Risk Factors Associated with Malaria Infection Identified through Reactive Case Detection in Zanzibar, 2012-2019

doi:10.21203/rs.3.rs-936591/v1

Download PDF

Research

Risk Factors Associated with Malaria Infection Identified through Reactive Case Detection in Zanzibar, 2012-2019

https://doi.org/10.21203/rs.3.rs-936591/v1

This work is licensed under a CC BY 4.0 License

Journal Publication

published 01 Dec, 2021

Read the published version in Malaria Journal →

You are reading this latest preprint version

Background

Over the past two decades, Zanzibar substantially reduced malaria burden. As malaria decreases, sustainable improvements in prevention and control interventions may increasingly depend on accurate knowledge of malaria risk factors to further target interventions. This study aimed to investigate the risk factors associated with malaria infection in Zanzibar.

Methods

We analyzed surveillance data from Zanzibar’s Malaria Case Notification system collected between August 2012 and December 2019. This system collects data from all malaria cases passively detected and reported by public and private health facilities, from household-based follow-up and reactive case detection activities linked to those primary cases. All members of households of the passively detected malaria cases were screened for malaria using a malaria rapid diagnostic test (mRDT); individuals with a positive mRDT result were treated with artemisinin-based combination therapy. Univariate and multivariate logistic regression analyses were done to investigate the association between mRDT positivity among the household members and explanatory factors (i.e., age, sex, history of fever, history of travel, rainfall, long-lasting insecticidal net [LLIN] density, LLIN use, household indoor residual spraying [IRS], and household location) with adjustment for seasonality

Results

The findings show that younger age (p-value for trend<0.001), history of fever in the last two weeks (odds ratio [OR]=32.0; 95% CI: 29.1-35.3), history of travel outside Zanzibar in the last 30 days (OR=2.3; 95% CI: 2.1-2.6) and living in Unguja (OR=1.2; 95% CI: 1.1-1.3) were independently associated with increased odds of mRDT positivity. In contrast, male sex (OR=0.8; 95% CI: 0.7-0.9), having higher household LLIN density (p-value for trend<0.001), sleeping under an LLIN the previous night (OR=0.8; 95% CI: 0.7-0.9), and living in a household that received IRS in the last 12 months (OR=0.9; 95% CI: 0.8-0.9) were independently associated with reduced odds of mRDT positivity. A significant effect modification of combining IRS and LLIN was also noted (OR=0.7; 95% CI:0.6-0.8).

Conclusions

The findings suggest that vector control remains an important malaria prevention intervention: they underscore the need to maintain universal access to LLINs, the persistent promotion of LLIN use, and application of IRS. Additionally, enhanced behavioral change and preventive strategies targeting children aged 5-14 years and travelers are needed.

Infectious Diseases

Malaria

travel

vector control

elimination

Zanzibar

Over the past two decades, Zanzibar substantially reduced malaria burden, with parasite prevalence decreasing from more than 30% in 2005 to 0.2% in 2017 [1]. These gains followed the introduction and rapid scale-up of malaria rapid diagnostic tests (mRDTs), artemisinin-based combination therapies (ACTs), long-lasting insecticidal nets (LLINs) and indoor residual spraying (IRS) of households with insecticide [2, 3]. Building on these achievements, Zanzibar is now pursuing malaria elimination by 2023 by maintaining high population-level coverage of malaria interventions, as well as by reinforcing malaria surveillance to actively investigate and classify 100% of confirmed malaria cases [4].

Numerous studies in Africa have examined the association of various demographic and behavioural factors with malaria outcomes. These studies have found that adolescent age, male sex, not sleeping under an LLIN the previous night, and the presence of fever in the two weeks preceding the study were positively associated with malaria infection [5, 6]. Similarly, several studies have shown LLIN ownership and use, and/or the application of IRS to be associated with a significant protective effect against malaria infection [5–8]. A recent literature review and meta-analysis of 22 studies investigating travel as a risk factor for malaria infection in sub-Saharan Africa showed that travel—within as well as to and from another country—was an important risk factor in many settings [9].

As malaria decreases, sustainable improvements in prevention and control interventions may increasingly depend on accurate knowledge of malaria risk factors. For Zanzibar to accelerate towards malaria elimination, it is crucial to obtain detailed knowledge of risk factors associated with malaria infection. Such knowledge will help to confirm whether currently implemented preventive and control measures are effective, as well as indicate programmatic areas where additional efforts should be targeted or reinforced.

