Skip to content

Advertisement

  • Research
  • Open Access

Household-level and surrounding peri-domestic environmental characteristics associated with malaria vectors Anopheles arabiensis and Anopheles funestus along an urban–rural continuum in Blantyre, Malawi

Malaria Journal201817:229

https://doi.org/10.1186/s12936-018-2375-5

  • Received: 1 April 2018
  • Accepted: 5 June 2018
  • Published:

Abstract

Background

Malaria is increasing in some recently urbanized areas that historically were considered lower risk. Understanding what drives urban transmission is hampered by inconsistencies in how “urban” contexts are defined. A dichotomized “urban–rural” approach, based on political boundaries may misclassify environments or fail to capture local drivers of risk. Small-scale agriculture in urban or peri-urban settings has been shown to be a major risk determinant.

Methods

Household-level Anopheles abundance patterns in and around Malawi’s commercial capital of Blantyre (~ 1.9 M pop.) were analysed. Clusters (N = 64) of five houses each located at 2.5 km intervals along eight transects radiating out from Blantyre city centre were sampled during rainy and dry seasons of 2015 and 2016. Mosquito densities were measured inside houses using aspirators to sample resting mosquitoes, and un-baited CDC light traps to sample host seeking mosquitoes.

Results

Of 38,895 mosquitoes captured, 91% were female and 87% were Culex spp. Anopheles females (N = 5058) were primarily captured in light traps (97%). Anopheles abundance was greater during rainy seasons. Anopheles funestus was more abundant than Anopheles arabiensis, but both were found on all transects, and had similar associations with environmental risk factors. Anopheles funestus and An. arabiensis females significantly increased with distance from the urban centre, but this trend was not consistent across all transects. Presence of small-scale agriculture was predictive of greater Anopheles spp. abundance, even after controlling for urbanicity, number of nets per person, number of under-5-year olds, years of education, and season.

Conclusions

This study revealed how small-scale agriculture along a rural-to-urban transition was associated with An. arabiensis and An. funestus indoor abundances, and that indoor Anopheles density can be high within Blantyre city limits, particularly where agriculture is present. Typical rural areas with lower house density and greater distance from urban centres reflected landscapes more suitable for Anopheles reproduction and house invasion. However, similar characteristics and elevated Anopheles abundances were also found around some houses within the city limits. Thus, dichotomous designations of “urban” or “rural” can obscure important heterogeneity in the landscape of Plasmodium transmission, suggesting the need for more nuanced assessment of urban malaria risk and prevention efforts.

Keywords

  • Urban–rural
  • Urban malaria
  • Small-scale agriculture
  • Vector ecology
  • Anopheles
  • Malawi

Background

Malaria continues to take the lives of nearly half a million people every year, with 90% of deaths occurring in sub-Saharan Africa (SSA). The World Health Organization (WHO) estimates 216 million cases and 445,000 deaths due to malaria in 2016, an increase of approximately 5 million cases during 2015 [1]. Malaria is endemic throughout most of SSA and is the leading cause of death in Malawi among children under five years of age [2].

In 2017, approximately 3.2 million people in Malawi (17% of the population) lived in an urban setting [3, 4]. With an annual urban growth rate of 4%, Malawi has one of the highest rates of urbanization of any African country [3, 4]. Although malaria in SSA has been widely studied, most research has been carried out in rural contexts, and little is known about how increasing urbanicity may be affecting Plasmodium transmission and malaria risk.

Considerable evidence suggests that people living in urban settings in SSA have improved health, including decreased infant mortality, better nutritional status, increased vaccine coverage, and increased access to care [5]. Specific to malaria, studies have shown that long-lasting insecticidal net (LLIN) use is higher in urban compared to rural settings, and overall parasite prevalence in children living in large cities in SSA is less than half that of children living in rural communities within the same zone of malaria endemicity [68]. Urban areas generally experience lower incidence of malaria compared to rural settings, as greater human population densities may reduce individual-level exposure [911, 13, 14]. Less vegetation and polluted water sources may reduce the number of suitable breeding sites for Anopheles mosquito vectors and limit opportunities for vector dispersal from breeding sites [1015].

Nonetheless, knowledge about local Plasmodium transmission in highly heterogeneous urban areas remains limited. Conditions of urban poverty, poor quality infrastructure, and small-scale crop production may enhance anopheline breeding habitats, particularly for adaptable species such as Anopheles funestus [12, 14, 16, 18, 19]. In addition, urban land use is often poorly monitored, especially in areas regarded as peri-urban “sprawl” [14]. Urban small-scale agriculture gardens may also provide more suitable breeding and resting sites for Anopheles mosquitoes, thereby contributing to local, urban Plasmodium transmission [1719].

Definitions of “urban” vary widely among countries, have changed over time, and are often incomplete or not useful for studying disease [20, 21]. Traditionally, a dichotomized approach has been used to differentiate “rural” from “urban”. In developing countries where urbanization is highly variable, this approach may misclassify or fail to capture the fine-scale heterogeneity within these broad classifications, which is essential to understanding underlying drivers of various disease-causing processes [22, 23]. Understanding specific urban or rural factors that are protective against or risky for malaria should improve disease surveillance and help target control efforts [24].

This aim of this study was to characterize the diversity and abundance of Anopheles species along an urban–rural continuum in Blantyre, Malawi, and to assess which household-level and surrounding peri-domestic environmental characteristics are associated with increased vector abundance.

Methods

Study design

A total of 320 houses were identified for study, comprised of five houses at each of eight locations situated 2.5 km apart along each of eight transects radiating out from Blantyre city centre (Fig. 1). Houses were sampled during five, 6-week periods in both rainy and dry seasons between February 2015 and August 2016, for a possible total of 1600 house-samples. The study protocol was approved by the University of Malawi College of Medicine Research and Ethics Committee, as well as the Institutional Review Boards at Michigan State University and the University of Michigan [25].
Fig. 1
Fig. 1

Sampling design (black boundary denotes Blantyre city administrative boundary). The eight transects were aligned with major roads leading outwards from Blantyre city centre towards rural Blantyre. Clusters of five households each within a distance of 1.5 km of the road were chosen at random from within a 500 m × 500 m area at each of the 64 sampling points. Each 500 m × 500 m area was divided into a grid of 25 subunits, each 100 m × 100 m, and five houses were chosen at random from five of the 25 subunits. If more than one household was in a 100 m × 100 m subunit, only one was selected. If fewer than five houses were located within the total 500 m × 500 m area, houses nearest to the grid were selected progressively until five households total were identified [25]

Informed consent to administer a questionnaire and collect mosquitoes was obtained from the head of household or another adult resident of the household at the time of the first survey visit. Socio-demographic and environmental data were collected for each household and its surrounding area. Household questionnaires were administered by trained surveyors to obtain demographic and malaria risk or prevention information. Data on housing construction and peri-domestic land use/land-cover (LULC) were collected by direct observation of the house, and within a ~ 50 m radius surrounding the dwelling. House construction variables included windows (open or partially open vs. closed), eaves (open or partially open vs. closed) and roofing material (iron sheets, thatch, and tile). The presence or absence of various LULC types including any type of agriculture (maize, millet, cassava, tomato, potato, green peas and/or cocoa), fruit trees, forest, and grazing land were documented. All questionnaire and observational data were recorded on tablets using OpenDataKit collect software.

