A Rift Valley fever atlas for Africa

transcript

Archie C.A. Clements a,b,*, Dirk U. Pfeiffer a,Vincent Martin c, M. Joachim Otte d

a Epidemiology Division, Department of Veterinary Clinical Sciences, Royal Veterinary College,

University of London, Hatfield, Hertfordshire, United Kingdomb Division of Epidemiology and Social Medicine, School of Population Health,

University of Queensland, Herston, Queensland, Australiac Animal Health Service, Food and Agriculture Organisation, Rome, Italy

d Livestock Information and Policy Branch, Food and Agriculture Organisation, Rome, Italy

Received 18 September 2006; received in revised form 8 May 2007; accepted 9 May 2007

Abstract

Rift Valley fever (RVF) epidemics have serious consequences for human and animal health and the

livestock trade. Recent epidemics have occurred in previously unaffected regions, increasing concerns

that the geographical range of RVF will continue to expand. We conducted an extensive, systematic

review of the literature to obtain serological data for RVF in Africa, collected between 1970 and 2000

from human, livestock and wild ungulate populations. Aims were to calculate sub-national estimates of

RVF infection prevalence and to define areas where no information was available. We presented the data

(aggregated at the first administrative level of countries) using a geographical information system. Data

from 71 publications were used to build a spatially explicit Bayesian logistic-regression model, with

spatial and non-spatial random effects, allowing us to identify clusters of high and low RVF seropre-

valence, and fixed effects that described the disparate nature of the survey subjects and methods.

Significant high-prevalence clusters encompassed areas that had experienced epidemics during the late

20th century and significant low-prevalence clusters were located in contiguous areas of Western and

Central Africa.

Keywords: Atlas; Mapping; Geographical information systems; Rift Valley fever; Epidemiology; Spatial analysis;

Seroprevalence; Systematic literature review

www.elsevier.com/locate/prevetmed

Preventive Veterinary Medicine 82 (2007) 72–82

* Corresponding author at: Division of Epidemiology and Social Medicine, School of Population Health, University of

Queensland, Herston Road, Herston, Queensland 4006, Australia. Tel.: +61 7 32405952.

E-mail address: a.clements@uq.edu.au (A.C.A. Clements).

doi:10.1016/j.prevetmed.2007.05.006

1. Introduction

Rift Valley fever (RVF) is a disease of increasing global importance, affecting a wide range of

animal species, including domestic livestock and humans (Lefevre, 1997). RVF epidemics are

characterized by abortion in pregnant animals and mortality (usually highest in young stock)

(Eisa et al., 1977; Woods et al., 2002). Additional economic effects arise due to restrictions on

animal movement and the trade of animals and animal products (World Health Organisation,

2000; Anyamba et al., 2001). Epidemics have also resulted in high morbidity and mortality in the

human population, such as those in South Africa (1974–1975), Egypt (1977–1978), Southern

Mauritania (1987) and Eastern Africa (1997–1998).

The aetiological agent is Rift Valley fever virus (RVFV), a Phlebovirus from the family

Bunyaviridae, which is potentially transmitted by many different species of insect vectors that

have a wide global distribution (Gubler, 2002). Epidemics of RVF were, until the late 20th

century, restricted to sub-Saharan Africa (SAA). The 1977 epidemic in Egypt was the first

reported outside SSA and a recent epidemic in the Arabian Peninsula (2000), which affected both

the animal and human populations, was the first reported outside the African continent. The wide

ranges of possible host and vector species and the increasing geographical range of RVF activity

have led to fears that RVF epidemics will continue to occur in previously unaffected regions of

the world.

Considering the increasing global importance and expanding geographical range of RVF

epidemics, it is timely to review the epidemiological information available for RVF. In

particular, a visual representation of the continental distribution of RVF would be useful to

describe the large-scale geographical patterns of RVF risk, facilitate optimal allocation of

resources for surveillance and control and identify geographical areas characterized by a paucity

or absence of information (Openshaw, 1996; Michael and Bundy, 1997; Lawson et al., 1999;

Kitron, 2000).

The value of disease atlases as a means of visually revealing spatial patterns of disease

occurrence has been emphasised (Lawson and Williams, 2001) and the concept of a disease atlas

has been applied in a number of global, continental and regional settings. Such atlases have been

developed for human helminth infections (Michael and Bundy, 1997; Brooker et al., 2000),

schistosomiasis (Brooker et al., 2000) and malaria (Singhasivanon, 1999; Snow et al., 1999).