Study setting and data sources

The archipelago of Zanzibar is located between longitudes 39.19793 and latitudes − 6.16394, 25–50 kilometers off the east coast of the Tanzania mainland in the Indian Ocean. There are two main islands—Pemba and Unguja—which cover a total land area of 2,461 km² and have an estimated population of 1,303,569 people [10]. There are two main rainfall seasons, a primary (March–May) and a secondary (November–December) season; rainfall is at its lowest in July. Both rainfall seasons are followed by peak malaria transmission seasons, with the highest malaria case count observed in the March–May rainfall season. We conducted a cross-sectional analysis of routine data collected by Zanzibar’s Malaria Case Notification (MCN) system between August 2012 and December 2019. The MCN system was established in August 2012 with the goal to collect data from all passively detected malaria cases (which we define as primary index cases); it started in 52 public health facilities and has since progressively scaled-up to 189 public and 124 private health facilities. Case-based follow-up and reactive case detection (RCD) activities, including household-based screening and treatment (HSaT), are linked to index cases through the Coconut surveillance platform (https://coconutsurveillance.org/). Figure 1 (map) shows the location of the households of individuals included in the analysis between 2012 and 2019.

Data collection

The MCN system collects and reports details of individual (index) malaria cases passively diagnosed by microscopy or mRDT at health facilities in real-time using mobile phones and Android tablets to a central database. Once the Council Malaria Surveillance Officer (CMSO) is notified by the health facility worker, surveillance data are collected using an electronic, standardized questionnaire completed by CMSO during routine reactive case follow-up and detection at households of primary index cases. Household members are revisited if not found at their households during the first household visit. The goal of RCD is to test all household members of the primary index cases for malaria infection, regardless of presence or absence of symptoms, using mRDTs (in most cases and years the SD Bioline® HRP2 / pLDH RDT from Standard Diagnostics, Giheung-ku, Republic of Korea, was used, others ever used are First Response® Malaria Antigen pLDH/HRP2 Combo card test from Premier Medical Corporation Private Limited, Gujarat, India and Paracheck® Pf - Rapid Test for P. Falciparum Malaria Device (Ver. 3) from Orchid Biomedical Systems, Goa, India); members with a positive mRDT result are treated with an ACT following the national malaria diagnosis and treatment guidelines [11]. The specific variables collected are individual factors (i.e., age, sex, self-reported history of travel in the last 30 days, self-reported history of fever in the last two weeks, mRDT positivity, and LLIN use the previous night); household factors (i.e., number of people residing in the household, number of household LLINs, and IRS application in the last 12 months); and geographical factors (i.e., household geolocation and weekly rainfall). During the study period, rainfall data were obtained from 11 meteorological stations managed by Zanzibar’s Tanzania Meteorological Agency, with rainfall data measured in millimeters (mm) and recorded daily.

Inclusion and exclusion criteria

Inclusion criteria included the index case’s household members identified during routine RCD efforts (i.e., through follow-up of primary index cases) and tested for malaria infection with an mRDT. Individuals were excluded from the analysis if they had missing data for any of the socio-demographic and clinical variables (i.e., individual factors) collected during the RCD efforts. Figure 2 shows the breakdown of included and excluded individuals in the analysis.

Data analysis

Data analysis was performed using STATA (Version 16, College Station, TX, USA). Net density was calculated by dividing the reported number of LLINs in a household by the number of people residing in the household. Daily rainfall data were aggregated into weekly district-level rainfall data, which was transformed into a continuous variable in 10 mm increments [8]. To account for the lag time between rainfall and malaria cases, we applied the rainfall data for the previous 9 weeks to the current week [12].

Univariate and multivariable logistic regression analyses were conducted to assess the association of mRDT positivity and explanatory factors (i.e., age, sex, self-reported fever, net density, travel history, household characteristics, zone, and weekly rainfall). We included the month of household reactive case investigation in the multivariable logistic regression to account for variability arising from malaria seasonality. We used backward stepwise multiple logistic regression to identify independent risk factors for mRDT positivity while controlling for potential confounders; the interaction between IRS and LLIN use was also assessed. Lastly, multicollinearity of the model was assessed using variance inflation factor (VIF) for the independent variables, with a VIF less than 5 indicating the model is not biased by multicollinearity of its independent variables. Sensitivity analysis was also undertaken on the univariate logistic regression excluding all the individuals with one or more missing explanatory variables (N = 64,073).

