of magnitude across Africa, due to reduced vector diversity and density, as well as lower anopheline survival, biting and sporozoite rates in urban versus rural areas [36]. Anopheles darlingi, the main malaria vector in America, has also been demonstrated to be unsuited to urban environments [38]. Urban malaria transmission is more entrenched in the Indian subcontinent because of the presence of An. stephensi and, to a lesser extent, An. culicifacies, both recognised urban malaria vectors [39]. No malaria vector is better adapted to urban environments than An. stephensi, and this is due to its ability to breed in all types of artificial collections of water, such as wells, pits, tanks and drains [40]. Anopheles culicifacies is less resilient to man-made environments and is particularly affected by pollution of water sources [40,41]. Importantly, the vector densities and sporozoite rates of both these species have been shown to decrease from peri-urban to urban areas [42,40,43]. Despite this, it is estimated that approximately 8% of malaria cases in India are reported from urban areas [44], with incidence often surpassing the stable risk threshold. Reported API estimates amongst 86 cities across India in 1993 ranged from 0 to 51.85 cases per 1,000 people p.a., with a median of 0.97 [45]. Seventy of these cities would have been classified as supporting stable transmission according to the API threshold used in this paper (i.e. API ≥ 0.1 cases per 1,000 people p.a.). Since An. culicifacies seems to be more affected by the process of urbanization, it was assumed that urban malaria transmission is maintained mainly by An. stephensi as defined by the rules of risk modulation described below. There are 51 cities cited as being malaria free in the two international travel and health guidelines consulted [10,11] (Table A1.2). In addition, urban areas in China, the Philippines and Indonesia (specifically those located in Sumatra, Kalimantan, Nusa Tenggara Barat and Sulawesi) are reported to be malaria free. This is obviously not a comprehensive list of malariafree cities but rather one restricted to main destinations of interest to travellers. Specific cities were geo-positioned and their urban extents were identified using the Global Rural Urban Mapping Project (GRUMP) urban extents layer [46]. In China, the Philippines and the areas of Indonesia specified above, all urban extents were identified and mapped. The resulting layer was overlaid on the PfAPI layer and biological masks to identify the underlying risk of malaria. Those cities falling within the range of An. stephensi [47] were also identified. 6
Of the 51 specified cities, 14 are in areas where malaria transmission is absent as defined by the PfAPI layer and the biological masks (e.g. highland areas). The urban extents of the remaining 37 cities cover areas defined as unstable or stable transmission or both (Table A1.2). Eight of these cities fall within the range of An. stephensi [47]: six in India (Bangalore, Kolkata, Mumbai, Nagpur, Nashik and Pune) and two in Myanmar (Mandalay and Yangon). In addition, urban areas in south-western Yunnan, China, also fall in areas inhabited by this vector. For those cities falling within the range of An. stephensi, transmission was assumed to be one level lower than the surrounding risk defined by PfAPI data and the biological masks to allow for the potential transmission of malaria by An. stephensi combined with the transmission reducing effects of urban areas [42,40,43]. Transmission was assumed to be zero in the remaining 29 cities.
Sub-national Territories and Administrative Areas Some sub-national territories and administrative areas are listed as being malaria free by the international travel and health guidelines consulted [10,11] (Table A1.3). These were mapped using the GAUL data set [14] and risk within them was assigned a malaria free category, if not already classified as such by the PfAPI layer and the biological masks. In addition to the territories listed in Table A1.3, the island of Socotra, in Yemen, has not reported cases since 2005 after malaria elimination activities were initiated in 2000 [48]; this island was considered to be malaria free. Two further exclusions were those of the island of Aneityum, in Vanuatu [49], and the Angkor Watt area, in Cambodia, corresponding to two districts in Siem Reap province, that were classified as malaria free following personal communication with malaria experts in these countries (Dr Akira Kaneko and Dr Doung Socheat, respectively).
Assembling the P. falciparum Spatial Limits Map Figure A1.3 summarises the different steps undertaken to assemble the P. falciparum spatial limits map. The layers described above were progressively applied on a geographical information system with subsequent reductions in estimated area and population at risk. This sequence is illustrated as different maps in Figure A1.4, and differences to the earlier 2007 iteration [1] are shown in Figure A1.5.
