Akatama B. Inambao1, 4, R. Kumar2, B. Hamainza3, M. Makasa4, C.F. Nielsen5
- Zambia Field Epidemiology Training Program, Ministry of Health, Lusaka, Zambia;
- ASPPH/CDC Allan Rosenfield Global Health Fellow;
- National Malaria Elimination Center, Lusaka, Zambia;
- University of Zambia, Lusaka, Zambia;
- United States Centers for Disease Control and Prevention, President’s Malaria Initiative, Zambia.
Citation Style For This Article: Inambao AB, Kumar R, Hamainza B, Makasa M, Nielsen CF. The Additional Effect of Focal Indoor
Residual Spraying on Incidence of Malaria in a Setting With High Insecticide Treated Bed Nets Coverage in Mansa District, Luapula
Province. Health Press Zambia Bull. 2019; 3(4); pp 12-20.
Malaria is a leading cause of morbidity and mortality especially in children under 5 years and pregnant women in Zambia [1, 2]. Environmental factors and behavioral patterns of vectors and human populations combine to provide favorable conditions for malaria transmission. Focal-Indoor Residual Spraying (IRS) was first conducted in Zambia’s Luapula Province in 2014 in areas with high burden of malaria. A quasi experimental study design comparing incidence of malaria pre and post-IRS intervention was used. Malaria is diagnosed by use of rapid diagnostic test or microscopically. Malaria incidence was calculated based on extrapolated census data for health centres. We extracted malaria morbidity data from the Health Management Information System from 2013 to 2015. There was no physical contact with participants as only secondary data were used. Epi-Info version 7 was used to analyze the data. Associations between variables were tested using a Chi-square with the level of statistical significance set at desired accuracy of 5% and 95% confidence interval.A total of 11 of 25 (44%) health facility catchment areas conducted focal-IRS in 2014 in addition to Insecticide Treated bed Nets (ITNs) use. Six of 11 (55%) IRS health facility catchment areas recorded spray coverage of above 85%. Of the 11 IRS health facility catchment areas, 5 (45%) recorded decrease in incidence of total malaria (clinical and confirmed) in 2015 compared to 2013 whilst 6 (55%) recorded increased incidence post spraying. About 5 of 14 (36%) ITN only health facility catchment areas recorded decrease in incidence of total malaria in 2015 compared to 2013. Only 1 of 11(9%) IRS-health facility catchment areas (Muwanguni RHC) recorded decrease in incidence of lab-confirmed malaria while 91% recorded increase in 2015 compared to 2013. Use of focal IRS strategy in addition to ITNs in Mansa district did not yield additional effect compared to use of ITNs only. This strategy needs to be redesigned to ensure that questions of its efficacy and operationalization are well understood before scale-up of the concept is enhanced.
INTRODUCTION
Malaria is a major public health problem. It is transmitted to humans by an infected female anopheles mosquito bite [3] with about 3.4 billion people at risk of the disease globally, with 1.2 billion people at high risk [4]. Environmental factors and behavioral patterns of vectors and human populations combine to provide favorable conditions for malaria transmission. Geographic distribution of malaria around the world where malaria is found depends mainly on climatic factors such as temperature, humidity, and rainfalls, where malaria is transmitted in tropical and subtropical areas [4].
In Africa, an estimated 74% of the population lives in areas that are highly endemic and 19% lives in epidemic prone areas [1]. About 30% of outpatient consultations, 20-50% of hospital admissions, and 20% of under five mortality are due to malaria [5]. In 2013, there were about 198 million malaria cases and an estimated 584 000 malaria deaths [1]. Increased prevention and control measures have led to a reduction in malaria mortality rates by 47% globally since 2000 and by 54% in the WHO African Region [1].
Malaria is also endemic in Zambia and a leading cause of morbidity and mortality, especially in children under five years and pregnant women [2]. Fighting the disease is a national priority that requires a focused, comprehensive, and consistent approach in order to achieve the vision of “a malaria-free Zambia by 2030” [2]. As part of Zambia’s National Malaria Control and Elimination Strategy, a number of interventions has been launched to reduce malaria, including universal insecticide treated mosquito net coverage and indoor residual spraying (IRS) in targeted areas. The plan also includes strategies to improve malaria case management, improve diagnostic testing capacity and quality; increase coverage of three doses of sulfadoxine – pyrimethamine (SP) for intermittent preventive treatment in pregnancy (IPTp), strengthen behavior change and communication or malaria prevention and treatment and establish a robust surveillance, and monitoring and evaluation framework [2].