Mosquito collection

To measure malaria vector species abundance and distribution, indoor resting adult mosquitoes were sampled using Prokopack™ aspirators, and foraging adult mosquitoes were collected using CDC miniature light traps [26, 27]. During each household visit, survey team members spent ~ 10 min. aspirating walls and ceilings of sleeping and living spaces, beneath furniture, behind curtains, and around clothing. Light traps without chemical attractants were turned on at dusk by a household member and were removed the following morning by a study team member. All mosquitoes collected by light trap or aspiration were returned the same day to the entomology lab for morphological species identification, sexing, and determination of blood-feeding by microscopy [28]. Species identification of all Anopheles females was later confirmed by Polymerase Chain Reaction (PCR) at the International Center of Excellence for Malaria Research (ICEMR) Molecular Core facilities at Malawi College of Medicine. Details of laboratory methods for mosquito speciation are presented in Additional file 1.

Satellite-derived variables

Global positioning system (GPS) coordinates for each sampled household were recorded on tablets with a mean accuracy of ± 4.9 m. Multiple GPS-derived locations at the same house were averaged. Composite Google Earth imagery was extracted and analysed with ArcMap 10.2.1 (ESRI, Redlands, CA); dates ranged from January 2015 to December 2016 depending on the highest resolution image with minimal cloud cover available for each region. All households in a 50 m buffer around each observation were digitized and density was computed. Study sites were classified as “within Blantyre city limits” (urban) or “outside Blantyre city limits” (rural) in ArcMap 10.2.1 based on official governmental administrative boundary limits. Publicly available, spatially referenced data were downloaded and values at the household-level were extracted in ArcMap 10.2.1 or QGIS 2.18 (Open Source) for elevation [digital elevation model (DEM)], normalized difference vegetation index (NDVI), and percentage of cropland within a 50 m radius around each dwelling [2931]. NDVI was calculated in QGIS 2.18 using bands 4 (Red) and 5 (near infrared (NIR)) from Landsat 8 OLI/TIRS C1 Level-1 30 m resolution satellite imagery for two time points, March 21, 2016, and July 27, 2016, corresponding to one rainy and one dry season within the study period respectively [3234]. NDVI ranges from − 1 to + 1.
$$NDVI = \frac{NIR - Red}{NIR + Red}$$

Statistical analyses

Descriptive statistics were summarized for all characteristics of households with non-missing exposure, outcome, and covariate data. Mosquito abundance data were summarized for rainy and dry seasons. All statistical analyses were conducted in SAS 9.4 (SAS Institute, Cary, NC).

Negative binomial regression models were used to evaluate possible associations of explanatory variables with counts of Anopheles funestus and Anopheles arabiensis, separately. Explanatory variables included those involving household demographics (number of rooms per house, number of household members, number of children under 5 years old, education status, and sex of household head), and anti-malaria behaviours (bed net ownership and use). In addition, associations between Anopheles abundances and household environmental and peri-domestic characteristics were evaluated, including season, elevation, urban or rural status, distance from city centre, surrounding house density, NDVI, presence of various LULC types (e.g. agriculture, forest, and grazing), livestock ownership, windows, and eaves (open or partially open vs. closed), and roof type.

Abundances of An. funestus and An. arabiensis were analysed as count data with an equal observation time of one trap-night considered for each household. Counts of Anopheles spp. (arabiensis and funestus) mosquitoes caught by light trap exhibited significant over-dispersion. Zero An. arabiensis and An. funestus mosquitoes were captured in 85.2 and 79.7% of households, respectively, one mosquito was caught in 5.6 and 7.1% of households, respectively, and two or more mosquitoes were caught in 9.2 and 13.2% of households, respectively.

Variances of mosquito counts exceeded means, thus Poisson and negative binomial models were compared to determine best fit. Zero-inflated models were not considered despite overdispersion of zero mosquito counts, because zero-inflated models assume that the zero outcome is due to two different processes, one process with zero being the only possible outcome. Since exposure to malaria vectors is generally ubiquitous in this study area, other types of statistical models were compared. Both Poisson and negative binomial model types gave equivalent effect estimates, but negative binomial models for both An. funestus and An. arabiensis fitted the data better based on Akaike information criterion (AIC) and the likelihood ratio test. Logistic regression models were also explored using presence or absence of An. funestus and An. arabiensis separately as the outcome variable. Observations were assumed to be independent due to the cross-sectional nature of the sampling design.

Results

Descriptive statistics

A total of 1548 household surveys were completed during five sample periods in 2015 and 2016. During these surveys, 1472 successful light-trap-nights captured a total of 38,895 mosquitoes (Table 1). Because aspiration capture was inconsistent and produced few adult Anopheles mosquitoes (of 7246 total aspiration captures, only 217 were Anopheles and the remainder Culex), no further analyses of these data were undertaken. Most mosquitoes (87%) collected by light trap were Culex spp., of which the majority (90%) were female Culex. For this report, no analyses of Culex mosquitoes were done as Culex do not contribute to the transmission of human malaria. A total of 4935 Anopheles spp. mosquitoes were collected using CDC light traps; female Anopheles made up 99% of the Anopheles captured (N = 4888) (Table 1).
Table 1

Summary of mosquitoes collected by CDC light traps

Sex

Genus

Trap nights

Total

Average per light-trap-night

Standard deviation

Female

Culex

1472

30,237

20.5

47.1

Anopheles

1472

4888

3.3

17.6

Aedes

1472

156

0.1

0.9

Male

Culex

1472

3526

2.4

9.7

Anopheles

1472

47

0.0

0.3

Aedes

1472

41

0.0

0.3

After identification of sex and genus by microscopy, a total of 4550 Anopheles spp. mosquitoes were tested by PCR to determine sibling species; An. arabiensis and An. funestus species were identified. Both An. arabiensis and An. funestus species were captured in the rainy and dry seasons with female An. funestus being much more abundant overall. During the rainy season, the average number of female An. funestus was 5.3 per light-trap-night compared to an average of 2.0 female An. arabiensis. During the dry season, an average of 0.3 female An. funestus were captured per light-trap-night compared to an average of 0 female An. arabiensis (Fig. 2, Table 2). While An. funestus was more abundant than An. arabiensis, both species were found on all transects, including within Blantyre city limits (Fig. 3c, d).
Fig. 2
Fig. 2

Average number of female An. arabiensis and An. funestus per light-trap-night, by season

