Risk factors associated with human Rift Valley fever infection: systematic review and meta-analysis

Risk factors associated with human Rift Valley fever infection:

systematic review and meta-analysis

Dennis E. Nicholas, Kathryn H. Jacobsen and Nigel M. Waters

George Mason University, Fairfax, VA, USA

Abstract objective To identify risk factors for human Rift Valley fever virus (RVFV) infection.

methods A systematic review identified 17 articles reporting on 16 studies examining risk factors

for RVFV. Pooled odds ratios (pOR) were calculated for exposures examined in four or more studies.

results Being male [pOR = 1.4 (1.0, 1.8)], contact with aborted animal tissue [pOR = 3.4 (1.6,

7.3)], birthing an animal [pOR = 3.2 (2.4, 4.2)], skinning an animal [pOR = 2.5 (1.9, 3.2)],

slaughtering an animal [pOR = 2.4 (1.4, 4.1)] and drinking raw milk [pOR = 1.8 (1.2, 2.6)] were

significantly associated with RVF infection after meta-analysis. Other potential risk factors include

sheltering animals in the home and milking an animal, which may both involve contact with animal

body fluids.

conclusions Based on the identified risk factors, use of personal protective equipment and

disinfectants by animal handlers may help reduce RVFV transmission during outbreaks. Milk

pasteurisation and other possible preventive methods require further investigation.

keywords meta-analysis, Rift Valley fever, risk factors, systematic review

Introduction

Rift Valley fever (RVF) is a zoonotic arboviral infection

that has caused outbreaks in Africa and parts of the Ara-

bian peninsula since at least the 1930s (Gerdes 2004;

Evans et al. 2008; Swanepoel & Paweska 2011). RVF out-

breaks have caused significant loss of human and animal

life (Gerdes 2004; Swanepoel & Paweska 2011) as well as

economic hardship for those who raise livestock (Rich &

Wanyoike 2010; Dar et al. 2013).

Livestock such as goats, sheep, camels and cattle can

become infected with RVF virus (RVFV) when bitten by

an infected mosquito, typically of the Aedes genus. In

more susceptible breeds, pregnant goats, sheep and cattle

infected with RVFV experience high rates of spontaneous

abortions, and there are high case fatality rates for young

animals (Davies 2003; Evans et al. 2008). Indigenous

breeds of livestock appear to be less susceptible than

imported breeds (Davies 2003; Evans et al. 2008). Most

infected humans are asymptomatic or experience a mild

febrile illness, but ocular damage, meningoencephalitis,

haemorrhagic fever or death may occur in a minority of

cases (Evans et al. 2008; World Health Organization

2008; Swanepoel & Paweska 2011). This possibility of

severe complications from infection makes identification

of human RVF risk factors a public health priority in

affected areas. Most of the likely risk factors relate to

modes of transmission of RVFV. Humans are thought to

become infected with RVFV when they handle the blood

or tissues of infected animals or inhale the aerosolised

body fluids of infected animals during slaughter or veteri-

nary procedures. Humans may also contract RVFV from

infected mosquitoes, and some studies suggest that raw

milk from infected animals and bites from infected hae-

matophagous (blood-consuming) flies may also be trans-

mission routes (World Health Organization 2008).

These potential transmission routes point towards

likely risk factors for contracting RVFV. However, no

previous meta-analysis has used a pooled analysis of RVF

field studies to measure the associations between various

possible risk factors and RVF infection. The goal of this

systematic review and meta-analysis is to identify the

RVF risk factors that are well-supported by the scientific

literature on RVF, with a particular goal of determining

which behavioural risk factors might be appropriate tar-

gets for preventive interventions.

Methods

In July 2014, we searched the Medline, Global Health,

CINAHL, AJOL (African Journals Online), SciELO and

ScienceDirect databases for reports of RVF risk factors.

To maximise the completeness of the search and reduce

selection bias, only two subject headers were used: ‘Rift

1420 © 2014 John Wiley & Sons Ltd

Tropical Medicine and International Health doi:10.1111/tmi.12385

volume 19 no 12 pp 1420–1429 december 2014

Valley fever’ and ‘risk factor OR seroprevalence’. (For

AJOL and SciELO, the only search term used was ‘Rift

Valley fever.’) All study locations, study and publication

years, and publication languages were eligible for inclu-

sion. All abstracts identified from the database searches

were screened for eligibility, and the full text of all poten-

tially relevant articles was read. Additionally, the refer-

ence lists of all full text articles, including review articles,

were scanned to identify non-indexed articles that might

meet the eligibility criteria. This search process yielded

128 potentially relevant articles.