IRS, while typically used for low malaria risk or epidemic prone regions [6], has been further advocated for use in high and medium malaria transmission settings [7], [8]. In 2011, 30 (of 44) countries in Africa with ongoing malaria transmission used both IRS and ITN in at least some areas to further reduce the malaria burden [9]. The proportion of at risk populations protected by IRS in Africa has increased from less than 5% in 2005 to 11% in 2011 [9].
Beyond mathematical modeling of ITNs and IRS on health outcomes [10], [11], [12], few studies have empirically measured the combined effectiveness of the two interventions. In rural Gambia, a community-based trial is currently exploring the effectiveness of having ITNs and IRS against clinical malaria [13]. In Kenya, Hamel and colleagues showed that household members who were exposed to both ITNs and IRS had significantly greater protection against malaria infection than those who only used ITNs [14]; however, the study did not include a comparison group of IRS – only users. In southern Benin, on the other hand, no significant protective benefits against malaria parasite density were provided by ITNs and IRS combined, as compared to ITNs only [15].
Implementing IRS in addition to ITNs was beneficial for individuals from villages with a wide range of transmission intensities and net utilization levels [16]. Net users received additional protection from IRS. These results demonstrated that there is a supplementary benefit of IRS even when ITNs are effective. Mamta et al. (2009) proved that in spite of the constraints associated with IRS, it still has a major role in the control of malaria if implemented with proper supervision, better coverage and community participation [17].
Zambia firstly introduced focal IRS in 2014. This was conducted in seven out of the eleven districts of Luapula province in targeted health facility catchment areas with high prevalence of malaria. These include Chienge, Kawambwa, Mansa, Milenge, Mwansabombwe, Mwense and Nchelenge districts [18]. Based on this activity, this study therefore focuses on effects of focal IRS in a setting with high utilization of ITN in Mansa district of Luapula province pre and post 2014 spraying season.
METHOD
A quasi experimental study design comparing incidence of malaria pre and post IRS intervention was used. The study was conducted in Mansa District in Luapula Province of Zambia. The district had a population of 251,505 in 2014 and has a total of 30 Health facilities which include 1 level II hospital, 26 HCs and 3 health posts. Mansa serves administrative and commercial functions, being situated on a relatively featureless plateau between the Luapula River to the west and Lake Bangweulu to the east. Fishing and subsistence farming are some of the major economic activities.
The study population consisted of health centre catchment areas for 25 health facilities. The study included all the 25 health facility catchment areas in Mansa district (excluding the hospital). All the 25 health centre catchments were selected to determine effect of focal IRS on incidence of malaria because data was available for these catchment areas and were estimated to be used as study sample.
Over three weeks period, Data on ITN coverage (sleeping bed spaces with ITNs), focal IRS coverage (structures sprayed over targeted, population protected) and type of insecticide used were extracted from ITNs and IRS record sheets respectively, from Mansa District Medical Office (DMO) for Health facility catchment areas. We accessed and verified available malaria data on ITNs and IRS for Mansa for the period under review (2013 to 2015). IRS was conducted in 2014 spraying season and never in 2013.
The DHIS2 database (web based) contains various data sets, but this study extracted data elements such as total malaria (clinical and confirmed), total tested cases and confirmed malaria. from HMIS dataset pertaining to malaria morbidity from 2013 (baseline data) to 2015.
Malaria data were processed to establish the malaria incidence based on census data for health facility catchment area for a particular year. The age category included all age groups (both under five and over five years old).
This study used secondary data and therefore, no standardized questionnaires were designed to collect the data. However, a data extraction form was used to capture from both focal spray data sheets obtained at the District Medical Office for 2014 spraying season, and the web-based DHIS2 database. Extracted data were edited and cleaned up to check on completeness of data. All information extracted was confidentially stored and data backup kept in an external hard drive at the end of the study for future reference.