Characteristics of study population

Between August 2012 and December 2019, 33,878 primary index malaria cases were reported by health facilities through the MCN system; overall, an index case investigation and household RCD was completed for 21,343 (63%) index case households. In these 21,343 index case households, 85,318 individuals were investigated, of whom varying numbers were excluded from the analyses due to missing malaria test, socio-demographic and clinical data (Fig. 1). Among the 82,021 individuals with malaria test results, the majority were: malaria negative (95.3%); female (51.7%); and living on Unguja Island (74.1%). The mean (standard deviation [sd]) age was 21.7 (17.0) years. Out of 21,343 households, 58.5% reported having received IRS in the previous year. Overall, the median weekly rainfall was 7.7 mm (inter quartile range, IQR: 0.0–31.4 mm).

Univariate analysis

Factors significantly associated with increased odds of mRDT positivity were younger age (p-value for trend < 0.001); self-reported history of fever during the last two weeks (odds ratio [OR] = 27.9; 95% CI: 25.7–30.5); history of travel outside Zanzibar during the last 30 days (OR = 2.1; 95% CI: 1.9–2.3); having more people in the household who travelled outside Zanzibar during the last 30 days (p-value for trend < 0.001), and living on Unguja Island (OR = 1.2; 95% CI: 1.1–1.3). Factors significantly associated with reduced odds of mRDT positivity were being male (OR = 0.9; 95% CI: 0.8–0.9); having a higher household net density (p-value for trend < 0.001); sleeping under an LLIN the previous night (OR = 0.7; 95% CI: 0.7–0.8); and living in a household that received IRS in the last 12 months (OR = 0.8; 95% CI: 0.8–0.9)) (Table 1). Sensitivity analysis excluding all the individuals with one or more missing explanatory variable produced similar results (data not shown).

Table 1

Univariate logistic regression analysis of risk factors associated with a positive malaria rapid diagnostic test
Explanatory variable		Total tested	Malaria positive	Malaria positivity (%)	Odds Ratio (95% CI)		P-value	P-trend
Individual factors
Sex (N = 80,532)	Female	41,623	2,103	5.1	1.0
Sex (N = 80,532)	Male	38,909	1,714	4.4	0.9 (0.8–0.9)		< 0.001
Age group (N = 80,275)	≥ 45 years	9,207	181	2.0	1.0			< 0.001
	35–44 years	9,016	266	3.0	1.5 (1.3–1.8)		< 0.001
	25–34 years	11,816	580	4.9	2.6 (2.2-3.0)		< 0.001
	15–24 years	16,887	838	5.0	2.6 (2.2–3.1)		< 0.001
	5–14 years	21,948	1,311	6.0	3.2 (2.7–3.7)		< 0.001
	< 5 years	11,401	642	5.6	2.9 (2.5–3.5)		< 0.001
Self-reported fever in last two weeks (N = 73,273)	No	70,384	2,151	3.1	1.0
Self-reported fever in last two weeks (N = 73,273)	Yes	2,889	1,354	46.9	27.9 (25.7–30.5)		< 0.001
History of travel during last 30 days (N = 76,970)	No	55,267	2,103	3.8	1.0			< 0.001
	Within Zanzibar	2,360	66	2.8	0.7 (0.6–0.9)		0.012
	Outside Zanzibar	19,343	1,506	7.8	2.1 (1.9–2.3)		< 0.001
Number of household members who traveled outside Zanzibar (N = 82,021)	None	48,966	1,751	3.6	1.0			< 0.001
	1	10,977	503	4.6	1.3 (1.2–1.4)		< 0.001
	≥ 2	22,078	1,569	7.1	2.1 (1.9–2.2)		< 0.001
Slept under an LLIN the previous night (N = 74,119)	No	29,316	1,658	5.7	1.0
Slept under an LLIN the previous night (N = 74,119)	Yes	44,803	1,864	4.2	0.7 (0.7–0.8)		< 0.001
Household factors
Net density (N = 75,789)	No nets	14,689	858	5.8	1.0			< 0.001
	0.1–0.4	43,518	2,187	5.0	0.9 (0.8–0.9)		< 0.001
	> 0.5	17,582	536	3.0	0.5 (0.5–0.6)		< 0.001
IRS in the last 12 months (N = 82,021)	No	21,934	1,173	5.3	1.0
IRS in the last 12 months (N = 82,021)	Yes	60,087	2,650	4.4	0.8 (0.8–0.9)		< 0.001
Geographical factors
Zone (N = 82,021)	Pemba	21,615	903	4.2	1.0
Zone (N = 82,021)	Unguja	60,406	2,920	4.8	1.2 (1.1–1.3)		< 0.001
Total weekly rainfall for previous 9 weeks (N = 82,021)	Increments of 10mm					1.0 (0.9-1.0)	0.886
CI, confidence interval; LLIN, long-lasting insecticidal net; IRS, indoor residual spraying of households with insecticide