To be eligible for inclusion, an article had to report on

primary studies of human risk factors for acute RVF

infection (as indicated by a positive IgM test or virologi-

cal methods) or past RVF infection (as indicated by an

IgG test). Articles were excluded if they did not report on

risk factors for RVF infection based on serological or

virological laboratory testing (n = 84), if they examined

animal RVF rather than human RVF (n = 14), if they

were review articles or secondary reports rather than pri-

mary research (n = 4), if they were RVF case reports that

did not include a comparison group of healthy controls

(n = 8), or if they did not report measures of association

for one or more potential risk factors (n = 1). Relevant

data from each of the 17 eligible articles (reporting on 16

studies) were extracted into a spreadsheet for further

analysis.

The odds ratio (OR) was the most frequently reported

measure of association in the eligible articles, so it was

used as the key measure for meta-analysis. The adjusted

OR was used for meta-analysis when one was available

for an exposure of interest. When an adjusted OR was

not available, the crude OR was used. When included

studies reported rate ratios (RRs) or chi-squared test

results rather than ORs, we used Comprehensive Meta-

Analysis 2.0 (Biostat, Englewood, NJ, USA) and/or Epi

Info 7.1.1.0 (Centers for Disease Control and Prevention,

Atlanta, GA, USA) along with descriptive statistics (such

as frequencies) to convert these statistics to ORs. When a

study reported a prevalence ratio (PR) or an RR without

reporting descriptive statistics that could be converted to

an OR, the study was not included in meta-analysis.

The repeated cross-sectional studies that were con-

ducted in 2006 and 2009 at the same site in Kenya were

treated as independent studies for analysis, even though

about half of the 2009 participants (102/194) had also

been surveyed in 2006 (LaBeaud et al. 2008, 2011). Sen-

sitivity analyses of each risk factor that was examined by

both of these studies demonstrated that the pooled effect

estimate was not significantly different when one or the

other study was removed and the OR recalculated. ORs

reported for only cases with haemorrhagic symptoms of

RVF (Anyangu et al. 2010) were not combined in pooled

analysis with studies that reported associations based on

broader RVF case definitions.

When the ORs for a risk factor were available for four

or more studies, a pooled effects estimate for the risk

factor was calculated using StatsDirect 2.7.9 (StatsDirect

Ltd, Cheshire, UK). A minimum criterion of four studies

was selected because the confidence intervals for pooled

estimates with fewer than four studies were generally too

wide to be useful.

Meta-analyses use either fixed effect or random effects

models to estimate pooled OR effects. A fixed effect

model is based on the assumption that all studies

included in the pooled analysis used comparable methods

and similar populations, and that as a result the variation

among the studies’ measures of association can be attrib-

uted to random differences in the composition of the

populations sampled for each study. A random effects

model uses the more realistic assumption that the

included studies sampled individuals from different popu-

lations and that the magnitude of association between an

exposure and disease in diverse populations may be dis-

similar. The random effects model adjusts for these

potential differences by including the between-studies var-

iance in the calculation of the pooled OR effect (Hedges

& Vevea 1998). Each meta-analysis in this investigation

includes studies from several independent populations,

and a random effects model is usually appropriate for

these analyses. However, random effects models may be

unreliable when fewer than five studies are included in

the pooled measure of association (Borenstein et al.

2009). When few studies are being pooled, the best

option is to perform a fixed effect meta-analysis with the

stipulation that the possible generalisability of results be

interpreted conservatively (Borenstein et al. 2009). In this

analysis, a conservatively-interpreted fixed effect model

was used to calculate a pooled OR effect when four stud-

ies without substantial between-studies variance were

included in the analysis; a random effects meta-analysis

model was used for all other analyses (Borenstein et al.

2009).

StatsDirect 2.7.9 was used to create forest plots of the

results of each meta-analysis (Figures 1a–f). I2 tests,

which estimate the proportion of total variability among

studies that can be explained by heterogeneity, were used

to examine the consistency of study results and the

appropriateness of pooled analysis (Higgins et al. 2003).

I2 values range from 0% to 100%, with lower values

indicating less heterogeneity (Higgins et al. 2003).