The analysis was conducted to establish IRS coverage for the single spraying season (2014). We obtained total malaria and laboratory confirmed malaria morbidity data retrieved from HMIS on the web based DHIS2 database to calculate malaria incidence rates using pivot tables of Microsoft Excel sheet, based on Central Statistical Office (CSO) population data for health facilities in Mansa District. We determined the change in incidence of malaria pre and post focal spraying, 2013 data being the baseline when only ITNs were used.
Data were then transferred and entered into Microsoft excel sheet and Epi-Info statistical package to analyze and compare descriptive statistics and incidence rates for the two groups. Group 1 included the health facility catchment areas that received ITNS only during the study period and group 2 included health facility catchment areas that received both ITNS and focal IRS for malaria intervention.
The spraying related variables that include malaria incidence rate, proportion of structures sprayed, population covered by the spray and timing of spraying, and type of insecticide used were analyzed. Associations between variables were tested using a Chi-square with the level of statistical significance set at desired accuracy of 5% and 95% confidence interval to measure the public health impact of focal IRS. Risk ratio statistical methods of analysis were used to compare the measure of association between IRS and non-IRS health facility catchment areas in Mansa district in relation to malaria incidence.
In order to determine the additional effect of IRS, the monthly morbidity data for this study were aggregated to 12 months post IRS to analyze and evaluate the 12-month effect of focal spraying on malaria incidence for the 25 health facility catchment areas.
The non-combined effect was measured by taking into account pre-IRS malaria morbidity data where only ITNs were used to determine malaria incidence before focal IRS was implemented in 2013.The combined effect was measured by determining outcome of interest (malaria incidence) in 2014 when both focal IRS and ITNs where used as interventions to counter the transmission of malaria. The combined effect henceforth, were measured 12 months post focal IRS (by Dec 2015).
Ethical considerations
There was no physical contact with participants as only secondary data were used. Ethics approval was received from ERES Converge, whilst permission to conduct the study was obtained from the Ministry of Health and via Mansa District Medical Office. All information extracted was confidentially stored at the end of the study. The information generated from this study helped improve individual participation in future IRS services and offered better health service provision that would contribute to the reduction of malaria disease burden in the community as a whole.
RESULTS
Data for the 25 Health facility catchment areas were abstracted to calculate and analyze malaria incidence rates pre and post focal indoor residual spraying period for 2013 to 2015.
Mansa district had a total population of 257, 517 by 2015 with a CSO 2010 projected annual growth rate of 2.1%. Of the total number of malaria cases (189,407) recorded in 2015, at least 163,135 cases (86%) were laboratory confirmed. Overall, the incidence of total malaria decreased by 0.4% from 739/1000 persons per year in 2013 to 736/1000 persons per year in 2015. With regard to lab-confirmed malaria, an increase of 74% occurred between 2013 and 2015. Total malaria was fluctuating somewhat while lab-confirmed malaria increasing substantially, largely due to increased confirmatory testing.
The Mansa district recorded ITN coverage of 100% during 2013 to 2015. All the 25 health facilities received and distributed ITNs into the community according to the available sleeping bed spaces. This provided 100% protection to the population of Mansa district.
A total 11 of 25 (44%) health facilities conducted focal IRS in their targeted catchment areas in 2014 in addition to use of ITNs. At least 7 out of 11 (64%) IRS health facilities recorded spray coverage of above 85% in their catchment areas. Mibenge RHC (96%) had the highest IRS coverage followed by Mabumba RHC (94%) and Buntungwa UHC (93%). The lowest coverage was recorded in Senama UHC (65%) followed by Mutiti RHCs with 74%. All the targeted IRS health facility catchment areas used an insecticide commonly known as Actellic 300CS during the 2014 spraying season. Mansa district recorded average spray coverage of 84% during October to December 2014 spray season.
A total of 5 of 11 (45%) IRS health facilities recorded decrease in incidence of total malaria in their catchment areas in 2015 compared to 2013 whilst 6 of 11 (55%) IRS health facilities recorded increase in incidence of total malaria post spraying.