Table 2

Summary of Anopheles spp. mosquitoes collected by CDC light traps

Sex

Species

Trap nights

Total

Average per light-trap-night

Standard deviation

Dry season

 Female

Anopheles arabiensis

845

36

0.0

0.4

Anopheles funestus

845

291

0.3

2.1

Other Anopheles

845

2

0.0

0.1

 Male

Anopheles arabiensis

861

0

0.0

0.0

Anopheles funestus

861

4

0.0

0.1

Other Anopheles

861

0

0.0

0.0

Rainy season

 Female

Anopheles arabiensis

578

1134

2.0

6.5

Anopheles funestus

578

3055

5.3

22.0

Other Anopheles

578

15

0.0

0.4

 Male

Anopheles arabiensis

611

4

0.0

0.1

Anopheles funestus

611

9

0.0

0.2

Other Anopheles

611

0

0.0

0.0

Fig. 3
Fig. 3

Distribution of a peri-domestic agriculture, b urban house density, c Female An. arabiensis, and d Female An. funestus for household clusters along an urban–rural continuum in Blantyre, Malawi

Household-level data were summarized for all 1548 households with non-missing data, and included demographic characteristics, malaria risk or prevention information, and environmental data (including peri-domestic LULC and housing structure characteristics) (Table 4). Almost two-thirds (65.6%) of study households were located within “rural” Blantyre. Households had an average of 3.6 rooms, 0.6 children under 5 years of age, and a head of the household of age 40.5 years on average. About half (52.5%) of all household heads had at least some primary education, and another third (32.3%) had some secondary education. Households owned an average of 1.6 total nets, with less than one net (0.5 nets) per person on average. Nearly two-thirds (63.7%) of respondents reported that they had slept under a bed net the night prior to the survey, and 59.2% reported that other family members had slept under a bed net the night prior to the survey (Table 4).

Households were located, on average, at 945 m above sea level, with those in urban Blantyre averaging higher elevation (1057 m) compared to rural Blantyre (886 m). Forty percent (39.9%) of household-samples occurred during the rainy season and 60.1% during the dry season. Slightly over half (53.0%) of all households were growing crops at the time of sampling (direct observation), and three quarters (73.3%) had cultivated fruit trees nearby. Approximately one-fifth (17.8%) of households were raising animals for nearby grazing, with 14.3 and 37.6% reporting ownership of goats and chickens respectively. Just 7.0% of households were located near forested areas. Most houses (88.5%) had open or partially open windows, and 90.5% had open or partially open eaves. Approximately three-quarters (74.7%) of roofs were constructed with iron sheets and most of the remainder were thatch (25.1%), with < 1% tile (Table 4).

House density within 50 m of sampled households averaged 8.3 other dwellings (satellite-derived). Households dedicated an average of 25.8% of surrounding land (within a 50 m radius) to crop production (satellite-derived). The average NDVI during the rainy season was 0.3, and 0.2 during the dry season (satellite-derived) (Table 4).

Measures of small-scale agriculture and nearby house density were heterogeneous along the urban–rural continuum in Blantyre, Malawi. Although the proportion of households producing small-scale agriculture tended to increase with distance from the city centre, there were households within Blantyre city limits engaged in small-scale crop production. Likewise, there were clusters within Blantyre city limits with low nearby-house density and clusters outside of Blantyre city limits situated in high house density (Fig. 3a, b).

Bivariate analysis

Negative binomial regression models were used to quantify the associations of explanatory variables with the number of female Anopheles mosquitoes in each household, using separate analyses for An. funestus and An. arabiensis.

Single statistically significant predictors of greater household-level abundances of both An. funestus and An. arabiensis included more children under-5 years old, use of a bed net the preceding night, greater distance from the city centre, survey during the rainy season, higher proportion of surrounding land used for cropping (satellite-derived), presence of small-scale agriculture within a 50 m radius around household (direct observation), an NDVI during the dry season of > 0.1 and ≤ 0.4, typically corresponding to shrub/grassland (satellite-derived), ownership of goats, and having a thatched roof (vs. iron sheets) (Table 3). It is of note that similar associations were observed between Anopheles spp. abundances and measures of small-scale agriculture originating from various sources, including direct observation and satellite-derived measures of NDVI and percentage of land used for cropping.
Table 3

Bivariate negative binomial models of association between species-specific mosquito abundances and various predictors

 

An. arabiensis

An. funestus

95% CI

P value

95% CI

P value

Demographics

 Number of rooms

0.8 (0.7, 0.9)

< 0.01

0.7 (0.6, 0.8)

0.0001

 Number of children under age 5

1.9 (1.3, 2.7)

< 0.001

1.9 (1.3, 2.8)

< 0.001

 Number slept in the house

1.0 (0.8, 1.1)

0.75

0.9 (0.8, 1.1)

0.42

 Age of household head (years)

0.99 (0.98, 1.0)

0.09

0.98 (0.97, 0.99)

< 0.01

 Male head of household

1.1 (0.7, 1.7)

0.78

1.3 (0.8, 2.0)

0.23

 Highest educational attainment of head of household

 

< 0.001

 

0.0001

 No formal education

Ref

 

Ref

 

 Some primary education

1.3 (0.5, 3.2)

 

2.8 (1.2, 6.9)

 

 Some secondary education

0.5 (0.2, 1.3)

 

1.6 (0.6, 4.0)

 

 Some college

0.4 (0.1, 1.2)

 

0.2 (0.1, 0.6)

 

Malaria prevention practices

 Total number of nets per household

0.8 (0.6, 0.9)

0.01

0.7 (0.6, 0.9)

< 0.001

 Respondent used a net night prior to study

2.5 (1.5, 3.9)

0.0001

2.2 (1.4, 3.4)

< 0.001

 Other family members used a net night prior to study

3.2 (2.0, 4.9)

0.0001

2.5 (1.6, 3.9)

0.0001

 Ave. number of nets per person

0.2 (0.1, 0.5)

0.0001

0.3 (0.2, 0.6)

< 0.001

Household environmental characteristics (50 m buffer)

 Within Blantyre city limits

0.1 (0.1, 0.2)

0.0001

0.02 (0.01, 0.03)

0.0001

 Increasing distance from city centre (2.5 km intervals)

1.5 (1.4, 1.7)

0.0001

1.7 (1.5, 1.9)

0.0001

Section/region

 Lunzu (1)

Ref

Ref

Ref

Ref

 Chileka (2)

1.1 (0.5, 2.6)

0.75

1.8 (0.9, 3.6)

0.08

 Chilomoni (3)

1.0 (0.4, 2.4)

0.97

1.1 (0.5, 2.1)

0.87

 Mpemba (4)

0.1 (0, 0.3)

0.0001

0.1 (0, 0.1)

0.0001

 Chigumula (5)

0.2 (0.1, 0.4)

0.0001

0.1 (0, 0.1)

0.0001

 Mikolongwe (6)

0.9 (0.4, 2.0)

0.75

0.1 (0.1, 0.2)

