Use of insecticide treated nets in children under five and children of school age in Nigeria: Evidence from a secondary data analysis of demographic health survey

Chinazo N Ujuju; Chukwu Okoronkwo; Okefu Oyale Okoko; Adekunle Akerele; Chibundo N Okorie; Samson Babatunde Adebayo

doi:10.1371/journal.pone.0274160

. 2022 Sep 29;17(9):e0274160. doi: 10.1371/journal.pone.0274160

Use of insecticide treated nets in children under five and children of school age in Nigeria: Evidence from a secondary data analysis of demographic health survey

Chinazo N Ujuju ^1,^*, Chukwu Okoronkwo ², Okefu Oyale Okoko ², Adekunle Akerele ³, Chibundo N Okorie ⁴, Samson Babatunde Adebayo ⁵

Editor: Clement Ameh Yaro⁶

PMCID: PMC9521839 PMID: 36174025

Abstract

Background and objective

Use of insecticide treated nets (ITN), one of the most cost-effective malaria interventions contributes to malaria cases averted and reduction in child mortality. We explored the use of ITN in children under five (CU5) and children of school age to understand factors contributing to ITN use.

Methods

A cross-sectional study analyzed 2018 Nigeria Demographic and Health Survey data. The outcome variable was CU5 or children of school age who slept under ITN the night before the survey. Independent variables include child sex, head of household’s sex, place of residence, state, household owning radio and television, number of household members, wealth quintile, years since ITN was obtained and level of malaria endemicity. Multi-level logistic regression model was used to access factors associated with ITN use among children.

Results

In total, 32,087 CU5 and 54,692 children of school age were examined with 74.3% of CU5 and 57.8% of children of school age using ITN the night before the survey. While seven states had more than 80% of CU5 who used ITN, only one state had over 80% of school children who used ITN. ITN use in CU5 is associated with living in rural area (aOR = 1.20, 95% CI 1.14 to 1.26) and residing in meso endemic area (aOR = 3.1, 95% CI 2.89 to 3.54). While In children of school age, use of ITN was associated with female headed households (aOR = 1.14, 95% CI 1.09 to 1.19), meso (aOR = 3.17, 95% CI 2.89 to 3.47) and hyper (aOR = 14.9, 95% CI 12.99 to 17.07) endemic areas. Children residing in larger households were less likely to use ITN.

Conclusions

This study demonstrated increased use of ITN in CU5 from poor households and children living in rural and malaria endemic areas. Findings provide some policy recommendations for increasing ITN use in school children.

Introduction

Malaria remains a global public health problem with 229 million malaria cases and 409 malaria related death reported globally in 2019 [1]. Sub-Saharan African countries contribute 85% of the malaria cases globally with Nigeria accounting for 27% of the global malaria cases and 23% of global malaria deaths [1]. Nigeria, home of over 200 million people, has the majority of her population at high risk of malaria. Prevalence of malaria in children under five (CU5) is more than 50% in Kebbi state while Lagos, Imo and Anambra state have prevalence less than 10%. The remaining 32 states and the Federal Capital Territory have prevalence between 11% and 50% [2]. The prevalence of malaria among CU5 in Nigeria decreased from 27% in 2015 to 23% in 2018 [2]. Recent studies in sub-Saharan Africa revealed an increased malaria parasitaemia in school children [3–5]. Some published and unpublished studies in Nigeria have reported increased prevalence of malaria in children of school age. For instance, a study conducted in Bayelsa state reported higher prevalence of malaria among children 6–8 years old [6]. Another study conducted in Plateau and Abia states reported high prevalence among children 5–9 years [7]. While malaria in children of school age is not associated with severity, absenteeism from school and anaemia is common among this age group [8, 9].

Several interventions have been deployed globally to tackle morbidity and mortality due to malaria. These include prompt diagnostic tests to confirm malaria prior to treatment with artemisinin-based combination therapy (ACT), prevention of malaria in pregnant women, seasonal malaria chemoprevention in children 3–59 months and vector control using insecticide treated nets and in-door residual spraying. Vector control is one of the most important approaches for eradicating malaria as it aims to interrupt malaria transmission. While all these aforementioned interventions have been found to be effective, implementation has substantial cost implications [10, 11]. Consequently, insecticide treated net (ITN) use has been identified as the most cost-effective malaria intervention and has largely contributed to over 50% of malaria cases averted and reduction of child mortality by 27% [12, 13].

To harness the benefits of ITNs, increase population access to ITN and rapidly achieve universal coverage, mass ITN distribution campaigns have been implemented in Nigeria [14–16]. ITN campaigns conducted once every three years are implemented with a lot of SBCC messages and these messages were effective in improving net culture and use especially for vulnerable groups [16]. Keep-up channels using continuous distribution mechanism maintain coverage between ITN campaigns. Children under five and pregnant women who are most vulnerable to malaria are prioritized through routine net distribution channels during antenatal care services and immunization clinics [17–19].