Kendall’s tau tests examine the possibility of the pooled

results being affected by publication bias, which may

occur when statistically significant associations are more

© 2014 John Wiley & Sons Ltd 1421

Tropical Medicine and International Health volume 19 no 12 pp 1420–1429 december 2014

D. E. Nicholas et al. Risk factors for Rift Valley fever infection

0.2 0.5 1 2 5 10 100

pooled OR (random effects) 1.36 (1.04, 1.79)

OR calculated by authors Lernout et al. (2013) 1.40 (0.80, 2.50)

OR Anyangu et al. (2010) 1.42 (1.03, 1.95)

OR calculated by authors LaBeaud et al. (2007) 0.60 (0.40, 0.90)

OR Pourrut et al. (2010) 1.75 (1.25, 2.45)

OR Hassanain et al. (2010) 2.80 (1.02, 7.60)

OR calculated by authors Marrama et al. (2005) 1.14 (0.73, 1.79)

OR calculated by authors Swai & Schoonman (2009) 3.69 (0.44, 30.63)

OR calculated by authors Jouan et al. (1989) 0.67 (0.34, 1.35)

OR calculated by authors Woods et al. (2002) 1.42 (0.65, 3.12)

OR LaBeaud et al. (2011) 2.33 (1.18, 4.61)

adjusted OR LaBeaud et al. (2008) 2.78 (1.18, 6.58)

adjusted OR Al-Azraqi et al. (2012) 1.25 (0.79, 1.99)

Odds ratio (95% confidence interval)

0.1 0.2 0.5 1 2 5 10 100 1000


OR calculated by authors Marrama et al. (2005) 0.96 (0.45, 2.03)

OR Lernout et al. (2013) 11.12 (1.10, 112.30)

OR calculated by authors Wilson et al. (1994) 4.66 (1.29, 16.82)



OR calculated by authors Archer et al. (2011) 0.71 (0.11, 4.47)

OR LaBeaud et al. (2011) 3.49 (1.52, 8.00)



1 2 5 10

pooled OR (fixed effect) 3.18 (2.38, 4.24)

OR Anyangu et al. (2010) 2.22 (1.40, 3.52)



OR LaBeaud et al. (2011) 3.62 (1.61, 8.14)


(a)

(b)

(c)

Figure 1 (a) Forest plot for being male. (b) Forest plot for contact with aborted animal tissue. (c) Forest plot for birthing an animal.

(d) Forest plot for skinning an animal. (e) Forest plot for slaughtering an animal. (f) Forest plot for drinking raw milk.




1 2 5 10 100

pooled OR (fixed effect) 2.47 (1.90, 3.20)



OR Anyangu et al. (2010) 2.19 (1.49, 3.22)

OR LaBeaud et al. (2011) 2.12 (1.06, 4.24)


0.01 0.1 0.2 0.5 1 2 5 10 100


OR Lernout et al. (2013) 2.15 (0.81, 5.70)

OR calculated by the authors LaBeaud et al. (2008) 1.51 (0.95, 2.38)

OR calculated by the authors Archer et al. (2011) 0.90 (0.08, 10.21)

OR Anyangu et al. (2010) 2.11 (1.43, 3.11)



0.2 0.5 1 2 5 10 100 1000



OR Lernout et al. (2013) 0.88 (0.45, 1.72)

OR calculated by authors Jouan et al. (1989) 1.92 (0.99, 3.72)


OR Anyangu et al. (2010) 1.71 (1.19, 2.46)



(d)

(e)

(f)

Figure 1 (Continued).




likely than null results to be published. The values of Ken-

dall’s tau range from �1 to 1, with 0 indicating no evi-

dence of bias and absolute values near 1 demonstrating a

strong likelihood of bias (Rothstein et al. 2006). However,

Kendall’s tau requires a substantial number of included

studies (at least 10) in order to have the power to evaluate

a possible association between the effects and their stan-

dard errors (Higgins & Green 2011). Because so few possi-

ble risk factors for RVF were examined by 10 or more

studies, two additional methods were used to evaluate pos-

sible publication bias: the metafor package in R 3.0.1 soft-

ware (Viechtbauer 2010) was used to implement Duval

and Tweedie’s trim and fill method for calculating an

adjusted pooled effect estimate that imputes the effects of

missing studies (Duval & Tweedie 2000; Borenstein et al.

2009), and R 3.0.1 was used to apply Orwin’s method for

estimating the number of missing studies with null results

that would be required to reduce the overall summary

effect to a trivial OR of 0.95–1.05 (Borenstein et al. 2009).