0.0001

 Kachere (7)

0.3 (0.1, 0.7)

< 0.01

0 (0, 0.1)

0.0001

 Machinjiri (8)

0.5 (0.2, 1.2)

0.11

0.1 (0, 0.2)

0.0001

 Rainy (vs. dry) season

46.1 (29.6, 71.6)

0.0001

15.3 (10.5, 22.4)

0.0001

 Elevation (m)

0.996 (0.995, 0.997)

0.0001

0.992 (0.991, 0.993)

0.0001

 Number nearby households

0.9 (0.9, 1.0)

0.0001

0.9 (0.8, 0.9)

0.0001

 Amount of land used for growing crops (%)

2.3 (1.2, 4.2)

< 0.01

1.8 (1.0, 3.1)

0.04

 NDVI (rainy season)

0.4 (0, 6.5)

0.50

2.6 (0.2, 30.4)

0.44

NDVI category (rainy season)

 ≤ 0.1 (barren: rock/sand/urban)

Ref

Ref

Ref

Ref

 > 0.1 and ≤ 0.4 (shrub/grassland)

2.6 (0.1, 94.7)

0.60

0.6 (0, 17.6)

0.80

 > 0.4 and ≤ 1 (temperate/tropical rainforest)

1.8 (0, 69.9)

0.74

0.8 (0, 22.3)

0.89

 NDVI (dry season)

96.5 (0, 212,362.4)

0.25

0.1 (0, 304.7)

0.53

NDVI category (dry season)

 ≤ 0.1 (barren: rock/sand/urban)

Ref

Ref

Ref

Ref

 > 0.1 and ≤ 0.4 (shrub/grassland)

19.4 (5.2, 72.1)

< .0001

111.6 (22, 566.7)

< .0001

 > 0.4 and ≤ 1 (temperate/tropical rainforest)

NA

NA

NA

NA

 Agriculture

5.8 (3.8, 8.8)

0.0001

4.0 (2.6, 6.1)

0.0001

 Fruit trees

0.9 (0.6, 1.6)

0.82

0.8 (0.5, 1.2)

0.27

 Grazing

0.6 (0.3, 1.0)

0.06

0.5 (0.3, 0.9)

0.02

 Forest

0.4 (0.2, 1.1)

0.08

0.2 (0.1, 0.4)

0.0001

 Ownership of goats

3.1 (1.7, 5.8)

< 0.001

3.9 (2.1, 7.0)

0.0001

 Ownership of chickens

1.3 (0.8, 2.1)

0.25

1.7 (1.1, 2.6)

0.03

Housing construction

 Closed (vs. fully/partially open) windows

0.9 (0.4, 1.8)

0.72

0.5 (0.2, 0.9)

0.03

 Closed (vs. fully/partially open) eaves

2.0 (1.0, 4.3)

0.06

2.2 (1.1, 4.5)

0.03

Roof type

 Iron sheets

Ref

Ref

Ref

Ref

 Thatched

3.0 (1.8, 4.9)

0.0001

4.1 (2.5, 6.6)

0.0001

 Tile

0.5 (0, 37.0)

0.74

0.6 (0, 31.2)

0.80

Italic values indicate significance of p value (p < 0.05)

Fewer mosquitoes of both species were independently associated with more rooms in the house, higher educational attainment of the household head, greater total number of bed nets, greater average number of bed nets per person, location within Blantyre city limits, location within certain sections of the study area (Mpemba, Chigumula, and Kachere), and greater nearby house density (Table 3).

Greater abundances of An. funestus alone were associated with owning chickens and having closed (vs. open or partially open) eaves. On the other hand, fewer An. funestus were associated with certain sections of the study area (Mikolongwe and Machinjiri), presence of animals for nearby grazing, location near forested areas, and having closed (vs. open or partially open) windows (Table 3). These findings may be suggestive of differences in vector behaviour.

To consider potential confounding, variables that were significantly associated with both An. funestus and An. arabiensis were further evaluated for significant relationships with the presence of small-scale agriculture. Potential confounders were determined to be the number of under 5-year-olds, educational attainment of the head of household, total number of bed nets per household, the household-average of bed nets, location within Blantyre city limits, increasing distance from city centre, rainy season, nearby house density, percentage of cropped land within a 50 m radius of the household, NDVI category during the dry season, and ownership of goats (Tables 3 and 4). Not all variables identified as potential confounders were included in the final models, as percent crop and NDVI category during the dry season were both highly correlated with the main predictor of interest, small-scale agriculture.
Table 4

Bivariate analysis of household demographics, behavioural factors, and peri-domestic environmental characteristics by presence/absence of agriculture

 

All households

Agriculture absent

Agriculture present

P value (T test or Chi square)

N

Mean or Freq

SD or %

N

Mean or Freq

SD or %

N

Mean or Freq

SD or %

Demographics

 Number of rooms

1546

3.7

1.9

726

3.8

2.1

820

3.6

1.6

0.14

 Number of children under age 5

1520

0.6

0.8

700

0.5

0.7

820

0.6

0.8

< 0.01

 Number slept in the house

1520

3.8

1.9

700

3.7

2.0

820

3.9

1.7

0.13

 Age of household head (years)

1520

40.5

16.8

700

40.7

16.4

820

40.3

17.1

0.64

 Male head of household

1520

621

40.9%

700

290

41.4%

820

331

40.4%

0.67

 Highest educational attainment of head of household

1509

  

692

  

817

  

< .0001

 No formal education

 

95

6.3%

 

35

5.1%

 

60

7.3%

 

 Some primary education

 

792

52.5%

 

315

45.5%

 

477

58.4%

 

 Some secondary education

 

487

32.3%

 

250

36.1%

 

237

29.0%

 

 Some college

 

135

8.9%

 

92

13.3%

 

43

5.3%

 

Malaria prevention practices

 Total number of nets per household

1546

1.6

1.3

726

1.7

1.4

820

1.5

1.2

0.0001

 Respondent used a net night prior to study

1548

986

63.7%

728

463

63.6%

820

523

63.8%

0.94

 Other family members used a net night prior to study

1548

917

59.2%

728

414

56.9%

820

503

61.3%

0.07

 Ave. number of nets per person

1520

0.5

0.4

700

0.5

0.4

820

0.4

0.4

< .0001

Household environmental characteristics (50 m buffer)

 Within Blantyre city limits

1548

532

34.4%

728

352

48.4%

820

180

22.0%

< .0001

 Increasing distance from city centre (2.5 km intervals)

1548

  

728

  

820

  

< .0001

 2.5 km

 

190

12.3%

 

143

19.6%

 

47

5.7%

 

 5 km

 

193

12.5%

 

132

18.1%

 

61

7.4%

 

 7.5 km

 

196

12.7%

 

115

15.8%

 

81

9.9%

 

 10 km

 

193

12.5%

 

82

11.3%

 

111

13.5%

 

 12.5 km

 