Central Urban HC (-46%) recorded the highest percent decrease in incidence of malaria after IRS followed, Mabumba RHC (-28%) and Buntungwa RHCs (-23%) by the end of 2015. Highest percent increase in incidence of malaria after spraying was observed in Matanda RHC (80%) followed by Mibenge (34%) and Mantumbusa RHC (28%) among others. On average, total malaria incidence among IRS-health facility catchment areas slightly decreased by 0.69% by end 2015 compared to 2013.
total of 14 of 25 (56%) health facilities did not conduct IRS in their catchment areas but used ITNs only as a strategy to prevent malaria. At least 5 of 14 (36%) ITN only health facilities recorded decrease in incidence of total malaria in 2015 compared to 2013, whilst 9 of 14 (64%) recorded increase in incidence. Lubende RHC (-36%) had the highest percent decrease in incidence of total malaria followed by Mano (-31%) and Moloshi RHCs (-21%). Highest percent increase in incidence of malaria after spraying was observed in Kansenga RHC (147%) followed by Musaila RHC (83%) and Nsonga HP (73%) by the end of 2015.
On average, total malaria incidence for ITN only health facilities increased by 155/1000 persons (20%) by end of 2015 compared to 2013. Based on the exposure – outcome analysis, the risk ratio of 0.85 (0.43 – 1.65) with p-value of 0.69 indicated non-statistically significant difference in incidence of total malaria between IRS and ITN only health facilities.
At least 1 of 11(9%) IRS-health facilities (Muwanguni RHC) recorded decrease in incidence of lab-confirmed malaria by 238 (40%) while 10 of 11 IRS-health facilities recorded increase in 2015 compared to 2013. Highest percent increase in incidence was observed in Mabumba (136%), Buntungwa UHC (116%), Chisembe and Senama UHC catchment areas with 107% increase respectively. On average, confirmed malaria incidence in IRS health facilities increased from 547 to 890 per thousand (63%) by end of 2015 compared 2013.
At least 1 of 14 (7%) of non-IRS health facilities (Mano RHC) recorded a decrease (-2%) in incidence of confirmed malaria from 517 per 1000 persons in 2013 to 506 per 1000 persons in 2015 while a total of 13 out of 14 (93%) ITN only health facilities recorded increased incidence in 2015 compared to 2013. Highest percent increase in incidence of confirmed malaria was observed in Musaila followed by Kalaba and Kansenga RHC catchment areas by the end of 2015. On average, total malaria incidence for ITN only health facilities increased from 359 to 789/1000 persons (120%) by end of 2015 compared to 2013.
Based on the exposure – outcome analysis, the risk ratio of 0.98 (0.77 – 1.24) with p-value of 1.0 indicated non-statistically significant difference in incidence of lab-confirmed malaria between IRS and non-IRS health facilities.
Table 2: Demographic and Malaria disease characteristics, Mansa District, 2013 to 2015
| Year | Population (CSO) | Projected annual pop growth rate | Total malaria cases | Lab-confirmed malaria cases | Total malaria incidence / 1000 persons | Lab-conf malaria incidence / 1000 persons |
| 2013 | 245,510 | 2.1 | 181,384 | 89,472 | 739 | 364 |
| 2014 | 251,505 | 2.1 | 193,781 | 146,111 | 770 | 581 |
| 2015 | 257,517 | 2.1 | 189,407 | 163,135 | 736 | 633 |
Table 3: Coverage of Sprayed Unit Structures by Health Facility Catchment Area, October to December, 2014, Mansa District
| Health facility catchment area | Total structures targeted (n) | Total structures sprayed (n) | Spray coverage (%) |
| Mibenge RHC | 532 | 512 | 96% |
| Mabumba RHC | 1,680 | 1,581 | 94% |
| Buntungwa UHC | 2,566 | 2,386 | 93% |
| Mantumbusa RHC | 757 | 688 | 91% |
| Matanda RHC | 502 | 450 | 90% |
| Muwanguni RHC | 427 | 366 | 86% |
| Chisembe RHC | 516 | 436 | 84% |