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Manila, Philippines: World Health Organization / Regional Office for the Western Pacific. 77. W.H.O. / S.E.A.R.O. (2010) Personal communication from Rakesh M. Rastogi, January 2010. New Delhi, India: World Health Organization / Regional Office for South-East Asia. 78. M.o.H.P. (2009) Personal communication from Suman Thapa, August 2009. Kathmandu, Nepal: Epidemiology and Disease Control Division, Department of Health Services, Ministry of Health and Population. 79. W.H.O. (2009) Information from the Global Malaria Program, World Health Organization, February 2009. Geneva, Switzerland: World Health Organization. 80. D.o.H. (2009) Personal communication from Dorina G. Bustos and Cristina Galang, July 2009. Muntinlupa City, Philippines: Research Institute for Tropical Medicine and Malaria Control Program, Department of Health. 81.
W.H.O.
/
W.P.R.O.
(2009)
Malaria
epidemiology,
http://www.wpro.who.int/sites/mvp/epidemiology/malaria/.
Solomon
Accessed
Islands.
URL,
November
2009.
Manila, Philippines: World Health Organization / Regional Office for the Western Pacific. 82. M.o.H. (2010) Personal communication from Gawrie N. Galappaththy, August 2009. Colombo, Sri Lanka: National Malaria Control Programme, Ministry of Health. 83. W.H.O. (2009) Personal communication from Nargis Saparova, October 2009. Dushanbe, Tajikistan: World Health Organization / Country Office in Tajikistan. 84. M.o.P.H. (2009) Personal communication from Wichai Satimai and Theeraphap Chareonviriyaphap, June 2009. Nonthaburi, Thailand: Bureau of Vector Borne Diseases, Department of Disease Control, Ministry of Public Health. 85. M.o.H. (2009) Personal communication from Johanes Don Bosco, October 2009. Dili, Timor-Leste Division of Communicable Diseases, Ministry of Health. 86.
W.H.O.
/
W.P.R.O.
(2009)
Malaria
epidemiology,
http://www.wpro.who.int/sites/mvp/epidemiology/malaria/.
Accessed
Vanuatu.
URL,
November
2009.
Manila, Philippines: World Health Organization / Regional Office for the Western Pacific. 87. M.o.H. (2008) Personal communication from Nguyen Manh Hung, December 2008. Ha Noi City, Vietnam: National Institute of Malariology, Parasitology and Entomology (NIMPE), Ministry of Health.
13
Table A1.1. Summary of the P. falciparum annual parasite incidence (PfAPI) data assembled for each country. The data are grouped by the three global regions defined by Hay et al. [2]: Africa+, America and Central and South East (CSE) Asia. ADMIN1, 2 or 3 refers to the administrative division level (first, second or third level) at which data were available. The number of risk units refers to how many administrative units, at the level specified, were populated with actual data. Year start and Year end mark the start and end of the period for which data were available. The average spatial resolution (ASR) of the mapped PfAPI data is calculated as the square root of (country area / number of PfAPI data units mapped). Region
Country
Admin. level
Africa+
Djibouti
ADMIN1
Africa+
Namibia
Africa+
Risk units
Year start
Year end
5
2007
2009
66
[50]
ADMIN1 & ADMIN2
30
2009
2009
166
[51]
Saudi Arabia
ADMIN1
13
2005
2006
385
[52]
Africa+
South Africa
ADMIN2
257
2006
2009
69
[53]
Africa+
Swaziland
ADMIN2
53
2007
2009
18
[54]
Africa+
Yemen
ADMIN1
19
2002
2006
155
[52]
America
Bolivia
ADMIN2
113
2008
2008
98
[55]
America
Brazil
ADMIN2
5510
2004
2008
39
[56]
America
Colombia
ADMIN2
1087
2005
2005
32
[57]
America
Dominican Republic
ADMIN2
162
2008
2008
17
[58]