193

12.5%

 

81

11.1%

 

112

13.7%

 

 15 km

 

196

12.7%

 

61

8.4%

 

135

16.5%

 

 17.5 km

 

194

12.5%

 

51

7.0%

 

143

17.4%

 

 20 km

 

193

12.5%

 

63

8.7%

 

130

15.9%

 

Section/region

1548

  

728

  

820

  

< .0001

 Lunzu (1)

 

190

12.3%

 

113

15.5%

 

74

9.0%

 

 Chileka (2)

 

193

12.5%

 

106

14.6%

 

84

10.2%

 

 Chilomoni (3)

 

196

12.7%

 

86

11.8%

 

104

12.7%

 

 Mpemba (4)

 

193

12.5%

 

120

16.5%

 

74

9.0%

 

 Chigumula (5)

 

193

12.5%

 

77

10.6%

 

122

14.9%

 

 Mikolongwe (6)

 

196

12.7%

 

82

11.3%

 

113

13.8%

 

 Kachere (7)

 

194

12.5%

 

84

11.5%

 

115

14.0%

 

 Machinjiri (8)

 

193

12.5%

 

60

8.2%

 

134

16.3%

 

 Rainy season (vs. dry)

1548

618

39.9%

728

124

17.0%

820

494

60.2%

< .0001

 Elevation (m)

1548

945.0

175.9

728

947.2

165.3

820

943.0

184.9

0.64

 Number nearby households

1548

8.3

10.4

728

9.6

13.0

820

7.2

7.2

< .0001

 Amount of land used for growing crops (%)

1548

25.8

40.4

728

21.4

38.7

820

29.6

41.5

< .0001

 NDVI (rainy season)

1548

0.3

0.1

728

0.3

0.1

820

0.3

0.1

< .0001

 NDVI category (rainy season)

1548

  

728

  

820

  

0.20

 ≤ 0.1 (barren: rock/sand/urban)

 

4

0.3%

 

2

0.3%

 

6

0.7%

 

 > 0.1 and ≤ 0.4 (shrub/grassland)

 

1291

83.4%

 

617

84.8%

 

674

82.2%

 

 > 0.4 and ≤ 1 (temperate/tropical rainforest)

 

251

16.2%

 

107

14.7%

 

144

17.6%

 

 NDVI (dry season)

1548

0.2

0.0

728

0.2

0.0

820

0.2

0.0

< .0001

 NDVI category (dry season)

1548

  

728

  

820

  

< .0001

 ≤ 0.1 (barren: rock/sand/urban)

 

92

5.9%

 

73

10.0%

 

19

2.3%

 

 > 0.1 and ≤ 0.4 (shrub/grassland)

 

1456

94.1%

 

655

90.0%

 

801

97.7%

 

 > 0.4 and ≤ 1 (temperate/tropical rainforest)

 

0

0%

 

0

0%

 

0

0%

 

 Fruit trees

1548

1134

73.3%

728

439

60.3%

820

695

84.8%

< .0001

 Grazing

1548

275

17.8%

728

217

8.7%

820

212

25.9%

< .0001

 Forest

1548

108

7.0%

728

65

8.9%

820

43

5.2%

< 0.01

 Ownership of goats

1546

221

14.3%

726

76

10.5%

820

145

17.7%

< .0001

 Ownership of chickens

1546

582

37.6%

726

248

34.2%

820

334

40.7%

0.01

Housing construction

 Closed (vs. fully/partially open) windows

1527

175

11.5%

719

93

12.9%

808

82

10.2%

0.09

 Closed (vs. fully/partially open) eaves

1537

146

9.5%

720

76

10.6%

817

70

8.6%

0.19

Roof type

1512

  

694

  

818

  

< .0001

 Iron sheets

 

1129

74.7%

 

565

81.4%

 

564

68.9%

 

 Thatched

 

379

25.1%

 

126

18.2%

 

253

30.9%

 

 Tile

 

4

0.3%

 

3

0.4%

 

1

0.1%

 

Italic values indicate significance of p value (p < 0.05)

Multivariate analysis

Multivariate negative binomial models were used to quantify the association of small-scale agriculture and various urbanity measures with the number of female Anopheles mosquitoes in each household, adjusting for confounding. Anopheles funestus and An. arabiensis were analysed separately for a total of 1387 household-visits after excluding those with missing outcome, exposure, or risk factor information.

Small-scale agriculture and increasing distance from city centre (in 2.5 km intervals) were significantly associated with increased abundances of An. funestus and An. arabiensis, while location within Blantyre city limits and greater nearby house density were significantly associated with decreased abundances of both Anopheles species (Table 5). These relationships remained similar after adjusting for the number of bed nets per person, number of children under 5 years old, education level, and rainy/dry season; however, the effect size of small-scale agriculture on Anopheles spp. abundances generally decreased after adjustment becoming non-significant (Table 6). As expected, season was a strong predictor of Anopheles abundances; inclusion of rainy/dry season in the models attenuated the effect of agriculture on An. arabiensis and reversed the direction of the association between agriculture and An. funestus. The effects of various urbanicity measures on Anopheles spp. abundances remained stable and significant after adjusting for confounding.
Table 5

Unadjusted multivariate negative binomial models of associations between Anopheles abundances, presence of small-scale agriculture, and urbanicity measures

 

An. arabiensis

An. funestus

95% CI

P value

95% CI

P value

Model 1a

 Agriculture

5.2 (3.4, 7.9)

< .0001

2.7 (1.8, 4.0)

< .0001

 Within city limits

0.2 (0.1, 0.3)

< .0001

0.02 (0.01, 0.04)

< .0001

Model 2a 

 Agriculture

5.6 (3.7, 8.6)

< .0001

3.4 (2.3, 5.1)

< .0001

 Increasing distance

1.4 (1.3, 1.6)

< .0001

1.6 (1.4, 1.7)

< .0001

Model 3a

 Agriculture

5.5 (3.6, 8.4)

< .0001

3.7 (2.4, 5.6)

< .0001

 House densitya

0.5 (0.4, 0.8)

0.001

0.3 (0.2, 0.4)

< .0001

Italic values indicate significance of p value (p < 0.05)

aUnits are an additional 10 households within a 50 m radius of the sampled household

Table 6

Multivariate negative binomial models of associations between Anopheles abundances, presence of small-scale agriculture, and urbanicity measures, adjusted for number of nets per person, number of children under 5 years old, education level, and rainy season

 

An. arabiensis

An. funestus

95% CI

P value

95% CI

P value

Model 1b

 Agriculture

1.4 (0.8, 2.2)

0.21

0.6 (0.4, 0.9)

0.01

 Within city limits

0.2 (0.1, 0.2)

< .0001

0.02 (0.01, 0.04)

< .0001

Model 2b

 Agriculture

1.6 (1.0, 2.5)

0.07

0.7 (0.4, 1.2)

0.19

 Increasing distance

1.3 (1.2, 1.5)