Geographical heterogeneity displayed in maps of aggregated disease data might be misleading

because raw measures might reflect differences in size of underlying populations, with extreme

values being more likely in areas with small populations or sample sizes (Clayton and Kaldor,

1987; Langford, 1994). One approach that allows for calculation of prevalence estimates that are

robust to these sample-size issues (and for incorporating the spatial correlation structure of the

data) is Bayesian smoothing. With a Bayesian approach, global or local (neighbourhood) risk is

taken as prior information and local estimates are smoothed towards the global average (where

spatially unstructured heterogeneity predominates) or to the neighbourhood average (where

spatially structured heterogeneity predominates).

Animal-disease reporting to the Office International des Epizooties and mapping of these data

(Clements et al., 2002) have been undertaken at the national level. In the current study, we

focused specifically on RVF and aimed to use supplementary data from the published literature to

obtain higher-resolution sub-national estimates of infection risk (as defined by seroprevalence)

across the African continent. We presented raw and smoothed data in the context of a continental

RVF atlas for Africa. We also aimed to identify areas characterized by little or no information that

warrant greater allocation of resources for surveillance.

A.C.A. Clements et al. / Preventive Veterinary Medicine 82 (2007) 72–82 73

2. Methods

2.1. Systematic review of the literature

Three internet-based databases were used to search for publications: CAB abstracts

(www.cabdirect.org), PubMed (www.ncbi.nlm.nih.gov/entrez/query.fcgi) and ISI web of

science (www.wok.mimas.ac.uk). Our search criteria were selected to extract publications

containing information on seroprevalence of RVF for all species in all countries since 1970. The

Boolean search terms were as follows: (‘‘Rift Valley Fever’’ OR RVF) AND Sero* AND

Prevalence (where Sero* indicates words with the root ‘‘sero’’, such as ‘‘serology’’ and

‘‘serological’’).

We created a list of all publications that, based on the abstracts, appeared to contain

serological data for RVF. Many publications were available on-line via university electronic-

journal subscriptions. Those that were not available on-line were obtained from the

British Library or the libraries of the Royal Veterinary College and the London School of

Hygiene and Tropical Medicine. We excluded six publications that were not available from

these locations.

An electronic database was developed using Microsoft Access. The same individual read each

of the publications and recorded (if reported): the year(s) of the study, the country and the first-

level administrative units (i.e. the largest division of a country; usually the state or province)

where the study was conducted, the sample design (non-random, simple random, stratified

random, multi-stage cluster design, abattoir-based, single herd, sentinel herds, not reported), the

diagnostic test(s) used, the number of individuals of each species tested and the number of

individuals found to be seropositive.

Where it was identified that two or more publications presented the same data, only one of

those publications was used in the study. To ensure that all the data were of an acceptable quality

(and, as far as possible, the results could be standardised), exclusion criteria were used to define a

subset for subsequent analysis. We only included serological results based on diagnostic tests

assessing the presence of IgG antibodies for RVF; we excluded all serosurveys conducted on

individuals with clinical signs that might have been indicative of RVF or surveys conducted

during or immediately following a known or suspected epidemic of RVF. Any survey that had a

design that was deliberately biased towards detecting RVFV infection was excluded (e.g.,

surveys of febrile animals or surveys of previously affected farms). We could not exclude all

surveys where a randomisation procedure was not used for selecting the subjects nor where a

detailed explanation of the selection criteria was omitted because this would have excluded most

studies.

2.2. Visual analysis

The first-level administrative unit boundaries were obtained (for all countries from which we

had serological data) in a format compatible with the geographical information system (GIS)

software ArcMap version 8.0 (ESRI, Redlands, CA). The serological data (aggregated by first-

level administrative units) were extracted from the database and imported into ArcMap 8.0 where

they were joined to the boundary map. Maps of the raw data were examined visually to

investigate spatial patterns in RVF seroprevalence. Separate maps were constructed for humans

and ungulates (cattle, sheep, goats, camels, water buffalo, horses, donkeys and wild ungulates)

for the decades 1970–1979, 1980–1989 and 1990–1999.