America
Ecuador
ADMIN2
220
2005
2008
34
[59]
America
French Guiana
ADMIN2
21
2006
2006
63
[60]
America
Guatemala
ADMIN1
22
2006
2006
71
[61]
America
Guyana
ADMIN1
10
2004
2007
145
[62]
America
Haiti
ADMIN1
10
2006
2006
52
[61]
America
Honduras
ADMIN2
291
2005
2008
20
[63]
America
Nicaragua
ADMIN1
17
2004
2007
87
[64]
America
Panama
ADMIN2
68
2006
2007
33
[65]
America
Peru
ADMIN3
1828
2005
2008
27
[66]
America
Suriname
ADMIN1
10
2008
2008
121
[67]
America
Venezuela
ADMIN1 & ADMIN2
30
2004
2008
175
[68]
CSE Asia
Afghanistan
ADMIN2
398
2005
2008
40
[52]
CSE Asia
Bangladesh
ADMIN2
64
2007
2008
46
[69]
CSE Asia
Bhutan
ADMIN1
20
2005
2009
43
[70]
CSE Asia
Cambodia
ADMIN1
26
2005
2008
84
[71]
CSE Asia
China
ADMIN1 & ADMIN3
263
2003
2007
189
[72]
CSE Asia
India
ADMIN2
574
2004
2007
72
[73]
CSE Asia
Indonesia
ADMIN2
346
2005
2008
74
[74]
CSE Asia
Iran
ADMIN2
283
2007
2008
76
[52]
CSE Asia
Lao PDR
ADMIN2
139
2006
2008
41
[75]
14
ASR
Source
Region
Country
Admin. level
CSE Asia
Malaysia
ADMIN1
CSE Asia
Myanmar
CSE Asia
Risk units
Year start
Year end
15
2003
2007
149
[76]
ADMIN3
325
2006
2008
45
[77]
Nepal
ADMIN3
75
2005
2008
44
[78]
CSE Asia
Pakistan
ADMIN2
119
2005
2008
82
[52]
CSE Asia
Papua New Guinea
ADMIN2
87
2005
2007
73
[79]
CSE Asia
Philippines
ADMIN2
82
2004
2007
60
[80]
CSE Asia
Solomon Islands
ADMIN1
10
2003
2007
54
[81]
CSE Asia
Sri Lanka
ADMIN2
25
2006
2009
52
[82]
CSE Asia
Tajikistan
ADMIN2
56
2005
2008
50
[83]
CSE Asia
Thailand
ADMIN1
76
2006
2008
82
[84]
CSE Asia
Timor-Leste
ADMIN1
13
2008
2008
34
[85]
CSE Asia
Vanuatu
ADMIN1
6
2003
2007
45
[86]
CSE Asia
Viet Nam
ADMIN2
671
2005
2008
22
[87]
15
ASR
Source
Table A1.2. Cities cited as being malaria-free by the sources consulted [10,11]. Defined risk refers to the malaria risk categories defined by the PfAPI layer and biological masks; note that urban extents often cover more than one category. Modified risk refers to the new malaria risk categories assigned according to the rules described in the text. Cities where the defined risk was “free” were not affected by these rules. Country
City
Defined risk
Modified risk*
Bangladesh
Dhaka
Free
NA
Bolivia
La Paz
Free
NA
Botswana
Gaborone
Free
NA
Cambodia
Phnom Penh
Free, unstable
Free
Colombia
Bogota
Free, unstable
Free
Colombia
Cartagena
Free, unstable
Free
Ecuador
Guayaquil
Unstable, stable
Free
Ecuador
Quito
Free
NA
Eritrea
Asmara
Stable
Free
Ethiopia
Addis Ababa
Stable, free
Free
French Guiana
Cayenne
Free
NA
Guatemala
Antigua
Free
NA
Guatemala
Guatemala
Free
NA
Honduras
San Pedro Sula
Unstable
Free
Honduras
Tegucigalpa
Unstable, free
Free
India
Bangalore
Stable
Unstable
India
Kolkata
Unstable, stable
Free, unstable
India
Mumbai
Stable, unstable
Unstable, free
India
Nagpur
Stable
Unstable
India
Nasik
Unstable
Free
India
Pune
Unstable
Free
Indonesia
Jakarta
Free
NA
Kenya
Nairobi
Stable
Free
Laos
Vientiane
Free
NA
Myanmar
Mandalay
Free, stable, unstable
Free, unstable
Myanmar
Yangon
Unstable
Free
Nepal
Kathmandu
Free
NA
Nicaragua
Managua
Unstable
Free
Panama
Panama
Unstable
Free
Peru
Cuzco
Free
NA
Saudi Arabia
Jeddah
Unstable
Free
Saudi Arabia
Mecca
Unstable
Free
Saudi Arabia
Medina
Unstable
Free
16
Saudi Arabia
Riyadh
Free
NA
Saudi Arabia
Ta'if
Unstable
Free
Suriname
Paramaribo
Free
NA
Thailand
Bangkok
Free, unstable
Free
Thailand
Chiang Mai
Stable
Free
Thailand
Chiang Rai
Unstable
Free
Thailand
Koh Phangan
Stable
Free
Thailand
Koh Samui
Stable
Free
Thailand
Pattaya
Unstable
Free
Viet Nam
Can Tho
Free, unstable
Free
Viet Nam
Da Nang
Unstable
Free
Viet Nam
Haiphong