The Nigeria National Malaria Strategic Plan (NMSP) aims at improving access and utilization of vector control interventions to 80% of the target population by 2025. Use of ITN by CU5 living in a household with at least one ITN increased from 58.6% in 2010 to 74.3% in 2018. However, the use of ITN by children of school age increased from 37.8% to 57.6% [2] with children of school age having the lowest proportion of ITN use in 2018 compared to other age group [2]. The low use of ITN by children of school age compared to other age group in the family has also been documented by Olapeju et al. [20].

Most studies on use of ITNs focus on general population with more emphasis on pregnant women and CU5 [21–25]. No study in Nigeria has been identified that explored the use of ITN in all children including children of school age and the progress made in achieving the ITN utilization target of 80% set by the NMSP at state level using a national survey. This study aims to conduct an analysis of ITN use in CU5 and children of school age at national and sub- national level with the aim of understanding ITN use for these children. Understanding factors associated with ITN use in children is timely to inform future intervention among this target group.

Methods

This cross-sectional study analyzed the 2018 Nigeria Demographic and Health Survey (NDHS) dataset. NDHS is a nationally representative survey with samples drawn from all states and Local Government Areas (LGAs) based on the sampling frame of enumeration areas in the country. Methods for sampling and fieldwork are described in the NDHS survey report. This study analyzed merged persons recode (PR) file and the household recode (HR) filtered for household identification number, any ITN in household and number of ITN in household. Data were adjusted for survey design clustering and non-response by applying the individual weight provided in the NDHS dataset to every analysis.

Target population

Target population for the analysis was CU5 (aged 0–4 years) and children of school age (aged 5–14 years) who slept under ITN the night before the survey in households with at least one ITN.

ITN campaign in Nigeria

In Nigeria, ITN campaign was conducted for the first time in 2009 and has been implemented on a rolling basis since then. In 2015, ITN campaign was conducted in Abia, Cross River, Ebonyi, Kano and Kaduna states. In 2016, only Benue and Oyo states conducted an ITN campaign. Adamawa, Edo, Imo, Kogi, Kwara, Ondo and Osun conducted ITN campaign in 2017 and Sokoto, Bauchi, Gombe, Jigawa, Katsina, Nasarawa, Ogun and Akwa Ibom states conducted ITN campaign in 2018. The following states did not conduct ITN campaign between 2015 and 2018: Zamfara, FCT, Niger, Yobe, Borno, Kebbi, Plateau, Taraba, Ekiti, Anambra Enugu, Rivers, Bayelsa, Delta and Lagos states.

Variables

Outcome variables

The outcome variable is ITN use in CU5 and children of school age. Use of ITN was defined as whether a child under five or 5–14 years living in a household that owns at least one ITN slept under an ITN the night before the survey.

Independent variables

The dataset was examined for variables of interest that were likely to influence the utilisation of ITNs. Literatures were also considered in identifying factors that could influence the utilization of ITN in children [20, 26, 27]. The independent variables considered for analysis were child sex, household characteristics such as head of household’s sex, household ownership of radio and television, number of household members, when ITN was obtained and wealth index. Demographic characteristics such as place of residence, state and region as well as malaria endemicity were included in the analysis. Malaria endemicity was classified into hypo-endemicity (states with prevalence less than 10%), meso-endemicity (10–50%) and hyperendemicity (51–75%) using state prevalence of malaria in CU5 obtained from 2018 NDHS. A study using Plasmodium falciparum parasite rate (PfPR) provided a basis for the classical categorical measures of malaria transmission into hypo-endemic (<10%), meso-endemic (10–50%), and hyper-endemic (51–75%) and this measure has been used in previous studies [28].

Statistical analysis

Statistical analysis was conducted with STATA version 14 and three levels of analysis conducted. Firstly, distribution of variables was conducted using frequency and proportion. Bivariate analysis was subsequently conducted to determine the level of association between the outcome variable, use of ITN in CU5 or children of school age with the independent variables with significant measure at p<0.05. Variable Inflation Factor (VIF) was calculated to determine the extent of the multi-collinearity of the independent variables and their suitability to be included into the multilevel analysis [29]. Variables with p-value <0.2 at bivariate level were included into a multi-level logistics regression model used to assess factors influencing the use of ITN in CU5 and children of school age. The nested structure of the demographic health survey (DHS) data in which children were selected from household within communities necessitated the use of the methodology. Use of multilevel logistic regression models for the analysis of DHS data has been documented and used severally in literature [29–31] therefore we would not document the theory in this paper. For this paper we constructed a model for CU5 and children of school age independently. Three models were constructed for each category which included the household model, the community level model and the combined model. The outcome variables for each model were use of ITN with “1” for use and “0” for non-use of ITN. We reported the variance and standard deviation at the household and community levels for each model, the residual, the log likelihood, the intraclass correlation, the Akaike information criteria and the Bayesian information criteria. Variables found to be correlated with other variables would be exempted from the logistic regression analysis. Significance was assessed based on 95% confidence interval of odds ratio not including 1

Ethical consideration

This work examined a population-based dataset accessed online from The Demographic Health Survey (DHS) Program. The DHS Program adheres to guidelines for protecting the privacy of respondents by removing all personal identifiers. As The DHS Program sought and received ethical approval before the survey, this research did not require any additional ethical approvals. However, The DHS Program granted permission to use the dataset for this work.