Results

Table 1 briefly describes each of the 16 eligible studies,

which span 24 years of data collection (1987–2011) andmuch of the current geographic range of RVF. Ten of the

studies were descriptional cross-sectional studies, three

were case–control studies that compared those with and

without laboratory-confirmed RVF, and two were cohort

studies that followed participants forward in time to see

how many became infected with RVFV during the

follow-up period. The sample sizes for the cross-sectional

studies ranged from 171 to 4323, participant numbers

that generally provide sufficient statistical power for prev-

alence estimation. While one case–control study included

only about 50 participants, the others included 169–861participants. The cohort studies followed 273–685 partic-

ipants for 2 weeks to 3 months. All of the studies used

laboratory tests to confirm RVF diagnoses.

Ten potential risk factors were examined by three or

more studies. Figure 2 presents these findings graphically,

with statistically significant associations shown in the

upper half of the figure and non-significant results in the

lower half. The results are grouped into four categories:

demographic characteristics, animal-related factors, milk-

related factors and environmental factors (flooding).

Whenever possible, the measurement listed in each box is

the odds ratio and 95% confidence interval for the asso-

ciation. Preference was given to adjusted ORs listed in

the included studies. When no adjusted OR for an associ-

ation was available, the crude OR from the article is

listed. When no OR was provided but one could be

calculated from data provided in the article, a calculated

OR is provided. When no OR could be calculated a risk

ratio or prevalence ratio is listed.

Six (LaBeaud et al. 2007, 2008, 2011; Anyangu et al.

2010; Hassanain et al. 2010; Pourrut et al. 2010) of the

13 (Jouan et al. 1989; Woods et al. 2002; Marrama et al.

2005; Swai & Schoonman 2009; Al-Azraqi et al. 2012;

Heinrich et al. 2012; Lernout et al. 2013) studies that

investigated being male as a possible risk factor found a

statistically significant association with RVF infection.

ORs were available or able to be calculated for twelve of

these thirteen studies, so twelve studies were included in

a meta-analysis (Jouan et al. 1989; Woods et al. 2002;

Marrama et al. 2005; LaBeaud et al. 2007, 2008, 2011;

Swai & Schoonman 2009; Anyangu et al. 2010;

Hassanain et al. 2010; Pourrut et al. 2010; Al-Azraqi

et al. 2012; Lernout et al. 2013). The I2 test result

(I2 = 63.9%) suggested inconsistency in these studies’

measures of association. Because the available data could

not sufficiently explain the heterogeneity among sub-

groups (Higgins & Green 2011), the best analytic option

was to use a random effects meta-analysis model that

adjusted for these inconsistencies among studies. Ken-

dall’s tau (0.18, P = 0.46) indicated a low likelihood of

publication bias. Duvall and Tweedie’s trim and fill

method estimated that two missing studies would lower

the original pOR of 1.4 (1.0, 1.8) to 1.3 (1.0, 1.7).

Pooled analysis stratified by epidemicity for this associa-

tion [non-epidemic pOR = 1.4 (1.0, 2.0), epidemic

pOR = 1.3 (1.0, 1.7)] did not differ significantly from the

original pOR. Orwin’s method estimated that 96 missing

studies with an average null effect would be necessary to

lower the pooled effect estimate to a trivial OR of 1.05

or less. These results support being male as a risk factor

for RVF infection.

Seven (Woods et al. 2002; LaBeaud et al. 2007, 2008,

2011; Pourrut et al. 2010; Heinrich et al. 2012; Lernout

et al. 2013) of the 11 (Marrama et al. 2005; Anyangu

et al. 2010; Hassanain et al. 2010; Al-Azraqi et al. 2012)

studies that examined the association between age and

RVF infection concluded that older people had a greater

risk of infection. Because the age variables reported in

these studies were not measured in the same manner –some were continuous and some dichotomous, and they

used different age groups for categories – a meta-analysis

was not appropriate. More importantly, six of these stud-

ies (LaBeaud et al. 2007, 2008, 2011; Pourrut et al.

2010; Heinrich et al. 2012; Lernout et al. 2013) tested

for RVFV IgG seroprevalence, an indication of non-acute

exposure. Older people will, by definition, have greater

risk of past exposure than younger cohorts. A greater

lifetime prevalence of past exposure cannot be assumed

to translate to a greater risk for acute infection.