A.C.A. Clements et al. / Preventive Veterinary Medicine 82 (2007) 72–8274

2.3. Bayesian spatial analysis

We constructed a mixed-effects logistic-regression model in a Bayesian framework using

WinBUGS 1.4 (MRC Biostatistics Unit, Cambridge, UK). The model was of the form:

yi� binomialð pi; niÞ;

logitð piÞ¼aþXr

b jxi; j þ ui þ vi; vi� normalð0; d2vÞ; ðuiju j; i 6¼ j; d2

uÞ� normalðui; d2i Þ;

where ui ¼ ð1=P

jci; jÞP

ju jci; j; d2i ¼ a2

jci; j; pi, the outcome variable, is the proportion of

serologically positive individuals, yi is the number of test-positive individuals, ni is the total

number of individuals tested in each administrative unit i, a is the intercept,Pr

j¼1 b jxi; j is a

vector of r covariates measured in each administrative unit i multiplied by coefficients bj, ui is a

spatial random effect (SRE) with a conditional autoregressive (CAR) prior; vi is a non-spatial

random effect (NSRE) (Besag et al., 1991) and ci,j equals 1.0 if a pair of administrative units was

contiguous (i.e. shared a border or touched at a corner of their borders) and equals 0.0 otherwise.

We specified non-informative gamma priors for the precision (the inverse of variance) of the SRE

and NSRE (i.e. 1=d2u; 1=d

2v). We also specified non-informative normal and gamma priors for the

mean and precision respectively of the intercept and fixed effect coefficients. Fixed effects

included the decade during which the data were collected, the species surveyed, the diagnostic

test used and the sample selection method. A separate category was created for administrative

units that had serosurveys conducted over multiple decades, and sample selection methods were

regrouped into not randomised, randomised, not reported and other.

Bayesian modeling is an iterative process involving regular examination of the posterior

distributions of the model parameters for evidence of convergence (Spiegelhalter et al., 2003).

The software applies Gibbs sampling to derive the posterior distributions. Three series of each

model were run consecutively with different starting values for the random variables in the model

(the intercept, coefficients and random effects) to observe and test convergence at the same,

stable distributions.

We allowed a burn-in of 1000 iterations, followed by intervals of 10,000 iterations between

which values of the random variables were analysed using diagnostic tests for convergence

(including visual examination of history and density plots of the stored values from each iteration

and application of the Gelman and Rubin statistic) (Brooks and Gelman, 1998). Convergence of

all monitored variables took �20,000 iterations. Because of autocorrelation within each of the

three series (particularly for the intercept and precision of the random effects), only every 20th

subsequent iteration was stored (otherwise, failure to reduce the effects of autocorrelation would

have resulted in underestimation of the variance of the posterior distributions). We then ran

sufficient iterations to give a total of 20,000 stored values from the posterior distribution of each

variable. Descriptive statistics for the posterior distributions of the model outputs were calculated

and analysed.

The posterior distributions of the SRE in each geographical unit were analysed to identify

‘‘significant’’ high and low-seroprevalence clusters. We considered administrative units where

the 5th percentile of the posterior distribution was above zero to be located in significant high-

seroprevalence clusters and units where the 95th percentile of the posterior distribution was

below zero to be located in significant low-seroprevalence clusters. A map of these delineated

clusters was created in ArcMap 8.0.

3. Results

3.1. The data

In total, 149 publications were obtained and data from 106 of these publications were recorded

in the electronic database. These data came from 31 African countries and 177 first-level

administrative units from these African countries. The database contained a total of 661 records,

where each record was a single entry for each study/country/administrative unit/year

combination. After applying the exclusion criteria, the analysis subset contained data from

71 publications, 28 African countries, 156 first-level administrative units of African countries

and 480 records. The raw seroprevalence of RVF in humans and ungulates, aggregated by decade,

is presented in Table 1.

3.2. Visual analysis of raw seroprevalence data

Overall, more data were available over a wider geographical area for humans and

ungulates during the 1980s than for the 1970s or 1990s. For humans, high-seroprevalence

was reported in Egypt and South Africa in the 1970s (Fig. 1a); and the Sahelian zone,

plus parts of Nigeria and Botswana in the 1980s (Fig. 1b). Data were only obtained from

Senegal and the Central African Republic in the 1990s (Fig. 1c). For ungulates, high-

seroprevalence was reported in parts of Egypt and South Africa in the 1970s (Fig. 2a); Egypt,

Sudan, the Sahel and parts of Nigeria and Cameroon in the 1980s (Fig. 2b); and parts of

Zambia, Madagascar and Benin in the 1990s (Fig. 2c). In all figures, white areas are those

where data were not available, reported or included following imposition of our exclusion

criteria.