Results

Univariate

Data on 86,778 children were analyzed with 32,087 CU5 and 54,692 children of school age. While 70% (n = 22,440) of CU5 live in households with at least one ITN, 68.6% (n = 37,502) of children of school age live in households with at least one ITN. Table 1 shows the demographic characteristics of CU5 and children of school age. Half (50.8%) of the children were male and 88.9% of the head of households were male. The majority of households (59.9%) have a radio while 44.6% indicated that the household owns a television. About 43% of nets were obtained within the past 1–3 years with more CU5 (44.8%) obtaining their net within 1–3 years ago. About forty-one percent (40.6%) of children live in households with 4–6 persons. More CU5 live in households with 4–6 person (45%) compared to children of school age. About 60% of all children included in the analysis reside in rural areas, while 87% reside in meso-endemic area with prevalence between 11% and 50%.

Table 1. Demographic characteristics of CU5 (0–4) years and children of school age (5–14 years).

Variables	Children under five	Children of school age	Total
Variables	n (%)	n (%)	n (%)
Sex
Male	16,366 (51.0)	27,709 (50.7)	44,075 (50.8)
Female	15,721 (49.0)	26,983 (49.3)	42,704 (49.2)
Sex of head of household
Male	29,117 (90.8)	48,027 (87.8)	77,114 (88.9)
Female	2,969 (9.2)	6,665 (12.2)	86,778 (11.1)
Household own radio
No	13,361 (41.6)	21,469 (39.3)	34,831 (40.1)
Yes	18,726 (58.4)	33,222 (60.7)	51,948 (59.9)
Household own TV
No	17,760(55.4)	30,288 (55.4)	48,047 (55.4)
Yes	14,327 (44.6)	24,404 (44.6)	38,731 (44.6)
When ITN was obtained
Less than one year	5,705 (34.0)	7,811 (35.8)	13,516 (35.1)
1–3 years	7,507 (44.8)	9,101 (41.7)	16 609 (43.0)
More than 3 years	3,550 (21.2)	4,931 (22.6)	8,481 (22.0)
Number of Household members
1–3 persons	3,202 (10.0)	2,728 (5.0)	5,930 (6.8)
4–6 persons	14,442 (45.0)	20,750 (37.9)	35,192 (40.6)
7–9 persons	7,832 (24.4)	16,965 (31.0)	24,797 (28.6)
>9 persons	6,611 (20.6)	14,248 (26.1)	20,859 (24.0)
Wealth quintiles
Poorest	6,988 (21.8)	12,308 (22.5)	19,296 (22.2)
Poorer	7,109 (22.2)	11,574 (21.2)	18,682 (21.5)
Middle	6,587 (20.5)	11,041 (20.2)	17,628 (20.3)
Richer	5,948 (18.5)	10,370 (19.0)	16,318 (18.8)
Richest	5,456 (17.0)	9,398 (17.2)	14,854 (17.1)