Multivariate analysis

The VIF was 1.9, suggesting low correlation between the independent variables (explanatory factors). Factors independently associated with increased odds of malaria positivity were younger age (p-value for trend < 0.001); self-reported history of fever during the last two weeks (OR = 32.0; 95% CI: 29.1–35.3); history of travel outside Zanzibar during the last 30 days (OR = 2.3; 95% CI: 2.1–2.6); and living on Unguja Island (OR = 1.2; 95%CI: 1.1–1.3). Factors independently associated with reduced odds of mRDT positivity were being male (OR = 0.8; 95% CI: 0.7–0.9); having higher household net density (p-value for trend < 0.001); sleeping under an LLIN the previous night (OR = 0.8; 95% CI: 0.7–0.9); and living in a household that received IRS in the last 12 months (OR = 0.9; 95% CI: 0.8–0.9). There was significant effect modification when IRS in the last 12 months and sleeping under an LLIN the previous night were combined (OR = 0.7; 95% CI:0.6–0.8) (Table 2).

Table 2

Multivariate logistic regression analysis of risk factors associated with a positive malaria rapid diagnostic test (N = 64,073)
Explanatory variable		Odds Ratio (95% CI)	P-value	P-trend
Individual factors
Sex	Female	1.0
Sex	Male	0.8 (0.7–0.9)	< 0.001
Age group	≥ 45 years	1.0		< 0.001
	35–44 years	1.5 (1.2–1.8)	0.001
	25–34 years	2.2 (1.8–2.7)	< 0.001
	15–24 years	2.4 (1.9–2.9)	< 0.001
	5–14 years	3.2 (2.6–3.8)	< 0.001
	< 5 years	2.6 (2.1–3.1)	< 0.001
Self-reported fever in last two weeks	No	1.0
Self-reported fever in last two weeks	Yes	32.0 (29.1–35.3)	< 0.001
History of travel during last 30 days	No	1.0		< 0.001
	Within Zanzibar	0.7 (0.5–0.9)	0.019
	Outside Zanzibar	2.3 (2.1–2.6)	< 0.001
Malaria prevention
Net density	No nets	1.0		< 0.001
	0.1–0.4	0.8 (0.7–0.9)	< 0.001
	> 0.5	0.4 (0.4–0.5)	< 0.001
Slept under an LLIN the previous night	No	1.0
Slept under an LLIN the previous night	Yes	0.8 (0.7–0.9)	0.027
IRS in the last 12 months	No	1.0
IRS in the last 12 months	Yes	0.9 (0.8–0.9)	0.025
Interaction term: Slept under an LLIN the previous night and household received IRS in the last 12 months		0.7 (0.6–0.8)	< 0.001
Geographical factors
Zone	Pemba	1.0
Zone	Unguja	1.2 (1.1–1.3)	< 0.001
CI, confidence Interval; LLIN, long-lasting insecticidal net; IRS, indoor residual spraying of household with insecticide.

This study investigated factors associated with malaria infection (as measured by mRDT positivity) among the general population living in households where malaria cases had been detected passively when attending public or private health facilities in Zanzibar. Our findings suggest that age, history of fever during the last two weeks and history of travel outside Zanzibar during the last 30 days, and living on Unguja Island were independently associated with increased odds of mRDT positivity. On the other hand, male sex, higher LLIN density, sleeping under an LLIN the previous night, living in a household that received IRS in the previous 12 months, as well as combining IRS in the last 12 months and sleeping under an LLIN the previous night were independently associated with reduced odds of mRDT positivity.

Age was an important factor within the model, with children aged 5–14 years being at greatest risk of malaria infection. Malaria prevalence in children under five years of age has decreased in Tanzania in recent years [12], and other studies in mainland Tanzania suggest the risk of malaria infection is now higher in school-aged children compared to children under five years of age [5, 13]. Our finding might partially be explained by the association between age and those who slept under an LLIN the previous night, with a lower proportion of children aged 5–14 years (55.2%) and young adults aged 15–24 years (47.5%) having been observed to sleep under LLINs, compared to children < 5 years [5, 8, 14]. Given their higher odds of having a positive mRDT result, targeting interventions to improve LLIN access and use in children aged 5–14 years and young adults aged 15–24 years will thus be important in Zanzibar, since these age groups appear to be at greater risk of infection and are likely to contribute to on-going malaria transmission. Our analysis also showed that males had reduced odds of having a positive mRDT result. Typically, an increased, sex-associated risk for a vector-borne disease such as malaria suggests sex-related differences in behavior (e.g., night time work or leisure activities), and further studies will have to confirm what specific behaviors could explain the association observed in our study.