Free
NA
Viet Nam
Hanoi
Free, unstable
Free
Viet Nam
Ho Chi Minh City
Unstable
Free
Viet Nam
Hue
Free, unstable
Free
Viet Nam
Nha Trang
Free, unstable
Free
Viet Nam
Qui Nhon
Unstable
Free
Yemen
Sana’a
Unstable
Free
*NA = not applicable
17
Table A1.3. Administrative areas defined as being malaria free by international travel and health guidelines. Country
Administrative areas/sub-national territories
Ecuador
Galapagos
French Guiana
Devil's Island
Mauritania
Adrar, Dakhlet-Nouadhibou, Inchiri and Tiris-Zemmour regions Aklan, Albay, Benguet, Bilaran, Bohol, Camiguin, Capiz, Catanduanes, Cavite,
Philippines
Cebu, Guimaras, Iloilo, Northern Leyte, Southern Leyte, Marinduque, Masbate, Eastern Samar, Northern Samar, Western Samar, Sequijor, Sorsogon, Surigao Del Norte and metropolitan Manila
Sri Lanka
Colombo, Galle, Gampaha, Kalutara, Matara, and Nuwara Eliya
Venezuela
Margarita Island (Nueva Esparta)
18
Figure A1.1. Environmental suitability for transmission of P. falciparum as defined by temperature. Areas shaded grey are those in which no windows exist across an average year in which the annual temperature regime is likely to support the presence of infectious vectors.
19
Figure A1.2. Environmental suitability for transmission of P. falciparum as defined by extreme aridity. Areas shaded grey are those classified as bare areas by the GlobCover land cover product, interpreted as lacking sufficient moisture to support populations of Anopheles necessary for transmission.
20
PfMECs Area: 62,107,465 PAR: 4,987
PfAPI data layer
Area excluded: 21,829,581 PAR excluded: 2,203
Duration of sporogony layer
Area excluded: 756,855 PAR excluded: 67
Aridity layer
Area excluded: 661,721 PAR excluded: 33
Medical intelligence layer
Area excluded: 606,035 PAR excluded: 110
Pf Limits map Area: 38,253,271 PAR: 2,574
Figure A1.3. Flow chart of the various exclusion layers used to derive the final map. Area (expressed in km2) and population at risk (PAR; expressed in millions) excluded are shown at each step to illustrate how these were reduced progressively.
21
22
Figure A1.4. Map sequence illustrating the different exclusion layers applied. A = all regions of the 85 P. falciparum endemic countries; B = downgrading or exclusion of risk informed by annual parasite incidence data; C = additional exclusion of risk informed by the biological temperature mask; D = additional downgrading or exclusion of risk informed by the aridity mask; E = the final limits definition after additional downgrading or exclusion of risk informed by medical intelligence and international travel and health guidelines. Stable transmission is shown in red, unstable transmission in pink and malaria free areas in grey.
23
Figure A1.5. Differences in the definition of risk areas between the 2007 and 2010 iteration of the P. falciparum spatial limits map. Light grey pixels indicate no change in defined risk. Blue pixels show negative change by one class (light blue pixels; stable to unstable transmission or unstable to malaria free) or two classes (darker blue pixels from stable transmission to malaria free). Red pixels indicate positive changes by one class (light red; malaria free to unstable transmission or from unstable to stable) or two classes (dark red from malaria free to stable transmission). Note that these differences derive mainly from improvements both in the input PfAPI data and the underlying methodology used to further constrain risk (i.e. biological masks) rather than local epidemiological changes.