Residence
Urban	12,638 (39.4)	22,439 (41.0)	35,077 (40.4)
Rural	19,448 (60.6)	32,253 (59.0)	51,701 (59.6)
Region
North Central	4,371 (13.6)	7,112 (13.0)	11,483 (13.2)
North East	5,885 (18.3)	10,483 (19.2)	16,368 (18.9)
North West	11,246 (35.1)	19,088 (34.9)	30,334 (35.0)
South East	3,393 (10.6)	5,220 (9.6)	8,613 (9.9)
South South	2,915 (9.1)	5,289 (9.7)	8,204 (9.5)
South West	4,276 (13.3)	7,500 (13.7)	11,776 (13.6)
States
Abia	426 (1.3)	607 (1.1)	1,033 (1.2)
Cross river	304 (1.0)	595 (1.1)	899 (1.0)
Ebonyi	824 (2.6)	1,287 (2.4)	2,111 (2.4)
Kano	2,471 (7.7)	4,463 (8.2)	6,934 (8.0)
Kaduna	2,090 (6.5)	3,349 (5.9)	5,339 (6.2)
Benue	921 (2.9)	1,276 (2.3)	2,197 (2.5)
Oyo	944 (2.9)	1,567 (2.9)	2,512 (2.9)
Adamawa	757 (2.4)	1,181 (2.2)	1,938 (2.2)
Edo	409 (1.3)	786 (1.4)	1,195 (1.4)
Imo	652 (2.0)	1,010 (1.9)	1,662 (1.9)
Kogi	380 (1.2)	681 (1.3)	1,061 (1.2)
Kwara	518 (1.6)	983 (1.8)	1,501 (1.7)
Ondo	392 (1.2)	751 (1.4)	1,142 (1.3)
Osun	565 (1.8)	962 (1.8)	1,526 (1.8)
Sokoto	921 (2.9)	1,514 (2.8)	2,435 (2.8)
Bauchi	1,383 (4.3)	2,445 (4.5)	3,829 (4.4)
Gombe	647 (2.0)	1,167 (2.1)	1,814 (2.1)
Jigawa	1,319 (4.1)	2,357 (4.3)	3,677 (4.34)
Katsina	2,203 (6.9)	3,737 (6.8)	5,940 (6.8)
Nasarawa	494 (1.5)	798 (1.5)	1,292 (1.5)
Ogun	606 (1.9)	1,015 (1.9)	1,621 (1.9)
Akwa Ibom	517 (1.6)	950 (1.7)	1,467 (1.6)
Zamfara	1,209 (3.8)	2,053 (3.8)	3,262 (3.8)
FCT Abuja	217 (0.7	349 (0.6)	566 (0.7)
Niger	1,226 (3.8)	1,899 (3.5)	3,125 (3.6)
Yobe	1,210 (3.8)	2,327 (4.3)	3,537 (4.1)
Borno	1,146 (3.6)	2,155 (3.9)	3,300 (3.8)
Kebbi	1,034 (3.2)	1,715 (3.1)	2,749 (3.2)
Plateau	615 (1.9)	1,125 (2.1)	1,740 (2.0)
Taraba	743 (2.3)	1,208 (2.2)	1,950 (2.3)
Ekiti	304 (1.0)	513 (0.9)	817 (0.9)
Anambra	1,029 (3.2)	1,436 (2.6)	2,464 (2.8)
Enugu	462 (1.4)	880 (1.6)	1,342 (1.6)
Rivers	877 (2.7)	1,437 (2.6)	2,314 (2.7)
Bayelsa	224 (0.7)	422 (0.8)	1,682 (0.7)
Delta	584 (1.8)	1,099 (2.0)	1,683 (1.9)
Lagos	1,466 (4.6)	2,692 (4.9)	4, 158 (4.8)
Malaria endemicity
Hypoendemic	3,147 (9.8.0)	5,139 (9.4)	8,285 (9.5)
Mesoendemic	27,906 (87)	47,838 (87.5)	75,744 (87.3)
Hyperendemic	1,034 (3.2)	1,715 (3.1)	2,749 (3.2)
Ownership of ITN
No ITN	9,647(30)	17,190(31.4)	26,837(30.9)
At least 1 ITN	22,440(70)	37,502(68.6)	59,942(69.1)
Total	32,087 (100)	54,692 (100)	86,778 (100)