| Central UHC | 7,477 | 6,147 | 82% |
| Kabunda RHC | 689 | 613 | 89% |
| Mutiti RHC | 297 | 221 | 74% |
| Senama UHC | 2,658 | 1,738 | 65% |
| Average | 18,101 | 15,138 | 84% |
Table 4: Comparison of Total Malaria Incidence for IRS-Health Facility Catchment Areas Pre and Post Spraying Period, Mansa, 2013 and 2015
| IRS Health facility | Total malaria incidence, 2013 (Pre-IRS) | Total malaria incidence, 2015 (Post-IRS) | Change in incidence | Percent difference | Comment on change in incidence | |||||
| Mabumba RHC | 1,824 | 1,315 | -509 | -28 | Decreased | |||||
| Kabunda RHC | 1,795 | 1,700 | -95 | -5 | Decreased | |||||
| Chisembe RHC | 1,032 | 1,158 | 126 | 12 | Increased | |||||
| Mutiti RHC | 1,004 | 1,027 | 23 | 2 | Increased | |||||
| Central UHC | 922 | 501 | -421 | -46 | Decreased | |||||
| Senama UHC | 854 | 957 | 103 | 12 | Increased | |||||
| Mantumbusa RHC | 817 | 1,046 | 229 | 28 | Increased | |||||
| Mibenge RHC | 805 | 1,082 | 277 | 34 | Increased | |||||
| Buntungwa UHC | 797 | 614 | -183 | -23 | Decreased | |||||
| Muwanguni RHC | 785 | 767 | -183 | -2 | Decreased | |||||
| Matanda RHC | 486 | 875 | 389 | 80 | Increased | |||||
| Average | 1,011 | 1,004 | -7 | -0.69 | Decreased | |||||
Table 5: Comparison of Total Malaria Incidence for ITN only Health Facilities Catchment Areas Pre and Post Spraying Period, Mansa, 2013 and 2015
| Non-IRS Health facility | Total malaria incidence 2013 (Pre-IRS) | Total malaria incidence 2015 (Post IRS) | Change in incidence | Percent difference | Comment on change in incidence |
| Lubende | 1,696 | 1081 | -615 | -36 | Decreased |
| Kalyongo RHC | 1,154 | 1640 | 486 | 42 | Increased |
| Fimpulu RHC | 876 | 1253 | 377 | 43 | Increased |
| Paul Mambilima RHC | 848 | 685 | -163 | -19 | Decreased |
| Kalaba RHC | 847 | 1179 | 332 | 39 | Increased |
| Luamfumu RHC | 808 | 643 | -165 | -20 | Decreased |
| Mano RHC | 754 | 519 | -235 | -31 | Decreased |
| Ndoba RHC | 746 | 1091 | 345 | 46 | Increased |
| Kansenga RHC | 725 | 1788 | 1063 | 147 | Increased |
| Musaila RHC | 656 | 1202 | 546 | 83 | Increased |
| Katangwe RHC | 469 | 528 | 59 | 13 | Increased |
| Moloshi RHC | 398 | 314 | -84 | -21 | Decreased |
| Chisunka RHC | 388 | 431 | 43 | 11 | Increased |
| Nsonga HP | 236 | 408 | 172 | 73 | Increased |
| Average | 757 | 912 | 155 | 20 | Increased |
Table 6: Laboratory Confirmed Malaria Incidence, IRS-Health Facility Catchment Areas, Mansa District, 2013 – 2015
| IRS Health facility | Conf. malaria incidence 2013 ( Pre IRS) | Conf. malaria incidence 2015 (Post IRS) | Change in incidence | Percent difference | Comment on change in incidence |
| Buntungwa UHC | 208 | 450 | 242 | 116 | Increased |
| Central UHC | 377 | 395 | 18 | 5 | Increased |
| Chisembe RHC | 462 | 958 | 496 | 107 | Increased |
| Kabunda RHC | 1,221 | 1647 | 426 | 35 | Increased |
| Matanda RHC | 456 | 875 | 419 | 92 | Increased |
| Mabumba RHC | 552 | 1305 | 753 | 136 | Increased |
| Mantumbusa RHC | 642 | 1046 | 404 | 63 | Increased |
| Mibenge RHC | 497 | 964 | 467 | 94 | Increased |
| Mutiti RHC | 560 | 861 | 301 | 54 | Increased |
| Senama UHC | 446 | 925 | 479 | 107 | Increased |
| Muwanguni RHC | 599 | 361 | – 238 | -40 | Decreased |
| Average | 547 | 890 | 343 | 63 | Increased |
Table 7: Laboratory Confirmed Malaria Incidence per 1000 person, ITN only Health Facility Catchment Areas, Mansa District, 2013 – 2015
| ITN only Health Facility | Conf. malaria Incidence 2013 (Pre IRS) | Conf. malaria incidence 2015 (Post IRS) | Change in incidence /1000 persons | Percent difference | Comment on change in incidence |
| Luamfumu RHC | 296 | 448 | 152 | 51 | Increased |
| Kalyongo RHC | 543 | 1632 | 1089 | 201 | Increased |
| Lubende | 868 | 1078 | 210 | 24 | Increased |