< .0001

1.4 (1.3, 1.6)

< .0001

Model 3b

 Agriculture

1.5 (1.0, 2.5)

0.08

0.7 (0.5, 1.2)

0.21

 House densitya

0.5 (0.4, 0.7)

< 0.001

0.2 (0.1, 0.3)

< .0001

Italic values indicate significance of p value (p < 0.05)

aUnits are an additional 10 households within a 50 m radius of the sampled household

Interactions were assessed between the main effect, presence of small-scale agriculture, and various urbanicity measures. A significant positive interaction was observed between agriculture and “urban” (within Blantyre city limits), while a significant negative interaction was observed between agriculture and increasing distance from city centre (increasingly rural) for An. arabiensis only (Table 7). These findings imply that the presence of small-scale agriculture is more predictive of An. arabiensis abundance at houses within Blantyre city limits and for houses increasingly close to Blantyre city centre. There was no significant interaction found between small-scale agriculture and nearby house density for either An. arabiensis or An. funestus.
Table 7

Multivariate negative binomial models of associations and interactions between Anopheles abundances, presence of small-scale agriculture, and urbanicity measures

 

An. arabiensis

An. funestus

95% CI

P value

95% CI

P value

Model 1c

 Agriculture

3.8 (2.3, 6.2)

< .0001

2.7 (1.7, 4.2)

< .0001

 Within city limits

0.1 (0, 0.2)

< .0001

0.02 (0.01, 0.05)

< .0001

 Agriculturea within city limits

3.4 (1.2, 9.6)

0.02

1.0 (0.4, 2.8)

1.00

Model 2c

 Agriculture

15.6 (5.4, 45.2)

< .0001

8.2 (2.9, 23.8)

< .0001

 Increasing distance

1.6 (1.4, 1.8)

< .0001

1.7 (1.5, 2.0)

< .0001

 Agriculturea increasing distance

0.8 (0.7, 1.0)

0.04

0.8 (0.7, 1.0)

0.07

Model 3c

 Agriculture

5.3 (2.7, 10.5)

<.0001

4.2 (2.0, 8.9)

0.0001

 Housing density

0.5 (0.3, 0.9)

0.02

0.3 (0.2, 0.6)

< 0.001

 Agriculturea house densitya

1.0 (0.5, 2.2)

0.91

0.8 (0.3, 2.0)

0.65

Italic values indicate significance of p value (p < 0.05)

aUnits are an additional 10 households within a 50 m radius of the sampled household

Discussion

The reasons why malaria persists in many urbanizing areas of sub-Saharan Africa are multifaceted and not well understood. One key question is whether incident cases in urban settings are resulting from transmission there, or from infection acquired during travel to more rural settings, which then is transported back to urban residences. Understanding such drivers of malaria risk is critical in contexts experiencing rapid urbanization, such as Malawi, where the urban growth rate is 4% per annum [4]. Malaria prevention among the ~ 3.2 million (17%) of Malawi’s population living in an urban setting is limited by inadequate knowledge of what determines risk [4]. One challenge is the diverse and imprecise definitions of “urban” or “rural”, which may be misleading and can obscure local heterogeneity across the risk landscape. To understand what constitutes risk may be further complicated by uneven urbanization, making it difficult to prioritize where resources and interventions should be directed. Results from this study demonstrate that small-scale crop production and other peri-domestic environmental factors are major influences on the local abundance of malaria vectors, even in high-density urban areas.

While An. funestus and An. arabiensis were often associated with similar risk factors, several species-specific risk factors were also identified, implying that different strategies may need to be utilized to address species-specific malaria risk. Greater An. funestus and An. arabiensis abundances inside households were predicted by the presence of more under 5-year-olds, greater distance from the city centre, rainy season, more peri-domestic land used for crop production, an NDVI during the dry season of > 0.1 and ≤ 0.4, typically corresponding to shrub/grassland, but which could also reflect the presence of small-scale agriculture in this setting, goat ownership, and having a thatched vs. iron or tile roof. These associations are generally consistent with what has been seen in other similar SSA high-transmission settings and have been explained by various biological and behavioural pathways [3538]. More An. funestus alone were predicted by chicken ownership and having closed (vs. open or partially open) eaves, suggestive of differences in species behaviour and species-specific risk.

Fewer mosquitoes of both species were independently predicted in households with more rooms, a higher level of educational attainment of the household head, location within Blantyre city limits, location in certain sections of the study area, and higher nearby house density. Plausible mechanisms for these associations have also been proposed in other studies, and mostly involve physical or knowledge-based relationships to mosquito breeding or household access [36, 37]. Fewer An. funestus only were associated with other sections of the study area, presence of animals for nearby grazing, location near forested areas, and having closed (vs. open or partially open) windows [39].

The number of bed nets per household and their reported use were associated differently with vector abundance. More An. funestus and An. arabiensis were observed in households with greater bed net use the night preceding the survey; however, fewer mosquitoes of both species were observed in households where more total bed nets were present, and with a higher number of bed nets per person. These findings are provocative and suggest that people more readily use nets when mosquitoes are more obvious or annoying, while the presence of more nets inside the dwelling, regardless of their nighttime use, may reduce survival or repel mosquitoes from these indoor settings. McCann et al. found that indoor Anopheles density decreased with increasing LLIN use when analysed categorically, which is contrary to the findings from this study [40]. One possible explanation is the cross-sectional nature of our study design. In areas where mosquitoes are more abundant, people may tend to use mosquito nets more frequently or consistently; however, it is not possible to definitely assess the direction of the association from this study alone.

As expected, the effect of seasonality on Anopheles abundances was large and significant, and impacted the other observed effects. Season attenuated the model effect size of small-scale agriculture on An. arabiensis and reversed the direction of the association between small-scale agriculture and An. funestus. Seasonality is a well-known predictor of Plasmodium transmission, as heavy rains in these settings can often allow for Anopheles breeding habitats to expand [35].

Finally, small-scale subsistence agriculture was found to be associated with greater Anopheles spp. abundance in urban and peri-urban Blantyre, even after adjusting for degree of urbanicity and other confounders. This suggests that small-scale agriculture is an important risk factor for greater malaria vector abundance, even in urbanized areas. Furthermore, results demonstrated that small-scale agriculture was more important to Anopheles spp. abundance in “urban” households located within city limits, as evidenced by significant interaction terms between small-scale agriculture and urbanicity measures. In other words, small-scale agriculture is more predictive of Anopheles spp. presence in households located within city limits and at distances closer to the city centre, but less predicative of Anopheles spp. presence in households located outside of Blantyre city limits and at distances further from the city centre. This observation implies there are additional factors at play in more rural area households which were not adequately captured in this study.