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Bivariate

Table 2 presents the results of the bivariate analyses of ITN use in CU5 and children of school age by demographic characteristics. Findings among CU5 show that out of 22,440 children that live in households with at least one ITN, 74.3% (n = 16,671) slept under ITN the night before the survey. ITN use was associated with ownership of radio, television, when ITN was obtained, number of household members, wealth quintiles, place of residence, region, states and malaria endemicity (p < 0.05). Children under five from households with less than 6 members were most likely to sleep under an ITN. Similarly, CU5 who obtained ITN less than 3 years (99.7%) were also more likely to sleep under ITN.

Table 2. Use of ITN in CU5 and children of school age living in households with at least one ITN by demographic characteristics.

Variable	Children under five		Children of school age
	Slept under ITN	P Value	Slept under ITN	P Value
	(n = 16,671)	P Value	n = 21,690	P Value
Sex	n (%)	<0.417	n (%)	<0.001
Male	8,353 (74.6)		10,582 (55.9)
Female	8,148 (74.0)		11,109 (59.8)
Sex of head of household		0.712		0.001
Male	15,277 (74.2)		19,158 (57.3)
Female	1,394 (74.8)		2,532 (62.2)
Household own radio		<0.001		0.267
No	7,316 (76.7)		8,800 (58.5)
Yes	9,355 (72.5)		12,890 (57.4)
Household own TV		<0.001		0.01
No	10,268 (77.5)		13,248 (59.1)
Yes	6,404 (69.7)		8,442 (56.0)
When ITN was obtained		<0.046		<0.665
Less than one year	18 (0.3)		7,708 (99.6)
1–3 years	21 (0.3)		8,965 (99.5)
More than 3 years	27 (0.8)		4,819 (99.5)
Number of Household members		<0.001		<0.001
1–3 persons	1,755 (85.8)		1,018 (69.3)
4–6 persons	7,608(78.0)		8,630(64.8)
7–9 persons	3,980 (71.3)		6,891 (57.9)
>9 persons	3,327 (65.7)		5,151 (47.6)
Wealth quintiles		<0.001		<0.021
Poorest	4,165 (77.9)		5,507 (58.0)
Poorer	4,179 (76.8)		5.219 (60.1)
Middle	3,525 (75.4)		4,487 (59.0)
Richer	2,667 (70.1)		3,639 (54.9)
Richest	2,136 (67.3)		2,838 (55.8)
Residence		<0.001		<0.116
Urban	5,640 (71.5)		7,766 (56.6)
Rural	11,032 (75.8)		13,925 (58.6)
Region		<0.001		<0.001
North Central	2,057 (76.2)		2,314 (56.5)
North East	2,777 (69.7)		3,648 (51.8)
North West	8,138 (80.3)		10,644 (62.3)
South East	1,234 (66.2)		1,493 (54.4)
South South	975 (63.1)		1,447 (52.9)
South West	1,490 (67.5)		2,146 (56.3)
		<0.001		<0.001
States
Abia	101 (47.7)		134 (45.3)
Cross River	130 (71.6)		234 (62.5)
Ebonyi	556 (89.1)		777 (79.4)
Kano	1,827 (82.3)		2,579 (65.9)
Kaduna	1,386 (78.0)		1,667 (61.4)
Benue	595 (93.1)		641 (76.2)
Oyo	389 (76.9)		597 (73.6)
Adamawa	360 (90.0)		478 (75.5)
Edo	126 (53.1)		182 (38.9)
Imo	196 (51.6)		199 (33.7)
Kogi	205 (71.0)		291 (58.8)
Kwara	172 (50.8)		238 (36.7)
Ondo	232 (69.8)		340 (60.7)
Osun	179 (62.9)		265 (49.7)
Sokoto	516 (64.1)		529 (40.5)
Bauchi	723 (60.8)		803 (38.7)
Gombe	246 (52.6)		303 (34.1)
Jigawa	1,168 (90.9)		1,941 (83.9)
Katsina	1,597 (77.5)		2,266 (65.0)
Nasarawa	281 (70.9)		338 (58.9)
Ogun	296(78.4)		404 (67.5)
Akwa Ibom	189 (52.4)		236 (37.7)
Zamfara	678 (69.7)		395 (23.8)
Yobe	712 (78.1)		1,080 (63.1)
Borno	559 (77.2)		792 (62.2)
Kebbi	967 (95.0)		1,268 (75.3)
Niger	450 (74.9)		391 (44.7)
FCT Abuja	76 (68.4)		67 (46.7)
Plateau	278 (85.6)		349 (66.4)
Taraba	178 (60.4)		192 (42.2)
Ekiti	79 (52.9)		112 (45.2)
Anambra	259 (59.5)		234 (46.0)
Enugu	123 (56.9)		147 (40.1)
Rivers	243 (65.1)		336 (59.8)
Bayelsa	71 (69.7)		117 (59.3)
Delta	216 (74.5)		341 (67.6)
Lagos	315 (56.5)		369 (38.4)
Malaria endemicity		<0.001		<0.001
Hypoendemic	770 (56.1)		802 (38.9)
Mesoendemic	14,935 (74.5)		19,620 (58.1)
Hyperendemic	967 (95.0)		1,268 (75.3)
Total	16,671(74.3)		21,690 (57.8%)

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A higher proportion of CU5 (77.9%) from the poorest wealth quintiles slept under ITN compared with those from the highest wealth quintile (67%). Use of ITN was significantly associated (p<0.001) with place of residence. About 75.8% of CU5 from rural areas slept under ITN compared to 71% from urban areas. A substantial geographical variation was noticed on use of ITN with CU5 from North West region being most likely to sleep under an ITN (80.3%) while in South-South, 63.1% of CU5 slept under ITN. Under five children living in malaria hypo-endemic area were least likely to sleep under ITN (56.1%) compared with 95% of under five children in hyperendemic areas. Considering the states with up to 80% ITN utilization in CU5, Ebonyi state (89.1%), Kano states (82.3%), Benue state (93.1%), Adamawa (90.0%) Kebbi (95.0%) Plateau (85.6%) and Jigawa state (90.9%) had above 80% ITN utilization in CU5.