3.3. Bayesian smoothed prevalence estimates

In the Bayesian model (Table 2), spatially structured residual variation predominated over

spatially unstructured residual variation (indicated by a higher value for the variance of the SRE

compared to the NSRE). This indicated that there was stronger evidence of a clustered spatial

pattern as opposed to a random spatial pattern in RVF seroprevalence, after accounting for the

fixed effects. Most fixed effects were significant using a 95% Bayesian credible interval that

excluded 1.0 as the significance criterion.

Table 1

Reported seroprevalence of Rift Valley fever in humans and ungulates (cattle, sheep, goats, camels, water buffalo, horses,

donkeys and wild ungulates), aggregated by decades beginning 1970, 1980 and 1990, in African countries

Species Decade beginning (year) Number serologically

positive/number tested

Seroprevalence (%)

Humans 1970 574/13,210 4.3

1980 757/21,599 3.5

1990 592/8,661 6.8

Ungulates 1970 1,490/15,139 9.8

1980 3,248/24,331 13.3

1990 1,939/30,880 6.3

Examination of the Bayesian probability map of the SRE measured in each administrative unit

(Fig. 3) showed significant high-prevalence clusters encompassing areas of Egypt, Sudan,

Mauritania, Senegal, South Africa and Madagascar and a single administrative unit in the Central

African Republic and significant low-prevalence clusters encompassing areas of Niger, Nigeria,

Benin, Cameroon, Gabon and Angola.

Fig. 1. Raw seroprevalence of Rift Valley fever in humans, aggregated by first-level administrative units of African

4. Discussion

We believe that we have presented the first continental maps of RVF seroprevalence in Africa.

In general, high-seroprevalence was reported in the arid or savannah regions of the continent and

Fig. 1. (Continued ).

Table 2

The Bayesian logistic-regression model for spatially aggregated Rift Valley fever seroprevalence in Africa

Variable Coefficient

(posterior

median)

Coefficient

(95% Bayesian

credible

interval limits)

Odds ratio

(posterior

median)

Odds ratio

(95% Bayesian

credible

interval limits)

Intercept �4.0 �5.1, �2.9

Decade: 1980s 2.9 1.5, 4.1 17.5 4.5, 62.3

Decade: 1990s 2.5 0.9, 4.2 12.4 2.4, 63.5

Decade: multiple 2.0 0.6, 3.2 7.5 1.9, 25.4

Sample selection: not reported �0.7 �1.6, 0.3 0.5 0.2, 1.4

Sample selection: randomised �0.8 �2.0, 0.3 0.4 0.1, 1.3

Sample selection: other �0.3 �1.4, 0.6 0.7 0.3, 1.8

Test: ELISA �0.4 �1.5, 0.7 0.7 0.2, 2.1

Test: IFAT �1.6 �2.7, �0.5 0.2 0.1, 0.6

Test: other �0.7 �1.6, 0.2 0.5 0.2, 1.2

Species: humans only �1.8 �2.6, �0.9 0.2 0.1, 0.4

Species: humans and ungulates 0.0 �0.6, 0.7 1.0 0.5, 2.0

Variance: spatial RE 2.0 1.0, 3.2 – –

Variance: non-spatial RE 0.9 0.2, 1.8 – –

The reference variables (odds ratio = 1) were: decade–1970s; sample selection—not randomised; test—haemagglutina-

tion inhibition test; species—ungulates only. ELISA is the enzyme-linked immuno-sorbent assay, IFAT is the indirect

fluorescent-antibody test and RE is random effect.

low-seroprevalence was reported in the high-rainfall tropical regions. This is consistent with

previous studies that found a relationship between the timing and location of RVF epidemics and

climatic indicators such as normalised difference vegetation index (NDVI) or NDVI anomalies

(indicators of rainfall), the Southern Oscillation Index and sea-surface temperatures in the Pacific

and Indian Oceans (which measure the El Nino Southern Oscillation (ENSO) climatic

phenomenon) and accumulation of ground-surface water (Linthicum et al., 1987, 1991, 1999;

Pope et al., 1992; Anyamba et al., 2001, 2002).