Conclusion

The role of environmental characteristics, particularly small-scale agriculture, in the reproduction and survival of malaria vectors in urban habitats is critical, yet still enigmatic. Findings from this study indicate that poverty, poor quality housing, and small-scale agriculture in urban settings contribute to conditions that amplify anopheline mosquito abundance, particularly for adaptable species such as An. funestus, and may thereby augment the risk of urban transmission. Household-level and peri-domestic environmental characteristics found to be associated with malaria vector abundance were identified by characterizing the presence of Anopheles species along an urban–rural continuum in this highly endemic transmission setting. These insights contribute to a better understanding of heterogeneous risk along the urban–rural continuum that impact on local Plasmodium transmission, and that require elucidation for malaria-prevention efforts to become more effective.

Abbreviations

AIC: 

Akaike information criterion

CDC: 

Centers for Disease Control and Prevention

DEM: 

digital elevation model

DNA: 

deoxyribonucleic acid

GIS: 

geographic information systems

GPS: 

global positioning system

ICEMR: 

International Center of Excellence for Malaria Research

IRB: 

Institutional Review Board

LLIN: 

long-lasting insecticidal net

NIR: 

near infrared

NDVI: 

normalized difference vegetation index

PCR: 

polymerase chain reaction

SAS: 

Statistical Analysis System

SSA: 

Sub-Saharan Africa

WHO: 

World Health Organization

Declarations

Authors’ contributions

ND assisted with data and sample collection, completed data cleaning and all statistical and spatial analyses, and drafted the manuscript. TM contributed to the design and execution of the transect study, as well as providing critical review of the manuscript. CK managed the Surveillance of Wild Anopheles Transmitters (SWAT) field team and the data collection process, as well as the processing of all mosquito samples and data entry. AB managed the database for the study as well as ensuring quality control of the data. DPM assisted with conception of study hypotheses and designed and managed the study. CD performed the molecular testing, drafted the laboratory methods section, and provided critical review of the manuscript. EDW contributed to the design of the study, the IRB approval processes, the interpretation of results, and provided critical review of the manuscript. MLW assisted with conception of study hypotheses, statistical analyses, and interpretations, and provided critical review of the manuscript. All authors read and approved the final manuscript.

Acknowledgements

The authors thank the many Malawian families who allowed us to disrupt their lives to study them and their households. The careful efforts of the Surveillance of Wild Anopheles Transmitters (SWAT) field team, the Entomology Laboratory assistants from the Malaria Alert Centre, and Dr. Terrie Taylor from the College of Osteopathic Medicine at Michigan State University are gratefully acknowledged. In addition, the efforts of Malawi International Center of Excellence for Malaria Research (ICEMR) Molecular Core laboratory technicians in collecting and processing mosquito samples, along with those of Dr. Karl Seydel who supervises the Molecular Core, are most appreciated.

Competing interests

The authors declare that they have no competing interests.

Availability of data and materials

The datasets used and/or analysed during the current study are available from the corresponding author on reasonable request.

Consent for publication

Not applicable.

Ethics approval and consent to participate

This study was approved by the independent Institutional Review Boards (IRB) of the University of Malawi College of Medicine, Michigan State University, and the College of Medicine at the University of Malawi. Informed consent was obtained by an adult from each household included in this study.

Funding

Funding was provided by 4U19AI089683 for the Malawi ICEMR. Additional support for NFD came from the Office of Global Public Health (School of Public Health) and the International Institute, both at the University of Michigan.

Publisher’s Note

Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

Open AccessThis article is distributed under the terms of the Creative Commons Attribution 4.0 International License (http://creativecommons.org/licenses/by/4.0/), which permits unrestricted use, distribution, and reproduction in any medium, provided you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license, and indicate if changes were made. The Creative Commons Public Domain Dedication waiver (http://creativecommons.org/publicdomain/zero/1.0/) applies to the data made available in this article, unless otherwise stated.

Authors’ Affiliations

(1)
Department of Epidemiology, School of Public Health, University of Michigan, Ann Arbor, MI, USA
(2)
Malaria Alert Centre, College of Medicine, University of Malawi, Blantyre, Malawi
(3)
College of Medicine, University of Malawi, Blantyre, Malawi
(4)
Malawi International Center of Excellence for Malaria Research (ICEMR) Molecular Core Laboratory, University of Malawi College of Medicine, Blantyre, Malawi
(5)
Department of Microbiology & Molecular Genetics, Michigan State University, East Lansing, MI, USA