24
Additional file A2 - Updates to the Plasmodium falciparum parasite rate survey database A2.1 Overview
Our rationale for the choice of Plasmodium falciparum parasite rate (PfPR) as the most appropriate available metric for measuring endemicity has been outlined previously [1-3], and is driven primarily by its global ubiquity [4] and sensitivity across a wide range of the P. falciparum malaria transmission spectrum [5]. The process of identifying, assembling and geo-locating community-based survey estimates of parasite prevalence undertaken since 1985 has been ongoing within MAP since 2005 [2] and was completed on 1 June 2010 for the current iteration. Up to that date, a total of 23,612 cross-sectional survey estimates of PfPR had been identified from 80 of the 85 PfMECs, of which 22,212 passed strict data fidelity tests for inclusion into the global database. This represented an increase of 180% over the 7,953 data used for the 2007 mapping iteration [3]. The five most data rich countries were Indonesia (n=2,516), Kenya (n=2,461), Tanzania (n=2,065), Sudan (n=1,907) and Somalia (n=1,656). Of the additional 14,259 data globally, 5,259 post-dated 2007. Other additional data were either newly assembled in the intervening period or were newly included for modelling as a result of our modified exclusion or aggregation rules. This document describes the PfPR data assembly, the auditing steps performed on the database, the exclusion rules applied prior to modelling and some key features of the PfPR data set used in the 2010 iteration of the global P. falciparum endemicity maps described in this paper. It also describes how the data were split into regions to facilitate modelling.
A2.2 Assembling the PfPR data
Revised Inclusion Criteria Table A2.1 lists the original and revised inclusion criteria of the MAP PfPR data. First, the original inclusion criterion of a minimum of 50 individuals surveyed was removed because the models adjust for sample size. Removing this 'minimum sample size' rule allowed the inclusion of 3,205 previously excluded records. Second, the minimum 36 month duration interval permitted between surveys conducted at the same location (spatial duplicates) was relaxed to six months, or three months where authors were explicit about having sampled different individuals between surveys or transmission seasons. This allowed the inclusion of 287 previously excluded surveys and enhanced the ability of the model to infer seasonal and secular changes.
1
Search Strategies Data searches aimed to retrieve data from published and unpublished sources and have been ongoing since March 2005 [2]. The published scientific literature was scanned periodically for data through subscription to malaria newsletters (mainly Malaria World newsletters (http://www.malaria-world.com/) and the Environmental Health at USAID malaria bulletins (http://www.ehproject.org/)). This was complemented by periodic data searches in online reference archives (mainly PubMed (http://www.ncbi.nlm.nih.gov/sites/entrez), ISI Web of Knowledge (http://wok.mimas.ac.uk) and Scopus (http://www.scopus.com)) to ensure that all relevant publications were captured. Keywords used in these searches were “malaria” and [Country Name]. Data from unpublished sources were obtained through active, direct communication with malaria specialists. Full acknowledgement of these interactions and data provision is provided on the MAP website [6].
Data Abstraction and Entry Data were abstracted from their original sources. Data owners and authors were contacted for clarification, missing information and if data disaggregation in space or time was desired. Data entry of checked records was undertaken into a Microsoft Access (Microsoft, 2006) custom database [2]. This database was subsequently migrated to an open source PostgreSQL 8.3 database (PostgreSQL Global Development Group, 2009) running on a Unix platform.
Geo-positioning Data Data geo-positioning was a particularly time-consuming task during data entry. The same guidelines described previously were used here [2]. In brief, data were classified according to the area for which they were representative: points (corresponding to an area ≤10 km2), wideareas (>10 and ≤25 km2), small polygons (>25 and ≤100 km2) or large polygons (>100 km2). Attempts were made to disaggregate polygon data into points or wide-areas with authors. Records that were judged to be geo-positioned less precisely were tagged as either a “good” (inaccuracy 5 km). Various digital resources were used to geo-position the data, amongst which the most useful were Microsoft Encarta Encyclopaedia (Microsoft, 2004) and Google Earth (Google, 2009). Importantly, the increasing provision of GPS readings accompanying new surveys (43% compared to 25% in the previous iteration) decreased the burden of geo-positioning and improved the positional accuracy of the more contemporary data. After these geo-positioning and follow up activities only 3.5% of records could not be geo-positioned.