Table

1Characteristics

ofthe16eligible

studiesofhumanRVFrisk

factors

Country

Year(s)

ofdata

collection

Studydesign

Case

definition

Sample

size

Number

ofcases

Epidem

icReference

Mayo

tte

2011

Cross-sectional

IgG

seropositivity

1413

58

No

Lernoutet

al.(2013)

Kenya

2006,2009

Repeatedcross-sectional

surveys

IgG

seropositivityandplaquereduction

neutralisationtesting(PRNT)

194

44

No

LaBeaudet

al.(2011)

SaudiArabia

2008

Cross-sectional

IgG

seropositivity

2322

139

No

Al-Azraqiet

al.(2012,2013)

South

Africa

2008

Case–control

IgM

seropositivity,

RT-PCR

nucleicacid

detectionorvirusisolation

51

8Yes

Archer

etal.(2011)

Tan

zania

2007–2

008

Cross-sectional

IgG

seropositivity

1228

64

No

Heinrich

etal.(2012)

Gab

on

2005–2

008

Cross-sectional

IgG

seropositivity

4323

145

No

Pourrutet

al.(2010)

Kenya

2007

Case–control

IgM

orRT-PCR

seropositivity,

RT-PCR

nucleicaciddetectionorhaem

orrhagic

symptoms

861

202

Yes

Anyan

guet

al.(2010)

Sudan

2007

Cross-sectional

IgG

seropositivity

149

122

Yes

Hassan

ain

etal.(2010)

Kenya

2006

Cross-sectional

IgG

seropositivity

248

33

No

LaBeaudet

al.(2008)

Tan

zania

2004

Cross-sectional

IgG

seropositivity

199

8No

Swai&

Schoonman(2009)

Senegal

1999

Cross-sectional

IgG

seropositivity

1520

79

No

Marramaet

al.(2005)

Kenya

1997–1

998

Cross-sectional

IgM

seropositivity

171

31

Yes

Woodset

al.(2002)

Kenya

1994,1996,

1997,1998

Cross-sectional

IgG

seropositivityandplaquereduction

neutralisationtesting(PRNT)

1263

143

No

LaBeaudet

al.(2007)

Egypt

1993

Cohort

(withnested

case–controlanalysis)

IgM

seropositivity

685

69

Yes

Centers

forDisease

Control

&Prevention(C

DC)(1994)

Senegal

1989

Cohort

IgG

seropositivity

273

61

No

Wilsonet

al.(1994)

Mauritania

1987

Case–control

IgM

seropositivityorvirusisolation

169

116

Yes

Jouanet

al.(1989)




Seven (Wilson et al. 1994; Woods et al. 2002; LaBeaud

et al. 2008, 2011; Anyangu et al. 2010; Al-Azraqi et al.

2012; Lernout et al. 2013) of the nine (Marrama et al.

2005; Archer et al. 2011) studies that examined contact

with aborted animal tissue found a statistically significant

association with RVF infection. Eight ORs for this associ-

ation were included in a meta-analysis (Wilson et al.

1994; Woods et al. 2002; Marrama et al. 2005; LaBeaud

et al. 2008, 2011; Archer et al. 2011; Al-Azraqi et al.

2012; Lernout et al. 2013). The I2 test result (I2 = 80.0%)

indicated inconsistency among the measures of association.

Duvall and Tweedie’s trim and fill method did not find evi-

dence of publication bias that would lower the point esti-

mate for the pOR, and Orwin’s method estimated that 194

missing studies with an average null effect would be neces-

sary to lower the pooled effect estimate to an OR of 1.05

or less. The random effects pOR of 3.4 (1.6, 7.3) supports

contact with aborted animal tissue as a risk factor for RVF

infection.

All four of the studies that examined birthing an ani-

mal found a statistically significant association with RVF

infection (Woods et al. 2002; LaBeaud et al. 2008, 2011;

Anyangu et al. 2010), and four ORs from these studies

were included in a meta-analysis. The I2 test result

(I2 = 23.9%) indicated a low likelihood of inconsistency

among the measures of association. Duvall and Tweedie’s

trim and fill method estimated that one missing study

would lower the original fixed effect pOR of 3.2 (2.4,

4.2) to 3.0 (2.3, 4.0). Orwin’s method estimated that 98

missing studies with an average null effect would be nec-

essary to lower the pooled effect estimate to an OR of

1.05 or less. This supports birthing an animal as a risk

factor for RVF infection.

All four studies that investigated skinning an animal

found a statistically significant association with RVF

infection (Woods et al. 2002; LaBeaud et al. 2008, 2011;

Anyangu et al. 2010), and the four ORs from these stud-

ies were included in a meta-analysis. The test for hetero-

geneity (I2 = 5.9%) indicated a low likelihood of

inconsistency in the reports. Duvall and Tweedie’s trim

and fill estimated that there were no missing studies in

the meta-analysis, and Orwin’s method estimated that 79

studies with an average null effect would be necessary to

lower the fixed effect pOR of 2.5 (1.9, 3.2) to 1.05 or

less. These results support skinning an animal as a risk

factor for RVF infection.