As reported previously [7, 14, 16, 17], history of fever during the last two weeks was significantly associated with having a positive mRDT result. While self-reported fever as a proxy for symptomatic malaria infection is prone to recall and/or reporting bias and thus lacks both the sensitivity and specificity, particularly in elimination settings [17, 18], the finding suggests there is a subset of the population who do not present to health facilities when they experience malaria symptoms such as fever. Therefore, persistent messaging on recognizing malaria symptoms and prompting care seeking behavior at health facilities is needed even in a low transmission setting such as Zanzibar. The finding also reinforces the continued need for timely RCD, including the testing of all household members in primary index case households and possibly neighboring households with or without malaria symptoms.

The results of this study confirm prior studies showing that individuals who travelled outside Zanzibar during the last 30 days had increased odds of a positive mRDT result at the time of the household investigations [9]. Malaria infection associated with travel (“malaria importation”) is frequently considered a concern in low transmission or elimination settings [19–22]. There is evidence that the majority of people travelling outside Zanzibar head to malaria endemic areas on the Tanzania mainland [20]. Therefore, implementing control measures to prevent infection among Zanzibaris visiting the mainland, as well as timely detection of travel-associated malaria cases and controlling onward transmission will be key to sustaining the low malaria burden and progressing towards malaria elimination in Zanzibar. Several approaches to limit the size and impact of travel-associated malaria could be considered, including targeting information and education messaging to potential travelers regarding the risks of malaria when travelling, malaria symptomatology, and the possible approaches for individual protection. Advice for travelers to malaria endemic locations could include recommendations to sleep under an LLIN every night, use mosquito repellents, and possibly take anti-malarial chemoprophylaxis depending on the travel destination and malaria risk. An approach to early detection for travel-associated malaria might involve screening every person arriving at key border entry points using ultra-sensitive diagnostic tests such loop-mediated isothermal amplification polymerase chain reaction, and when clinically appropriate treating or referring positive individuals. Such approach could also reinforce malaria surveillance and education on malaria prevention among travelers at these entry points.

The reduced odds of mRDT positivity independently associated with LLIN density and use shown here adds to the vast body of evidence supporting the effectiveness of LLIN in their protection against malaria and other vector-borne diseases [23]. Our study also found that increased availability of LLINs in households was associated with a reduced odd of mRDT positivity, with an LLIN density ≥ 0.5 having the greatest protective effect [24]. Nonetheless, having access to LLINs at household level is not sufficient, and approaches to encourage at-risk populations to consistently sleep under an LLIN, even in a low transmission setting like Zanzibar, are essential.

The protective effect of IRS reported in this study was within the range reported by other studies [16, 25, 26]. Our findings suggest that IRS reduces the risk of malaria infection in the household; however, IRS operations are expensive and require considerable human resources. IRS operations in Zanzibar started in 2006, with different classes of insecticides used over the past 14 years [27]. To mitigate programmatic costs and limit the expansion of insecticide resistance, Zanzibar has shifted from a blanket IRS approach used between 2006–2012 to a more targeted and focal approach from 2015 onwards, whereby only areas experiencing a high number of cases and malaria incidence are prioritized.

Combining IRS and LLINs has increasingly become a common approach to control malaria in sub-Saharan Africa. This study provides further evidence of the added benefit offered to individuals when combining IRS and LLINs [28, 29]. While people who lived in households where IRS was applied in the last 12 months were less likely to be mRDT positive compared to those who were not living in households where IRS has been applied, regardless of whether they slept under a LLIN or not, they were even less likely to test mRDT positive if sleeping under an LLIN. This suggests that even in low transmission settings, the possibility of combining these two vector control interventions is effective.