2
A2.3 Database fidelity checks The entire database was first checked with a series of simple range-check constraint queries to identify potential errors that could have occurred during data entry. These queries addressed all data fields relevant to modelling for missing or inconsistent information. The fields checked included those describing the study area (area type, geographical coordinates, and urban or rural author definitions) and those providing specific information about the survey (number of cross-sectional surveys used to estimate PfPR, month and year of start and end of the survey, age range of study population, number examined and positive for P. falciparum, and diagnostic method utilised). The second objective was to check that survey sites were located precisely with respect to the master raster grid templates in which the endemicity models were developed (see section A4.3 in Additional file A4). The locations therefore needed to be on grid squares identified as land and within the border of the country in which the survey was conducted. All survey locations were intersected with the relevant grids and erroneous locations identified and corrected manually, showing an average displacement of 36 months
>3-6 months
Numerator/denominator
Required
No change
Age groups sampled
Children preferred (Africa)
No change
Spatial coverage
Points/wide-areas preferred
No change
Examination method
Microscopy preferred over RDT
No change
12
Table A2.2. The PfPR data exclusions by region. America
Africa+
CSE Asia
Total
Countries with PfPR survey data†
14
49
22
85
Total records in completed database
541
16,297
6,774
23,612
Large polygons
5
108
63
176
Small polygons
8
42
50
100
Unable to geo-position
79
449
299
827
Imprecise geographical coordinates
0
4
19
23
Temporally aggregated surveys
4
49
59
112
Surveys with missing month
8
39
115
162
437
15,606
6,169
22,212
Exclusions
Total records for input data set
†Those countries from which PfPR data were available are listed alphabetically by region: Americas (Bolivia, Brazil, Colombia, Costa
Rica, Ecuador, French Guiana, Guatemala, Haiti, Honduras, Mexico, Nicaragua, Peru, Suriname, Venezuela); Africa+ (Angola, Benin, Botswana, Burkina Faso, Burundi, Cameroon, Cape Verde, Central African Republic, Chad, Comoros, Congo, Côte d'Ivoire, Democratic Republic of the Congo, Djibouti, Equatorial Guinea, Eritrea, Ethiopia, Gabon, The Gambia, Ghana, Guinea, GuineaBissau, Kenya, Liberia, Madagascar, Malawi, Mali, Mauritania, Mayotte, Morocco, Mozambique, Namibia, Niger, Nigeria, Rwanda, São Tomé and Principe, Saudi Arabia, Senegal, Sierra Leone, Somalia, South Africa, Sudan, Swaziland, Togo, Uganda, United Republic of Tanzania, Yemen, Zambia, Zimbabwe) and CSE Asia (Afghanistan, Bangladesh, Cambodia, China, India, Indonesia, Iraq, Lao People's Democratic Republic, Malaysia, Myanmar, Nepal, Pakistan, Papua New Guinea, Philippines, Solomon Islands, Sri Lanka, Tajikistan, Thailand, Timor-Leste, Turkey, Vanuatu, Viet Nam).
13
Table A2.3. A summary of the most important aspects of the PfPR data by geographical region. The figures presented are after the exclusions shown in Table A2.2. America
Africa+
CSE Asia
Total
437
15606
6169
22737
Peer reviewed sources
277
3522
1171
4970
Unpublished work†
56
6751
3990
11094
Reports††
104
5333
1008
6673
Personal communication
79
1376
807
2262
GPS
116
7594
1822
9955
Encarta
115
2056
552
2731
Combination
80
1504
2061
3649
Other digital gazetteers
32
2966
293
3381
Paper source
14
56
9
79
Map
1
54
625
680
1985-1989
49
1011
212
1272
1990-1994
42
1270
475
1787
1995-1999
120
1074
686
1880
2000-2004
165
3527
1555
5247
2005-2010
61
8724
3241
12551
10 and 15 and 20 Diagnostic method
Denominator No denominator
8
1195
76
1285
1-49
167
6250
1343
7893
50-100
92
4382
1342
6156
101-500
135
3348
2464
5993
35 74 (33-167)
431 53 (28-100)
944 113 (55-277)
1410 69 (34-124)
>500 Median (IQR)
Raw data from unpublished studies obtained through personal communication.