Fig. 2. Raw seroprevalence of Rift Valley fever in ungulates, aggregated by first-level administrative units of African

Bayesian modeling enabled us to differentiate spatially structured and unstructured variation

in RVF seroprevalence while accounting for important covariates, and to examine uncertainties

surrounding our model outputs (leading to the creation of a Bayesian probability map of RVF

clusters). The significant high-prevalence clusters encompassed areas that had experienced

epidemics of RVF during the latter part of the 20th century (Egypt, Sudan, South Africa,

Madagascar and Northern Senegal/Southern Mauritania). Even though we excluded surveys that

were conducted during or immediately after known or suspected RVF epidemics, high-

seroprevalence of RVF occurred in inter-epidemic periods. This was probably due to persistence

Fig. 2. (Continued ).

Fig. 3. Bayesian probability map showing significant high and low-seroprevalence clusters of Rift Valley fever in Africa.

of protective antibodies in epidemic-affected populations, which has been shown to occur for at

least 10 years in human populations (Thonnon et al., 1999). Therefore, part of the spatially

structured residual variation in our model was probably due to epidemics in addition to variations

in the level of endemic RVF.

The significant low-prevalence clusters (previously not quantitatively identified) might be of

interest in the context of trade; they could be investigated as potential low-risk (though not

necessarily risk-free) sources of animals and animal products for export.

There were many areas of Africa from which no data had been collected, data were not

published in accessible journals, or data did not satisfy our eligibility criteria. These areas

included all of North Africa (except Egypt), Mali, much of Western Africa south of the Sahelian

Zone (from Guinea-Bissau to Ghana), the Democratic Republic of the Congo, much of Eastern

Africa (particularly Tanzania, Uganda, Rwanda, Burundi and Mozambique), the Horn of Africa

and parts of Southern Africa (e.g., Southern Namibia, Botswana and Zimbabwe). Given the

serious economic and health consequences of RVF epidemics, we recommend greater investment

in conducting or reporting RVF surveillance activities in these areas.

The higher odds ratios in the model for seroprevalence in the 1980s and 1990s compared to the

1970s might be explained by the occurrence of large epidemics during these decades. The lower

odds ratios for studies that reported a randomised sampling design, or that did not report their

sampling design, compared to explicitly non-randomised studies might reflect the deliberate

tendency of non-randomised studies to be conducted in high-prevalence populations. The lower

odds ratio for studies conducted in human populations as opposed to ungulate populations might

reflect lower susceptibility or less exposure of humans to RVFV compared to livestock. The

lower odds ratio where the IFAT was used to define infection status (as opposed to the HI test)

might reflect differences in test sensitivities and specificities. There is currently little published

information on the diagnostic performance of serological tests for RVF and the possible effects of

test sensitivity and specificity on observed seroprevalence need to be studied further.

Publication bias might have resulted in an overestimation of RVF prevalence in some areas

because serosurveys, that tested for but did not find RVF, might have been less likely to have been

reported than those that did detect RVF. However, we did include 41 publications where a zero

seroprevalence was reported for at least one species, including 32 publications that reported a

zero seroprevalence for all species investigated.

Despite using pre-determined criteria to exclude survey data that might have biased our

results, most studies did not describe in detail their sample selection methods and we

acknowledge that data quality was likely to have been highly variable between studies.

Additionally, although we attempted to control for important potential covariates that arose due

to the disparate nature of the survey subjects, many unmeasured sources of variation were likely

to have influenced our results.

5. Conclusions

We have highlighted sub-national areas of the African continent where evidence synthesised

from published serological data indicated high, low or unknown risk of RVFV infection in

humans and livestock. Our maps might assist decision-makers to target resources and attention to

parts of Africa where RVF poses a greater or unknown threat and to assist risk management in

non-affected regions by identifying potential sources of the infection and areas from which

animals and animal products may be imported with lower risk of introduction of RVF.

Acknowledgement

A.C.A. Clements was supported by a Royal Veterinary College clinical research fellowship.

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A Rift Valley fever atlas for Africa

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