This study has a number of potential limitations. Firstly, the analysis included members living in the same households as passively detected malaria cases (primary index cases); therefore, we can expect that this population may be at greater risk of malaria compared to the general population in Zanzibar. Secondly, only two thirds of the 33,878 passively detected cases were followed-up for HSaT; therefore, this may be a source of bias since most of the index cases lost-to-follow-up were detcted during the peak malaria transmission season. Thirdly, mRDTs’ have a limited sensitivity and are likely to miss a large proportion of infections, since in Zanzibar’s elimination setting most of these are sub-clinical, low-parasite density infections [18]. The mRDTs’ low sensitivity during RCD is a potential source of bias because they are designed to identify symptomatic malaria cases that are typically high-density infections. This low mRDT sensitivity has broader programmatic implications, with Zanzibar possibly having to consider the use of more sensitive diagnostics (e.g., Polymerase chain reaction [PCR]), in addition to other strategies such as focal mass drug administration or seasonal malaria chemoprevention, to eliminate malaria [3]. Finally, a fifth of the individuals with malaria test results were missing one or more explanatory variable; however, sensitivity analysis showed that this missingness was not a source of bias.

This study has identified risk factors associated with malaria infection (as measured by mRDT positivity) that could be addressed through increased surveillance and targeted interventions. Our study found that between 2012 and 2019 children aged 5–14 years, males, residents of Unguja and residents who travel outside of Zanzibar had greater odds of having a positive mRDT result. These findings suggest that even in a low transmission setting like Zanzibar, vector control remains an important malaria prevention intervention and underscores the need to maintain universal access to LLIN, persistent promotion of LLIN use, and IRS. In addition to continued effective coverage and use of vector control interventions, enhanced behavioral change and preventive strategies specifically targeting children aged 5–14 years and travelers could enhance Zanzibar’s progress towards malaria elimination.

ACTs: Artemisinin-based combination therapies

CI: Confidence interval

CMSO: Council malaria surveillance officer

HSaT: Household-based screening and treatment

IRS: Indoor residual spraying

IQR: Inter quartile range

LLIN: Long-lasting insecticidal net

MCN: Malaria case notification (system)

mRDT: Malaria rapid diagnostic test

OR: Odds ratio

PCR: Polymerase chain reaction

RCD: Reactive case detection

VIF: Variance inflation factor

ZAHRI: Zanzibar Health Research Institute

Ethics approval and consent to participate

Ethical clearance to undertake secondary analysis of surveillance data was granted by the Zanzibar Health Research Institute (ZAHRI) with reference number ZAHREC/03/AUG/2021/20. All personal identifiers were removed during data cleaning before analysis.

Consent for publication

Not applicable

Availability of data and materials

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

Competing interests

The authors declare they have no competing interests.

Funding

This study was made possible through support provided to the RTI International through Okoa Maisha Dhibiti Malaria (OMDM) activity (Cooperative agreement number 72062118CA-00002) by the President’s Malaria Initiative (PMI) via the United States Agency for International Development (USAID) and the U.S. Centers for Disease Control and Prevention (CDC).

Author contributions

The study was designed by HRM, EJR and JMN. HRM, EJR and JMN undertook the analysis. HRM, EJR and JMN drafted the first versions of the manuscript. All authors edited, read, and approved the final manuscript.

Acknowledgements

This analysis was supported by RTI International through Okoa Maisha Dhibiti Malaria (OMDM) activity (cooperative agreement number: 72062118CA-00002) in collaboration with the President's Malaria Initiative, the United States Agency for International Development and the U.S. Centers for Disease Control and Prevention.

Disclaimer

The opinions expressed herein are those of the authors and do not necessarily reflect the views of the President’s Malaria Initiative, the United States Agency for International Development, the U.S. Centers for Disease Control and Prevention, or other employing organizations or sources of funding.

Authors’ information

¹ RTI International, Dar es Salaam, United Republic of Tanzania.² U.S. President’s Malaria Initiative, U.S. Centers for Disease Control and Prevention, Dar es Salaam, United Republic of Tanzania.³ RTI International, Washington D.C., United States of America.⁴ Zanzibar Malaria Elimination Programme, Ministry of Health, Zanzibar, United Republic of Tanzania.⁵ U.S. President’s Malaria Initiative, United States Agency for International Development, Dar es Salaam, United Republic of Tanzania.

1. Ministry of Health, Community Development, Gender, Elderly and Children (MoHCDGEC) [Tanzania Mainland], Ministry of Health (MoH) [Zanzibar], National Bureau of Statistics (NBS), Office of the Chief Government Statistician (OCGS), and ICF. 2017. Tanzania Malaria Indicator Survey 2017. Dar es Salaam, Tanzania, and Rockville, Maryland, USA: MoHCDGEC, MoH, NBS, OCGS, and ICF. [Internet]. Available from: https://dhsprogram.com/pubs/pdf/MIS31/MIS31.pdf

2. Roll Back Malaria Partnership. Global Strategic Plan, 2018–2020 [Internet]. 2020 Oct. Available from: https://endmalaria.org/sites/default/files/RBM-Strategic-Plan-digital-JW-220218.pdf

3. Björkman A, Shakely D, Ali AS, Morris U, Mkali H, Abbas AK, et al. From high to low malaria transmission in Zanzibar—challenges and opportunities to achieve elimination. BMC Med. 2019;17:14.