References

  1. WHO. World malaria report. Geneva: World Health Organization; 2017.Google Scholar
  2. Demographic and Health Surveys. Malawi malaria indicator survey. Rockville: Demographic and Health Surveys; 2017.Google Scholar
  3. The World factbook: Malawi (Washington, DC: Central Intelligence Agency). https://www.cia.gov/library/publications/the-world-factbook/index (2018). Accessed 12 May 2018.
  4. National Statistical Office. Population and housing census: preliminary, report. Malawi: National Statistical Office; 2008.Google Scholar
  5. Hay S, Guerra C, Tatem A, Atkinson P, Snow R. Urbanization, malaria transmission and disease burden in Africa. Nat Rev Microbiol. 2005;3:81–90.View ArticlePubMedPubMed CentralGoogle Scholar
  6. Holtz T, Marum L, Mkandala C, Chizani N, Roberts J, Macheso A, et al. Insecticide-treated bednet use, anaemia, and malaria parasitaemia in Blantyre District, Malawi. Trop Med Int Health. 2002;7:220–30.View ArticlePubMedGoogle Scholar
  7. Monasch R, Reinisch A, Steketee R, Korenromp E, Alnwick D, Bergevin Y. Child coverage with mosquito nets and malaria treatment from population-based surveys in African countries: a baseline for monitoring progress in Roll Back Malaria. Am J Trop Med Hyg. 2004;71(Suppl 2):232–8.PubMedGoogle Scholar
  8. Pond B. Malaria indicator surveys demonstrate a markedly lower prevalence of malaria in large cities of sub-Saharan Africa. Malar J. 2013;12:313.View ArticlePubMedPubMed CentralGoogle Scholar
  9. Bousema T, Griffin J, Sauerwein R, Smith D, Churcher T, Takken W, et al. Hitting hotspots: spatial targeting of malaria for control and elimination. PLoS Med. 2012;9:e1001165.View ArticlePubMedPubMed CentralGoogle Scholar
  10. Trape J, Lefebvre-Zante E, Legros F, Ndiaye G, Bouganali H, Druilhe P, et al. Vector density gradients and the epidemiology of urban malaria in Dakar, Senegal. Am J Trop Med Hyg. 1992;47:181–9.View ArticlePubMedGoogle Scholar
  11. Knudsen A, Slooff R. Vector-borne disease problems in rapid urbanization: new approaches to vector control. Bull World Health Organ. 1992;70:1–6.PubMedPubMed CentralGoogle Scholar
  12. Keating J, MacIntyre K, Mbogo C, Githeko A, Regens J, Swalm C, et al. A geographic sampling strategy for studying relationships between human activity and malaria vectors in urban Africa. Am J Trop Med Hyg. 2003;68:357–65.PubMedGoogle Scholar
  13. Qi Q, Guerra C, Moyes C, Elyazar I, Gething P, Hay S, et al. The effects of urbanization on global Plasmodium vivax malaria transmission. Malar J. 2012;11:403.View ArticlePubMedPubMed CentralGoogle Scholar
  14. De Silva P, Marshall J. Factors contributing to urban malaria transmission in sub-Saharan Africa: a systematic review. J Trop Med. 2012;2012:819563.View ArticlePubMedPubMed CentralGoogle Scholar
  15. Lindsay S, Campbell H, Adiamah J, Greenwood A, Bangali J, Greenwood B. Malaria in a peri-urban area of the Gambia. Ann Trop Med Parasitol. 1990;84:553–62.View ArticlePubMedGoogle Scholar
  16. Donnelly M, McCall P, Lengeler C, Bates I, D’Alessandro U, Barnish G, et al. Malaria and urbanization in sub-Saharan Africa. Malar J. 2005;4:12.View ArticlePubMedPubMed CentralGoogle Scholar
  17. Klinkenberg E, McCall P, Wilson M, Amerasinghe F, Donnelly M. Impact of urban agriculture on malaria vectors in Accra, Ghana. Malar J. 2008;7:151.View ArticlePubMedPubMed CentralGoogle Scholar
  18. Afrane Y, Klinkenberg E, Drechsel P, Owusu-Daaku K, Garms R, Kruppa T. Does irrigated urban agriculture influence the transmission of malaria in the city of Kumasi, Ghana? Acta Trop. 2004;89:125–34.View ArticlePubMedGoogle Scholar
  19. Matthys B, N’Goran E, Koné M, Koudou B, Vounatsou P, Cissé G, et al. Urban agricultural land use and characterization of mosquito larval habitats in a medium-sized town of Côte d’Ivoire. J Vector Ecol. 2006;31:319–33.View ArticlePubMedGoogle Scholar
  20. United Nations Population Division. World urbanization prospects: the 2001 revision. New York: United Nations Population Division; 2002.Google Scholar
  21. Vlahov D, Galea S. Urbanization, urbanicity, and health. J Urban Health. 2002;79(Suppl 1):1–12.View ArticlePubMed CentralGoogle Scholar
  22. McDade T, Adair L. Defining the ‘urban’ in urbanization and health: a factor analysis approach. Soc Sci Med. 2001;53:55–70.View ArticlePubMedGoogle Scholar
  23. Dahly D, Adair L. Quantifying the urban environment: a scale measure of urbanicity outperforms the urban-rural dichotomy. Soc Sci Med. 2007;67:1407–19.View ArticleGoogle Scholar
  24. Mathanga D, Kapito Tembo A, Mzilahowa T, Bauleni A, Mtimaukenena K, Taylor T, et al. Patterns and determinants of malaria risk in urban and peri-urban areas of Blantyre, Malawi. Malar J. 2016;15:590.View ArticlePubMedPubMed CentralGoogle Scholar
  25. Walker E, Mathanga D, Wilson ML, Mzilahowa T, Taylor T, Kapito-Tembo A. Anopheles mosquito abundance along an urban-to-rural gradient in Blantyre. Protocol. 2014;1–20.Google Scholar
  26. John W. Hock (Gainesville, Florida). Improved prokopack aspirator. Model 1419. 2009.Google Scholar
  27. John W. Hock (Gainesville, Florida). CDC miniature light trap. Model 512. 2012.Google Scholar
  28. Gillies M, Coetzee M. A supplement to the Anophelinae of Africa south of the Sahara (Afrotropical Region). South African Inst Med Res. 1987;55:1–143.Google Scholar
  29. ASTER Global Digital Elevation Model. NASA JPL. 2009. https://doi.org/10.5067/aster/astgtm.002. Accessed 18 May 2018.
  30. Landsat 8 OLI/TIRS C1 Level-1. USGS. Accessed 18 May 2018.Google Scholar
  31. Kellndorfer J, Cartus O, Bishop J, Walker W, Holecz F. Large scale mapping of forests and land cover with synthetic aperture radar data. In: Holecz F, Pasquali P, Milisavljevic N, Closson D, editors. Land applications of radar remote sensing. Rijeka: InTech; 2014. p. 59–94.Google Scholar
  32. Ke Y, Im J, Lee J, Gong H, Ryu Y. Characteristics of Landsat 8 OLI-derived NDVI by comparison with multiple satellite sensors and in situ observations. Rem Sens Environ. 2015;164:298–313.View ArticleGoogle Scholar
  33. Roy D, Wulder M, Loveland T, Woodcock C, Allen R, Anderson M, et al. Landsat-8: science and product vision for terrestrial global change research. Rem Sens Environ. 2014;145:154–72.View ArticleGoogle Scholar
  34. ARSET Advanced NDVI Webinar Series. NASA. 2016.Google Scholar
  35. Kelley-Hope L, Hemingway J, McKenzie F. Environmental factors associated with the malaria vectors Anopheles gambiae and Anopheles funestus in Kenya. Malar J. 2009;8:268.View ArticleGoogle Scholar
  36. Ghebreyesus T, Haile M, Witten K, Getachew A, Yohannes M, Lindsay S, et al. Household risk factors for malaria among children in the Ethiopian highlands. Trans R Soc Trop Med Hyg. 2000;94:17–21.View ArticlePubMedGoogle Scholar
  37. Kirby M, Green C, Milligan P, Sismanidis C, Jasseh M, Conway D, et al. Risk factors for house-entry by malaria vectors in a rural town and satellite villages in The Gambia. Malar J. 2008;7:2.View ArticlePubMedPubMed CentralGoogle Scholar
  38. Peterson I, Borrell L, El-Sadr W, Teklehaimanot A. Individual and household level factors associated with malaria incidence in a Highland Region of Ethiopia: a multilevel analysis. Am J Trop Med Hyg. 2009;80:103–11.PubMedGoogle Scholar
  39. Mzilahowa T, Luka-Banda M, Uzalili V, Mathanga D, Campbell C, Mukaka M, et al. Risk factors for Anopheles mosquitoes in rural and urban areas of Blantyre District, southern Malawi. Malawi Med J. 2016;28:154–8.View ArticlePubMedPubMed CentralGoogle Scholar
  40. McCann R, Messina J, MacFarlane D, Bayoh M, Gimnig J, Giorgi E, et al. Explaining variation in adult Anopheles indoor resting abundance: the relative effects of larval habitat proximity and insecticide-treated bed net use. Malar J. 2017;16:288.View ArticlePubMedPubMed CentralGoogle Scholar

Copyright

Comments

By submitting a comment you agree to abide by our Terms and Community Guidelines. If you find something abusive or that does not comply with our terms or guidelines please flag it as inappropriate. Please note that comments may be removed without notice if they are flagged by another user or do not comply with our community guidelines.

Advertisement