Turning attention to findings on children of school age, out of 37,502 children of school age that live in households with least one ITN, 57.8% (n = 21,690) slept under ITN the night before the survey. Similar significant associations as in the case of CU5 were observed. While no differentials of child’s sex, head of household sex and ownership of television were evident in the case of CU5, these variables were significantly associated with ITN use among children of school age. Furthermore, ITN use is significantly associated with number of household members, wealth quintiles, region, state and malaria endemicity. Considering the states with up to 80% ITN utilization in children of school age, only Jigawa state has over 80 percent of children of school age that slept under ITN the night before the survey. Table 3 with crude odds ratio (COR) of factors associated with ITN use in CU5 and children of school age is included in S1 File.

Multivariate results

The VIF computation done before fitting the multilevel logistic regression models for under five and above five revealed a mean VIF score of 3.25 and 3.91 for CU5 and children of school age respectively after removing the variable state and region due to collinearity.

Table 3 presents the results of the multilevel logistic regress with three models for each of CU5 and school age children. For the CU5, the household model demonstrated that children from richer (aOR = 0.25, 95% CI 0.09 to 0.65) and richest (aOR = 0.18, 95% CI 0.06 to 0.53) wealth quintile were less likely to utilize ITN, the community model demonstrated that households in rural area (aOR = 1.20, 95% CI 1.14 to 1.26) and in meso endemic (aOR = 3.10, 95% CI 2.89 to 3.54) areas were more likely to use ITN. The combined model for CU5 demonstrated that having TV (aOR = 2.14, 95% CI 1.02 to 4.50) and living in rural areas (aOR = 2.93, 95% CI 1.14 to 1.26) contributed to using ITN while households with 4–6 persons (aOR = 0.37, 95% CI 0.16 to 0.89), 7 to 9 persons (aOR = 0.34, 95% CI 0.14 to 0.85), and >9 persons (aOR = 0.35, 95% CI 0.14 to 0.90) and households from richest quintiles (aOR = 0.19, 95% CI 0.04 to 0.78) were less likely to use ITN. Variability in use of ITN was highest in the household with an intraclass correlation of 14%.

Table 3. Multilevel logistic regression on socio and demographic factors associated with ITN use in CU5 and children of school age in households owning at least one ITN.

Fixed Effect
Characteristics	Categories	CU5 aOR (95% CI)			Children of school age aOR (95% CI)
Characteristics	Categories	Household	Community	Combined	Household	Community	Combined
Sex of head of household	Male
Sex of head of household	Female				1.14(0.89,2.17)		1.14(1.09,1.19)
Household own radio	No
Household own radio	Yes	1.29(0.87,1.92)
Household own TV	No
Household own TV	Yes	1.82(0.95,3.50)		2.14(1.02,4.50)	1.09(1.02,4.5)		1.08(1.00,1.15)
When net was obtained	Less than one year1
	1–3 years	0.78(0.5,1.23)		0.76(0.45,1.27)
	More than 3 years	1.03(0.58,1.85)		0.95(0.50,1.81)
Number of Household members	1 to 3 persons
	4 to 6 persons	1.05(0.59,1.86)		0.37(0.16,0.89)	0.93(0.46,1.27)		0.92(0.84,1.02)
	7 to 9 persons	1.29(0.67,2.48)		0.34(0.14,0.85)	0.67(0.5,1.82)		0.66(0.59,0.72)
	>9 persons	1.06(0.54,2.07)		0.35(0.14,0.90)	0.4(0.16,0.88)		0.38(0.35,0.43)
Socio Economic Status	Poorest
	Poorer	0.71(0.38,1.32)		0.61(0.30,1.25)	1.01(0.14,0.86)		1.03(0.96,1.11)
	Middle	1.22(0.49,3.01)		1.49(0.49,4.51)	1.02(0.14,0.9)		1.08(0.99,1.19)
	Richer	0.25(0.09,0.65)		0.3(0.08,1.06)	1.05(0.3,1.25)		1.15(1.02,1.29)
	Richest	0.18(0.06,0.53)		0.19(0.04,0.78)	1.14(0.49,4.52)		1.30(1.14,1.49)
Residence	Urban
Residence	Rural		1.20(1.14,1.26)	2.93(0.84,10.24)		1.08(1.04,1.12)	1.37(1.19,1.59)
Malaria endemicity	Hypo endemic
	Meso endemic		3.10(2.89,3.54)	3.29(0.66,16.42)		3.17(2.89,3.47)	4.60(3.58,5.90)
	Hyper endemic					14.9(13,17.07)	36.10(22.41,58.14)
Random Effect
	Household(Variance (Std.Dev.))	33.47(5.79)		12.94(3.60)	1.69(1.30)		1.37(1.17)
	Cluster (Variance (Std.Dev.))		0.01(0.11)	0.49(0.7)		0.015(0.12)	0.01(0.12)
	Residual	1652	41786.4	1643.8	61472.7	70155.9
	Log likelihood	-846.2	-20893.2	-821.9	-30736.3	-35077.9	-30579.3
	ICC	14%	30%	17%	23%	30%	20%
	AIC	1720.4	41796.4	1677.8	61494.7	70165.9	61188.5
	Bayesian IC	1827.9	41838.2	1808.4	61592.6	70210.3	61322