4. Zanzibar Malaria Elimination Programme. Zanzibar Malaria Elimination Strategic Plan IV 2018/19-2022/23. 2020.

5. Winskill P, Rowland M, Mtove G, Malima RC, Kirby MJ. Malaria risk factors in north-east Tanzania. Malar J. 2011;10:98.

6. Rulisa S, Kateera F, Bizimana JP, Agaba S, Dukuzumuremyi J, Baas L, et al. Malaria Prevalence, Spatial Clustering and Risk Factors in a Low Endemic Area of Eastern Rwanda: A Cross Sectional Study. Noor AM, editor. PLoS ONE. 2013;8:e69443.

7. Temu EA, Coleman M, Abilio AP, Kleinschmidt I. High Prevalence of Malaria in Zambezia, Mozambique: The Protective Effect of IRS versus Increased Risks Due to Pig-Keeping and House Construction. Noor AM, editor. PLoS ONE. 2012;7:e31409.

8. Graves PM, Richards FO, Ngondi J, Emerson PM, Shargie EB, Endeshaw T, et al. Individual, household and environmental risk factors for malaria infection in Amhara, Oromia and SNNP regions of Ethiopia. Transactions of the Royal Society of Tropical Medicine and Hygiene. 2009;103:1211–20.

9. Ahmed S, Reithinger R, Kaptoge SK, Ngondi JM. Travel Is a Key Risk Factor for Malaria Transmission in Pre-Elimination Settings in Sub-Saharan Africa: A Review of the Literature and Meta-Analysis. The American Journal of Tropical Medicine and Hygiene. 2020;103:1380–7.

10. 2012 Population and housing census [Internet]. National Bureau of Statistics, Tanzania; Office of Cheif Government Statistician, Zanzibar; 2020 Oct. Available from: http://www.tzdpg.or.tz/fileadmin/documents/dpg_internal/dpg_working_groups_clusters/cluster_2/water/WSDP/Background_information/2012_Census_General_Report.pdf

11. Zanzibar guidelines for malaria diagnosis and treatment : Zanzibar malaria elimination programme [Internet]. Revolutionary Government of Zanzibar, Ministry of Health and Social Welfare; Available from: https://searchworks.stanford.edu/view/11527091

12. Nkrumah B, Fobil J, Krefis AC, Loag W, Ranft U, Acquah S, et al. Modeling the Relationship between Precipitation and Malaria Incidence in Children from a Holoendemic Area in Ghana. The American Journal of Tropical Medicine and Hygiene. 2011;84:285–91.

13. Ministry of Health, Community Development, Gender, Elderly and Children (MoHCDGEC) [Tanzania Mainland], Ministry of Health (MoH) [Zanzibar], National Bureau of Statistics (NBS), Office of the Chief Government Statistician (OCGS), and ICF. 2017. Tanzania Malaria Indicator Survey 2017. Dar es Salaam, Tanzania, and Rockville, Maryland, USA: MoHCDGEC, MoH, NBS, OCGS, and ICF. [Internet]. 2010 Oct. Available from: https://dhsprogram.com/pubs/pdf/MIS31/MIS31.pdf

14. Khatib RA, Chaki PP, Wang D-Q, Mlacha YP, Mihayo MG, Gavana T, et al. Epidemiological characterization of malaria in rural southern Tanzania following China-Tanzania pilot joint malaria control baseline survey. Malar J. 2018;17:292.

15. Tchinda VHM, Socpa A, Keundo AA, Zeukeng F, Seumen CT, Leke RGF, et al. Factors associated to bed net use in Cameroon: a retrospective study in Mfou health district in the Centre Region. Available from: http://www.panafrican-med-journal.com/content/article/12/112/full/

16. West PA, Protopopoff N, Rowland M, Cumming E, Rand A, Drakeley C, et al. Malaria Risk Factors in North West Tanzania: The Effect of Spraying, Nets and Wealth. Keating JA, editor. PLoS ONE. 2013;8:e65787.

17. the Ogumaniha-SCIP Zambézia Consortium, Carlucci JG, Blevins Peratikos M, Cherry CB, Lopez ML, Green AF, et al. Prevalence and determinants of malaria among children in Zambézia Province, Mozambique. Malar J. 2017;16:108.