The four studies that investigated sheltering animals in

the home all found a statistically significant association

with RVF infection (Woods et al. 2002; LaBeaud et al.

2008, 2011; Al-Azraqi et al. 2012), and four ORs from

these studies were included in a meta-analysis. The I2 test

result (56.5%) suggested possible inconsistency in these

studies’ findings. As neither a fixed effect nor a random

effects model was appropriate, a pOR was not calcu-

lated.

Three (Al-Azraqi et al. 2012; Anyangu et al. 2010;

Centers for Disease Control & Prevention (CDC) 1994)

of the six (LaBeaud et al. 2008; Archer et al. 2011;

*OR = 16.6 (2.2,121.0) Lernout et al. (2013)

≥15 years old

OR = 11.1 (1.1, 112.3) Lernout et al. (2013) births or abortions

OR = 1.4 (1.0, 2.0) Anyangu et al. (2010)

acute cases

*OR 3.3 (2.1, 5.1) LaBeaud et al. (2007)

≥16 years old

*OR = 4.7 (1.3, 16.8) Wilson et al. (1994)births or abortions

OR = 1.8 (1.3, 2.5) Pourrut et al. (2010)

OR = 0.5 (0.3, 0.8) Pourrut et al. (2010)

15-33 years old

OR = 3.5 (1.5, 8.0) LaBeaud et al. (2011)

OR = 2.8 (1.0, 7.6) Hassanain et al. (2010)

PR = 1.02 (1.01, 1.04) Heinrich et al. (2012)

per year

aOR = 3.8 (1.7, 9.1) Anyangu et al. (2010)

severe cases

OR = 2.2 (1.4, 3.5) Anyangu et al. (2010)

acute cases

*OR = 4.9 (2.1, 11.1) Woods et al. (2002)

aOR = 2.0 (1.1, 3.6) Al-Azraqi et al. (2012)

aOR = 2.8 (1.2, 6.6) LaBeaud et al. (2008)

aRR = 0.3 (0.1, 1.0) Woods et al. (2002)

< 15 years old

*OR = 4.6 (2.0, 10.4) Woods et al. (2002)

OR = 3.6 (1.6, 8.1) LaBeaud et al. (2011)

OR = 2.1 (1.1, 4.2) LaBeaud et al. (2011)

OR = 3.8 (1.5, 9.4) LaBeaud et al. (2011)

cow only

RR = 2.5 (1.2, 5.1) CDC (1994)

*OR = 17.1 (2.2, 129.8) Woods et al. (2002)

OR = 2.1 (1.5, 2.9) Anyangu et al. (2010)

acute cases

OR = 2.3 (1.2, 4.6) LaBeaud et al. (2011)

aOR = 1.04 (1.02, 1.06) LaBeaud et al. (2011)

aOR = 2.8 (1.0, 7.5) LaBeaud et al. (2008)

*OR = 4.3 (1.9, 9.9)Woods et al. (2002)

OR = 2.2 (1.5, 3.2) Anyangu et al. (2010)

acute cases

*OR = 8.3 (2.7, 24.8) Woods et al. (2002)


aOR = 2.9 (1.3, 6.4) LaBeaud et al. (2011)

*OR = 5.7 (2.2, 14.8) Woods et al. (2002)

OR = 1.6 (1.1, 2.2) Anyangu et al. (2010)

acute cases*OR = 0.6(0.4, 0.9)

LaBeaud et al. (2007)male is protective

aOR = 1.05 (1.03, 1.07) LaBeaud et al. (2008)


*OR = 4.1 (2.5, 6.7) LaBeaud et al. (2008)

*OR = 2.5 (1.6, 4.0) LaBeaud et al. (2008)

*OR = 2.0 (1.3, 3.2) LaBeaud et al. (2008)

OR = 2.1 (1.4, 3.1) Anyangu et al. (2010)

acute cases

OR = 1.7 (1.2, 2.5) Anyangu et al. (2010)

acute cases

*OR = 2.6 (1.7, 4.2) LaBeaud et al. (2008)

*OR = 1.7 (1.1, 2.7) LaBeaud et al. (2008)

*OR = 1.4 (0.7, 3.1) Woods et al. (2002)

aOR = 1.2 (0.6, 2.3)Al-Azraqi et al. (2012)