| Kansenga RHC | 510 | 1695 | 1185 | 232 | Increased |
| Mano RHC | 517 | 506 | -11 | -2 | Decreased |
| Kalaba RHC | 259 | 1165 | 906 | 350 | Increased |
| Moloshi RHC | 193 | 294 | 101 | 52 | Increased |
| Paul Mambilima RHC | 477 | 685 | 208 | 44 | Increased |
| Ndoba RHC | 514 | 862 | 348 | 68 | Increased |
| Nsonga HP | 210 | 401 | 191 | 91 | Increased |
| Musaila RHC | 86 | 966 | 880 | 1,023 | Increased |
| Chisunka RHC | 217 | 424 | 207 | 95 | Increased |
| Fimpulu RHC | 463 | 1183 | 720 | 156 | Increased |
| Katangwe RHC | 227 | 500 | 273 | 120 | Increased |
| Average | 359 | 789 | 430 | 120 | Increased |
DISCUSSION
This study was set out to determine whether there is an additional benefit of using focal IRS in a setting with high ITN use compared to the practice of use of ITNs alone. The main outcome variable was change in incidence of malaria disease assessed using abstracted malaria morbidity data for health facility catchment areas in Mansa district between 2013 (pre-IRS) and 2015 (post-IRS).
Overall, Mansa district recorded a slight decrease in total malaria incidence of less than 1% in 2015 (post IRS) compared to 2013 (pre-IRS). A proportion of more than 80% of recorded cases in 2015 were laboratory confirmed by either use of Rapid Diagnostic Test or microscopically (Table 1). This signified an improvement in adherence to testing recommendations compared to 2013. The district recorded average spray coverage of more than 80% during 2014 spraying season using Actellic 300CS, a new long-lasting micro-encapsulated formulation of the organophosphate insecticide pirimiphos-methyl. This insecticide has been specifically designed for use in indoor residual spraying (IRS) programs to provide up to one year’s residual control of mosquitoes and other public health pests. Actellic 300CS is effective against pyrethroid resistant Anopheles, Aedes and Culex species [19].
Mansa district, conducted focal indoor residual spraying strategy in combination with ITN usage, targeting health facility catchment areas with high incidence of malaria and also considering cost effectiveness compared to carrying out generalized spraying. Spraying was conducted in a total of 11 out of 25 health facilities from October to December of 2014 in addition to use of ITNs with over half (63%) having achieved spray coverage of above 85% (table 3). We however, observed that conducting IRS during this time frame may have a negative impact on the coverage due to high refusals and or poor community acceptability of the programme presumably due to heightened rainfall and busy farming schedules by some of the community members. Commencing IRS slightly earlier would improve spray coverage.
The results further showed that almost half of the IRS health facility catchment areas had recorded relatively reduced incidence of malaria after spraying compared to the incidence they had before spraying signifying probable effect of spraying in addition to use of ITNs. Conversely, half of the health facilities that achieved spray coverage above 85% recorded increase in incidence of malaria, with Matanda RHC almost doubling the incidence in 2015 compared to 2013. This seemed unusual and prompted us to further ask ourselves questions concerning some operational dynamics, such as whether the insecticide solution wasn’t well formulated, maybe rightful residue was not applied on the walls, whether all the rooms were sprayed or not or emerging insecticide resistance among others. The average effects of spraying showed insignificant decrease (0.69%) in incidence among sprayed health facilities comparing the 12-month malaria incidence rates for unsprayed health facility catchment areas (20% increase) in 2015 with the similar period in the preceding 2 years, 2013.