Open in a new tab

For the school age children’s models, the household model demonstrated that children from household with female head (aOR = 1.14, 95% CI 1.09 to 1.19) and having a television (aOR = 1.09, 95% CI 1.01 to 1.16) were likely to use ITN while children living in household with 7 to 9 persons (aOR = 0.67, 95% CI 0.60 to 0.74) or >9 persons (aOR = 0.39, 95% CI 0.36 to 0.44) were less likely to use ITN. The community model demonstrated that children living in rural (aOR = 1.08, 95% CI 1.04 to 1.12), coming from meso endemic (aOR = 3.17, 95% CI 2.89 to 3.47) and hyper endemic (aOR = 14.9, 95% CI 12.99 to 17.07) areas were more likely to use ITN. The combined model demonstrated that children from households with female head (aOR = 1.14, 95% CI 1.1 to 1.19), having television (aOR = 1.08, 95% CI 1.01 to 17.07), coming from richer (aOR = 1.15, 95% CI 1.03 to 1.29) or richest (aOR = 1.31, 95% CI 1.14 to 1.49) wealth quintile, living in rural areas (aOR = 1.38, 95% CI 1.2 to 1.59) and coming from meso endemic (aOR = 4.6, 95% CI 3.59 to 5.9) or hyper endemic area (aOR = 36.08, 95% CI 22.39 to 58.14) were more likely to use ITN. while children from households with 7 to 9 persons (aOR = 0.66, 95% CI 0.6 to 0.73) or > 9 persons (aOR = 0.39, 95% CI 0.35 to 0.43) were less likely to use ITN. The variability in the school age children’s models was higher in the household (1.37(1.17)) while the intraclass correlation was 20%.

Discussions

This paper explored ITN use in CU5 and children of school age in Nigeria using a nationally representative study. Compared to the NDHS report, our study has established associated factors that influence ITN use in CU5 and children of school age by developing the novel multilevel logistic regression model to identify these factors [32]. Findings revealed that household and community- level factors were significantly associated with ITN use in CU5 and children of school age. Factors associated with use of ITN in CU5 include living in rural areas, meso and hyper endemic areas, poor households and owning a television. Similarly, factors associated with use of ITN in children of school age include living in rural area, meso and hyper endemic areas, female headed households, belonging to richer and richest wealth quintile and having television. These findings provide further direction for the malaria elimination strategy on how to enhance ITN use among CU5 and children of school age for malaria elimination effort.

The prevalence of malaria has been documented to be higher in rural areas and in poor population [33–35]. Findings from this study which revealed increased use of ITN in CU5 living in households in lowest wealth quintiles, rural areas showed the effectiveness of ITN campaign in increasing use of ITN to vulnerable and poor population with higher prevalence of malaria.

Furthermore, the finding demonstrates that effort of the malaria control programme in Nigeria to deploy ITN to areas of core endemicity to reduce prevalence and incidence of malaria in these areas is significant. With higher malaria prevalence reported in the northern region which mostly makes up the hyper endemic areas compared to the southern region [36], we can say that efforts to reduce malaria prevalence through use of ITN are effective and needs to be fortified to achieve greater success.

In addition, Higher ITN use and association seen in the meso and hyper endemic areas of the country also shows that people living in these areas may feel more vulnerable to malaria and hence make consistent efforts to use ITNs. Hence, location with hyper endemicity were seen to have high odds of ITN use [27]. The National Malaria Strategic Plan set target of 80% utilization for vector control intervention has not been determined at sub-national level. At state level, only seven states (Ebonyi, Kano, Benue, Jigawa, Kebbi, Plateau, Adamawa) out of the Nigerian 36 states plus FCT had over 80% ITN utilization among CU5. While only Jigawa state had 80% ITN utilization for children of school age. This result reinforces the need for concerted efforts by relevant stakeholders to increase ITN utilization in states where utilization is low, more especially in states that lack donor funding for ITN campaign.

Children of school age in richer and richest wealth quintile were more likely to use ITN. While previous studies showed that children of school age were less prioritized in ITN use at household level [20]. This study further revealed use to be higher in children of school age from rich households. Efforts should be made by the malaria programme to increase use of ITN in school children from poor households.