†
Ministry of Health reports, theses and other grey literature sources.
††
14
Table A2.4. Specific RDTs used in the PfPR surveys recorded. RDT name
Number of records
Target species* Pf, Pan
Azog MFV 124R
102
CareStart Malaria
28
FalciVax
399
Pf, Pv
First Response Ag Pf/Pv
299
Pf, Pan
ICT Malaria Pf
448
Pf
ICT Malaria Pf/Pv
82
Pf, Pan
OptiMAL
558
Pf, Pan
OptiMAL-IT
64
Pf, Pan
‡
ParaCheck Pf
1,172
Pf
ParaCheck Pf (Cassette)
1,506
Pf
ParaCheck Pf (Dipstick)
167
Pf
ParaHIT-f
444
Pf
Rapid Uni-Gold
120
Pf
1,122
NA
Not specified
*Pf = P. falciparum; Pv = P. vivax, Pan = Plasmodium species, NA = not applicable. ‡The specific type of CareStart Malaria test was not provided
15
Table A2.5. The training set used for developing the age-standardisation models. Country
Area
Angola
Ave Maria & Luvo
Angola Benin Cambodia
Rattanak Kiri
Congo
Linzolo
Djibouti Eritrea
Date
Surveys
11/2005
1
Tomboco
4/2006
Cotonou
6/1989-4/1990
Sample size
Technique
PfPR 55.47
Citation
1,015
RDT
[14]
1
405
RDT
34.81
[14]
3
1,248
Microscopy
36.78
[15]
2001
1
5,533
RDT
30.13
[16]
11/1980-5/1985
26
1,441
Microscopy
76.2
[17]
National
12/2008
1
6,707
RDT
0.63
[18]
National
9/2000-11/2000
1
12,661
RDT
2.04
[19]
Ethiopia
Amhara
12/2006-1/2007
1
7,745
Microscopy
2.48
[20]
Ethiopia
Oromia & SNNPR
1/2007-2/2007
1
3,856
Microscopy
2.18
[21]
Ghana
Navrongo
5/2001-11/2001
2
6,985
Microscopy
44.91
[22]
India
Orissa
1998-2000
8
12,107
Microscopy
10.55
[23,24]
Indonesia
Legundi
7/2000-3/2004
4
8,781
Microscopy
10.31
[25]
Indonesia
Papua
11/2007
1
360
Microscopy
34.72
[26]
Indonesia
Purworejo
5/2000-7/2002
3
3,975
Microscopy
12.53
[25]
Indonesia
Sukabumi
6/2003-1/2004
2
10,260
Microscopy
3.70
[25]
4/2008
1
1,205
Microscopy
33.36
[27]
7/1999-6/2001
6
4,399
Microscopy
32.98
[28,29]
Kenya
Assembo Bay
Kenya
Chonyi
Kenya
Gucha
7/2000
1
1,770
RDT
7.80
[30]
Kenya
Kericho
6/1999-3/2002
1
2,209
Microscopy
10.91
[31]
50.11
[32]
12
[33]
Kenya
Kilifi
1993
1
2,347
Microscopy
Kenya
Kisii
5/2000
1
2,016
RDT
Kenya
Ngerenya
7/1999-6/2001
6
4,440
Microscopy
22.73
[28,29]
Kenya
Suba
11/2001-5/2002
1
1,221
Microscopy
37.84
[34]
Kenya/Uganda
Pokot territory
6/2006-9/2006
1
337
RDT
13.65
[35]
Mozambique
Manhica
10/1997-8/1999
2
2,749
Microscopy
12.99
[36]
Namibia
4/2009-6/2009
1
4,572
RDT
2.76
[37]
11/2007-12/2007
1
1,102
RDT
43.19
[38]
Nigeria
National 4 Local Government Areas 4 Local Government Areas
11/2008-12/2008
1
1,433
RDT
45.99
[38]
Microscopy
Nigeria
Papua New Guinea
Wosera
7/1990-7/1992
7
10,001
39.59
[39]
Rwanda
9 Provinces
10/2007-11/2007
1
3,593
RDT
0.95
[40]
Rwanda Sao Tome & Principe
9 Provinces
10/2008-11/2008
1
3,572
RDT
1.12
[40]
Riboque
1/1998-3/1998
1
493
39.55
[41]
Senegal
Dielmo
6/1990-9/1990
1
8,539
Microscopy
71.95
[42]
Senegal
Ndiop
1993-1994
24
3,352
Microscopy
32.46
[43]
Somalia
Central
1/2005-2/2005
1
4,409
RDT
4.99
[44]
Somalia
North East
5/2005-6/2005
1
2,533
RDT
5.96
[44]
Microscopy
Somalia
Puntland
4/2009
1
1,455
RDT
2.06
[45]
Somalia
South
1/2005-2/2005
1
4,686
RDT
11.93
[44]
Somalia
South/Central
1/2007-6/2007
4
10,408
RDT
15.47