The findings on incidence of total malaria in ITN only health facilities demonstrated that 5 out of 14 (less than half) of the facilities recorded a decrease in incidence of malaria while about 64% of health facilities recorded increased incidence in 2015 compared to 2013. Comparatively, we observed that incidence of total malaria either increased or decreased in both sprayed and non-sprayed health facility catchment areas regardless of spray status. There was an insignificant difference in change of incidence of malaria between IRS and ITN only health facilities. The exposure – outcome analysis indicated the risk ratio of 0.89 (0.43 – 1.65) demonstrating a non-statistically significant difference in incidence of total malaria between IRS and ITN only health facilities suggesting that provision of focal IRS and ITNs was relatively not protective compared to use of ITNs only.
We further analyzed incidence of laboratory confirmed malaria for both IRS and ITN only health facilities. Among the IRS health facilities, Muwanguni RHC was the only health facility that recorded decrease in incidence of lab-confirmed malaria in 2015 while only Mano RHC recorded decreased incidence among ITN only health facilities. In view of the analysis above, these results demonstrates that there was no difference in effect of both interventions on incidence of lab-confirmed malaria post IRS period entailing that there were no advantages of combining IRS with ITNs relative to using ITNs alone in Mansa district. We are however, mindful that this outcome may be different in certain situations, since there are numerous confounding factors that can affect the results.
LIMITATIONS OF THE STUDY
This study focused on the additional effect of focal indoor residual spraying on incidence of malaria in a setting with high insecticide treated bed nets coverage in Mansa district, Luapula province. This being the case, the findings may not be generalized to other parts of Zambia since only health facility catchment areas in Mansa provided information for this study.
CONCLUSION AND RECOMMENDATIONS
Based on the findings of the study, it became clear that even though combining IRS and ITNs is increasingly being practiced and are already known to confer significant benefits against malaria [21], [15] the results for Mansa district could not give us sufficient evidence as to whether it is indeed better than ITNs on their own. This is especially evident when we analyzed incidence of laboratory confirmed malaria that showed no difference in outcome between IRS and ITN only health facilities. We therefore concluded that, use of focal IRS strategy in addition to ITNs in Luapula province and Mansa district in particular did not yield additional effect compared to use of ITNs only.
This strategy needs to be redesigned to ensure raised questions of its efficacy and operationalization are well understood by stakeholders before scale-up of the concept is enhanced. Therefore, to maximize any possible additional benefits from IRS/ITN co-applications, rigorous field evidence, supported by mathematical modeling where necessary should be pursued to support the entire process of decision making, including the selection of which insecticides to be used for IRS and what type of ITNs to use. This will require different kinds of studies on combined ITN-IRS use to include: 1) experimental hut investigations where efficacies of the combinations are directly assessed against wild free-flying malaria vectors in malaria endemic areas, 2) mathematical simulation incorporating characteristics of candidate insecticidal applications to estimate likely benefits of the combinations in different scenarios, 3) long-term community-wide studies to determine effectiveness of the combinations and 4) cost benefit analysis of the combinations compared to individual methods on their own and also to other existing interventions.
As correlations between these two methods and accrued health benefits become better understood, their acquisition and utilization will require that the implementation is monitored closely to ensure proper use and optimal efficacy, maximum cost effectiveness and also to prevent problems such as insecticide resistance. On the other hand, health authorities in Mansa district need to ensure that important gains so far achieved from existing interventions are not lost especially as malaria control enters the phase of intensive and sustained vector control.
Acknowledgements
This work was funded by the United States Centers for Disease Control and Prevention and the US President’s Malaria Initiative. The author acknowledges them with gratitude for supporting and funding the manuscript writing workshop on behalf of the Zambian Field Epidemiology Training Program (FETP). We also acknowledge the role the Ministry of Health is undertaking in coordinating, supporting and ensuring that FETP becomes a success in Zambia.
We would like to thank the following: Dorothy Southern, for providing mentorship and technical skills during training; Nicole Bellows, for providing guidance and mentorship towards the production of this document and Dr. Henry K. Baggett, for reviewing and providing technical advice towards the production of this document. This publication was supported by Cooperative Agreement Number U36OE000002 from the Centers for Disease Control and Prevention and the Association of Schools and Programs of Public Health. The findings and conclusions of this publication do not necessarily represent the official views of CDC or ASPPH.
Competing interests
The authors declare that they have no competing interests.
Authors’ contributions
Extracted the data: AI, Analyzed the data: AI, RK. Wrote the paper: AI, RK, BH, MM, CFN. All authors read and approved the final manuscript.
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