18. Stuck L, Fakih BS, Al-mafazy AH, Hofmann NE, Holzschuh A, Grossenbacher B, et al. Malaria infection prevalence and sensitivity of reactive case detection in Zanzibar. International Journal of Infectious Diseases. 2020;97:337–46.

19. Bradley J, Monti F, Rehman AM, Schwabe C, Vargas D, Garcia G, et al. Infection importation: a key challenge to malaria elimination on Bioko Island, Equatorial Guinea. Malar J. 2015;14:46.

20. Le Menach A, Tatem AJ, Cohen JM, Hay SI, Randell H, Patil AP, et al. Travel risk, malaria importation and malaria transmission in Zanzibar. Sci Rep. 2011;1:93.

21. World Health Organization, Global Malaria Programme, World Health Organization. A framework for malaria elimination [Internet]. 2017 [cited 2020 Jun 30]. Available from: http://apps.who.int/iris/bitstream/10665/254761/1/9789241511988-eng.pdf

22. Sturrock HJW, Hsiang MS, Cohen JM, Smith DL, Greenhouse B, Bousema T, et al. Targeting Asymptomatic Malaria Infections: Active Surveillance in Control and Elimination. PLoS Med. 2013;10:e1001467.

23. Lengeler C. Insecticide-treated bed nets and curtains for preventing malaria. Cochrane Infectious Diseases Group, editor. Cochrane Database of Systematic Reviews [Internet]. 2004 [cited 2020 Jun 30]; Available from: http://doi.wiley.com/10.1002/14651858.CD000363.pub2

24. The Ethiopia Malaria Indicator Survey Working Group, Ngondi JM, Graves PM, Gebre T, Mosher AW, Shargie EB, et al. Which nets are being used: factors associated with mosquito net use in Amhara, Oromia and Southern Nations, Nationalities and Peoples’ Regions of Ethiopia. Malar J. 2011;10:92.

25. Pluess B, Tanser FC, Lengeler C, Sharp BL. Indoor residual spraying for preventing malaria. Cochrane Infectious Diseases Group, editor. Cochrane Database of Systematic Reviews [Internet]. 2010 [cited 2020 Jun 30]; Available from: http://doi.wiley.com/10.1002/14651858.CD006657.pub2

26. Kleinschmidt I, Sharp B, Benavente LE, Schwabe C, Torrez M, Kuklinski J, et al. Reduction in infection with Plasmodium falciparum one year after the introduction of malaria control interventions on Bioko Island, Equatorial Guinea. Am J Trop Med Hyg. 2006;74:972–8.

27. Haji KA, Thawer NG, Khatib BO, Mcha JH, Rashid A, Ali AS, et al. Efficacy, persistence and vector susceptibility to pirimiphos-methyl (Actellic® 300CS) insecticide for indoor residual spraying in Zanzibar. Parasites Vectors. 2015;8:628.

28. Kleinschmidt I, Schwabe C, Shiva M, Segura JL, Sima V, Mabunda SJA, et al. Combining indoor residual spraying and insecticide-treated net interventions. Am J Trop Med Hyg. 2009;81:519–24.

29. Okumu FO, Moore SJ. Combining indoor residual spraying and insecticide-treated nets for malaria control in Africa: a review of possible outcomes and an outline of suggestions for the future. Malar J. 2011;10:208.

Download PDF

Journal Publication

published 01 Dec, 2021

Read the published version in Malaria Journal →

Editorial decision: Minor Revision
21 Oct, 2021
Review #1 received at journal
20 Oct, 2021
Reviews received at journal
18 Oct, 2021
Review #2 received at journal
17 Oct, 2021
Reviewers invited by journal
03 Oct, 2021
Reviewer #2 agreed at journal
02 Oct, 2021
Reviewer #1 agreed at journal
02 Oct, 2021
Editor invited by journal
30 Sep, 2021
Editor assigned by journal
27 Sep, 2021
Submission checks completed at journal
26 Sep, 2021
First submitted to journal
24 Sep, 2021

You are reading this latest preprint version

Risk Factors Associated with Malaria Infection Identified through Reactive Case Detection in Zanzibar, 2012-2019

Status:

Journal Publication

Version 1

Abstract

Figures

Background

Methods

Study setting and data sources

Data collection

Inclusion and exclusion criteria

Data analysis

Results

Characteristics of study population

Univariate analysis

Multivariate analysis

Discussion

Conclusions

Abbreviations

Declarations

References

Status:

Journal Publication

Version 1