[46]
Sudan
10 States
10/2005
1
9,880
5.36
[47]
Microscopy
Sudan
North
10/2009-11/2009
1
22,146
Tanzania
Kilombero
5/2001-8/2001
1
1,849
Microscopy
RDT
2.19
[48]
19.15
[49]
Tanzania
Lower Moshi
4/2005-12/2005
1
2,508
Tanzania
Michenga
7/1989-7/1991
12
4,830
Microscopy
1.83
[50]
Microscopy
75.78
[51]
Tanzania
Namawala
7/1989-7/1991
12
3,901
Microscopy
77.62
[51]
Tanzania
Rufiji
5/2001-8/2001
1
3,166
Microscopy
25.71
[49]
Tanzania
Ulanga
5/2001-8/2001
1
1,246
Microscopy
19.02
[49]
16
Country
Area
Thailand
Tak Province
Uganda
Kabale/Rukungiri
Uganda
Mulanda
Vanuatu
16 Islands
Vanuatu
Sanma
Vanuatu
Sanma & Shefa
Zambia
South
Date
Surveys
Sample size
9/1998-10/2002
3
13,983
Technique Microscopy/ RDT
PfPR 2.3
Citation [52,53]
7/2007-8/2007
1
2,100
RDT
9.62
[54]
10/2008-12/2008
1
1,863
Microscopy
38.49
[55]
1988-1992
4
13,070
Microscopy
5.49
[56]
2/2005-5/2005
1
2,743
Microscopy
2.04
[57]
3/2002
1
2,351
Microscopy
16.93
[57]
4/2005-6/2005
1
1,254
Microscopy
4.78
[58]
17
Figure A2.1. Sequence of data exclusion rules for the formulation of a refined global PfPR input data set for modelling. For each stage of exclusion the number of records excluded are shown in parentheses.
18
Figure A2.2. Cumulative data record count (y-axis) in relation to year of survey (x-axis). A lighter shade is used after the year 2000 to highlight the predominance of more contemporary data.
19
Figure A2.3. Data records used in the first (orange; n=7,953) and current (orange and green; n=22,212) iteration of the endemicity models. Dashed lines separate the three regions considered (America, Africa+ and CSE Asia). The spatial limits of P. falciparum transmission are shown in shades of grey.
20
Figure A2.4. Percentage of surveys using RDTs rather than microscopy by year for each region (A, America; B, Africa+; C, CSE Asia). Vertical bars are 95% binomial confidence intervals.
21
Figure A2.5. Median (red horizontal bars) and IQR (black horizontal bars) PfPR2-10 by year with smooth fit line (continuous thick black) generated by a loess smoother. Also shown is the cumulative number of data available through time (dashed line).
22
Figure A2.6. The division of the 85 PfMECs into eight global regions for separate handling in the geostatistical modelling framework.
23
Additional file A3 - Model-based geostatistical procedures In the 2007 iteration [1] we described an approach to predicting a continuous surface of P. falciparum endemicity within the defined geographic limits of stable transmission, centred on a model-based geostatistical framework [2] with model fitting achieved via Bayesian inference and Markov chain Monte Carlo (MCMC). We based this updated 2010 version on a refined version of the same underlying architecture. Again, the aim was to generate both a continuous estimated surface of endemicity and a corresponding categorical surface classifying the stable endemic world into classes of risk. The classification scheme matched that used in the 2007 version [1], with areas where PfPR2-10 ≤5%, PfPR2-10 >5-5%-5%-5% - 5% to