20 years old

*OR = 0.7 (0.1, 4.5) Archer et al. (2011)

*OR = 1.5 (1.0, 2.4) LaBeaud et al. (2008)

*OR = 1.5 (1.0, 2.4) LaBeaud et al. (2008)

*OR = 1.5 (0.6, 3.9)Woods et al. (2002)

*OR = 0.7 (0.3, 1.4) Jouan et al. (1989)

*OR = 0.8 (0.5, 1.3) Marrama et al. (2005)

15 years old

*OR = 1.0 (0.5, 2.0) Marrama et al. (2005)

*OR = 0.9 (0.1, 10.2) Archer et al. (2011)

*OR = 1.9 (1.0, 3.7) Jouan et al. (1989)

all participants


OR = 1.0 (0.9, 1.0) Hassanain et al. (2010)

OR = 2.2 (0.8, 5.7) Lernout et al. (2013)

OR = 0.9 (0.5, 1.7) Lernout et al. (2013)

PR = 1.1 (0.7, 1.8) Heinrich et al. (2012)

*OR = 1.3 (0.8, 2.2) Anyangu et al. (2010) ≥ 15 years acute cases

*OR = 3.7 (0.4, 30.6) Swai & Schoonman

(2009)

*OR = 1.1 (0.7, 1.8) Marrama et al. (2005)

*OR = 1.4 (0.8, 2.5) Lernout et al. (2013)

Being Male Older Age Contact with Aborted Animal

Tissue

Birthing an Animal

Skinning an Animal

Sheltering Animals in the

Home

Slaughtering an Animal

Drinking Raw Milk Milking an Animal Home Flooding

Sta

tistic

ally

Sig

nific

ant

Not

Sta

tistic

ally

Sig

nific

ant

Figure 2 The risk factors for RVF reported in three or more studies. *OR calculated by the authors.




Lernout et al. 2013) studies that examined slaughtering

an animal found a statistically significant association with

RVF infection. Five ORs for this association were included

in a meta-analysis (LaBeaud et al. 2008; Anyangu et al.

2010; Archer et al. 2011; Al-Azraqi et al. 2012; Lernout

et al. 2013). The I2 test result (71.0%) suggested incon-

sistency in these studies’ findings. Duvall and Tweedie’s

trim and fill method did not find evidence of publication

bias that would lower the point estimate for the pOR,

and Orwin’s method estimated that 68 missing studies

with an average null effect would be necessary to lower

the pooled effect estimate to an OR of 1.05 or less. The

random effects pOR of 2.4 (1.4, 4.1) supports slaughter-

ing an animal as a risk factor for RVF infection.

Additionally, three studies that looked at general con-

tact with various types of animals (cattle, sheep, goats

and camels) found that exposure to animals was associ-

ated with increased risk of RVF (Woods et al. 2002;

LaBeaud et al. 2008; Anyangu et al. 2010). However, the

non-specific nature of this exposure means that it is of

limited use in identifying particular risks. Some analyses

may also be incomplete because they combined exposures

to multiple animals. Risk factors for RVFV transmission

to humans may vary for different species of livestock, as

was observed in the analyses of exposure to aborted

animal tissue [cow OR = 4.5 (1.5, 13.3), goat OR = 3.0

(1.3, 7.1), sheep OR = 3.6 (1.5, 8.6)] and birthing an

animal [cow OR = 8.5 (2.7, 26.4), goat OR = 3.4 (1.4,

7.9), sheep OR = 3.4 (1.4, 7.9)] reported in stratified

analysis in one study (LaBeaud et al. 2011) (not shown

in Figure 2).

Three (Woods et al. 2002; Anyangu et al. 2010;

LaBeaud et al. 2011) of the six studies (Jouan et al.

1989; LaBeaud et al. 2008; Lernout et al. 2013) that

examined drinking raw milk found a statistically signifi-

cant association with RVF infection. The six ORs from

these six studies were included in a meta-analysis. The

test for heterogeneity (I2 = 53.6%) suggested possible

inconsistency among the measures of association. Duvall

and Tweedie’s trim and fill method estimated that one

missing study would lower the original random effects

pOR of 1.8 (1.2, 2.6) to 1.6 (1.1, 2.5). Orwin’s method

estimated that 105 missing studies with an average null

effect would be necessary to lower the pooled effect esti-

mate to an OR of 1.05 or less. These results support

drinking raw milk as a risk factor for RVF infection.

Other exposures were investigated too infrequently to

allow for meta-analysis. The three studies that examined

milking an animal as a possible risk factor all found a

statistically significant association with RVF infection

(Woods et al. 2002; LaBeaud et al. 2008; Anyangu et al.

2010), as did two (LaBeaud et al. 2008; Anyangu et al.

2010) of the three (Woods et al. 2002) studies that inves-

tigated the association between home flooding and RVF

infection.

Discussion

Almost two-thirds (28/44) of the statistically significant

associations reported by the eligible studies were for

exposures related to contact with animals or consump-

tion of animal products: contact with aborted animal

tissue, birthing an animal, skinning an animal, sheltering

animals in the home, slaughtering an animal, drinking

raw milk and milking an animal. Based on the meta-

analyses, contact with aborted animal tissue, birthing an

animal, skinning an animal and slaughtering an animal

can be considered confirmed risk factors for RVF. The

results of the meta-analysis also confirm drinking raw

milk as a risk factor for infection. However, a recent study

found that raw milk consumption was not a significant

predictor of RVF infections in South Africa between 2008

and 2011 (Archer et al. 2013), suggesting that additional

studies are needed to further evaluate this association in

different contexts. Contact with aborted tissue, birthing,

skinning and slaughtering involve contact with infected

animal blood or tissue in ways that may allow the virus to

enter into wounds in the skin or be inhaled (World Health

Organization 2008; Swanepoel & Paweska 2011). Small

amounts of RVFV have been reported in the milk, saliva

and nasal discharges of infected sheep and cattle (Swane-

poel & Paweska 2011), so consuming infected raw milk

could represent another pathway for the virus to be intro-

duced into the body. Additionally, being male appears to

be a risk factor for RVF, likely because of occupational

exposure, but additional confirmatory studies are required.

More research is also necessary to further examine

whether birthing an animal (or particular types of live-

stock), sheltering animals in the home, slaughtering an ani-

mal, milking an animal and home flooding increase the

risk of contracting RVFV.

While it is possible that some of these results were influ-

enced by various types of bias (Jacobsen 2011), we were

careful to minimise bias when identifying relevant articles

and to interpret our results cautiously when tests sug-

gested the possible presence of bias in pooled estimates.

To minimise search bias, the search terms were minimally

restrictive. To minimise selection bias, all publication

languages and all study designs were eligible for inclusion

in the review. When the sample size was sufficiently large,

we used Kendall’s tau to test for the likelihood of publica-

tion bias in each meta-analysis. Two additional statistical

tests (Duvall and Tweedie’s trim and fill and Orwin’s

method) were used to evaluate the impact of potentially




unpublished studies on the pooled effect. Thus, the conclu-

sions from this analysis should be valid even with the

small sample size for some potential risk factors. How-

ever, the findings of some studies may be affected by recall

bias resulting when notable events such as home flooding

may have been more easily recalled than less notable

potential exposures such as an insect bite.

These findings point to potentially effective RVF infec-

tion control policies and practices. As contact with

animal tissue appears to be an important mode of trans-

mission of RVFV, especially during outbreaks, increased

use of personal protective equipment (such as shoulder-

length polyethylene gloves) and disinfectants (such as

alcohol-based hand sanitisers) may help to reduce trans-

mission during times when RVF is endemic or epidemic

within a community (Archer et al. 2011; Swanepoel &

Paweska 2011; Fyumagwa et al. 2012). These control

strategies may be more feasible to implement in a com-

mercial setting like an abattoir than in a rural farm set-

ting. If drinking raw milk is a risk factor, pasteurising

milk through boiling could aid in control efforts. Addi-

tional research on behavioural risk factors and on modes

of transmission may help to identify other targets for pre-

vention activities. Ideally, future studies should seek to

use common definitions for exposure variables, which

would allow for easier comparisons of studies and would

facilitate further meta-analysis. Subsequent research

should explore the effect of the intensity of exposure

(such as the amount of milk consumed or number of

animals slaughtered or birthed) in addition to the pres-

ence or absence of exposure.

RVF has demonstrated the capacity to expand into

new geographical regions and cause outbreaks in suscep-

tible populations, and climate change may further expand

the range of RVF (Chevalier et al. 2010). RVF studies

that definitively characterise the behavioural and demo-

graphic risk factors for infection are vitally important for

preparing to address future outbreaks of this emerging

viral infection.

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Corresponding Author Dennis E. Nicholas, 4400 University Dr 5B7, Fairfax, VA 22030, USA. E-mail: [email protected]




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Risk factors associated with human Rift Valley fever infection: systematic review and meta-analysis