WHO recommends a combination of ITN mass campaign, continuous distribution of ITN through multiple channels and several other intervention strategies to eliminate malaria. In Nigeria, continuous distribution via antenatal care and immunization has been prioritized with pockets of school distribution implemented in the country. Plateau state that has been documented to have high prevalence of malaria in children of school age has about one out of every three school age children sleep under ITN. Considering the reported high prevalence of malaria among children of school age, including school distribution of ITN as one of the keep-up channels for achieving universal coverage in Nigeria could be a possible strategy for increasing use of ITN by school children from poor households. Increasing ITN distribution through schools could also sustain the decline in malaria prevalence recorded over the years as school children are reported to be an asymptomatic reservoir for malaria parasites and are the least prioritized in ITN use [20]. Findings also show that children in households with more household members were less likely to sleep under ITN. This finding further buttresses the need to consider family sizes during ITN distribution, as currently there is a cap on maximum number of nets to be given to a household.

Study strength

The main strength of this study is the representative sample at state, regional and national level to guide ITN utilization strategy and decision making. In addition, while the DHS report presented ITN use in CU5 and children of school age at national level, this paper presents ITN utilization at sub- national levels.

Study limitation

On the limitation of the study, information on ITN use in children was not verified. The question on use of ITN by children was asked mothers and caregivers of these children and could be subject to social desirability bias (respondents may want to show in their response that they are taking care of their children by making them sleep under ITNs even if this is not true). In addition, the cross-sectional nature of the study design is a limitation to the study as the cause relationship between use of ITN and the observed predisposing factors could not be established. The study was also not able to measure co-variates such as climatic factors, seasonality etc which influence prevalence of malaria. Finally, similar to studies of this nature, this study was not able to measure all factors that could influence ITN use.

Conclusion

Our study demonstrates higher use of ITN among CU5 and children of school age living in malaria endemic areas and rural areas. However associated factors differ in CU5 and children of school age. To achieve the national target of ITN utilization in children, the authors recommend concerted efforts to increase ITN use in states where utilization remain low while reviewing further household size during ITN campaign. We recommend ITN distribution via schools should be considered as one of the continuous distribution channels to increase use of ITN among children of school age.

Supporting information

S1 File. Supplementary file containing Table 3.

Factors associated with ITN use in CU5 and children of school age.

(DOCX)

Click here for additional data file.^{(17.8KB, docx)}

Acknowledgments

The authors wish to thank Cameron Taylor of DHS Programs for reviewing this work. Your valuable contribution is highly appreciated.

Data Availability

The data underlying the results presented in this study are available on DHS program website https://www.dhsprogram.com/data/.

Funding Statement

The authors received no funding for this work.

References

1.WHO. World malaria report 2020: 20 years of global progress and challenges. Geneva: World Health Organization; 2020. Licence: CC BY-NC-SA 3.0 IGO. 2021 update. Geneva: World Health Organization; 2021. https://apps.who.int/iris/handle/10665/342995
2.National Population Commission (NPC) [Nigeria] and ICF. 2019. Nigeria Demographic and Health Survey 2018. Abuja, Nigeria, and Rockville, Maryland, USA: NPC and ICF. https://dhsprogram.com/pubs/pdf/FR359/FR359.pdf
3.Sarpong N, Owusu-Dabo E, Kreuels B, Fobil JN, Segbaya S, Amoyaw F, et al. Prevalence of malaria parasitaemia in school children from two districts of Ghana earmarked for indoor residual spraying: a cross-sectional study. Malar J. 2015;14: 260. doi: 10.1186/s12936-015-0772-6 [DOI] [PMC free article] [PubMed] [Google Scholar]
4.Yapi RB, Hürlimann E, Houngbedji CA, Ndri PB, Silué KD, Soro G, et al. Infection and Co-infection with Helminths and Plasmodium among School Children in Côte d’Ivoire: Results from a National Cross-Sectional Survey. Stothard JR, editor. PLoS Negl Trop Dis. 2014;8: e2913. doi: 10.1371/journal.pntd.0002913 [DOI] [PMC free article] [PubMed] [Google Scholar]
5.Walldorf JA, Cohee LM, Coalson JE, Bauleni A, Nkanaunena K, Kapito-Tembo A, et al. School-Age Children Are a Reservoir of Malaria Infection in Malawi. Snounou G, editor. PLoS ONE. 2015;10: e0134061. doi: 10.1371/journal.pone.0134061 [DOI] [PMC free article] [PubMed] [Google Scholar]
6.Hart A, Abinye B. The prevalence of malaria parasitaemia in children 0–11 years in twon-brass. World Journal of Pharmaceutical and Medical Research 2020;6:25–29. 2020 [cited 26 Nov 2021]. https://journals.indexcopernicus.com/search/article?articleId=2473132 [Google Scholar]
7.Noland GS, Graves PM, Sallau A, Eigege A, Emukah E, Patterson AE, et al. Malaria prevalence, anemia and baseline intervention coverage prior to mass net distributions in Abia and Plateau States, Nigeria. BMC Infect Dis. 2014;14: 168. doi: 10.1186/1471-2334-14-168 [DOI] [PMC free article] [PubMed] [Google Scholar]
8.Cohee L, Laufer M. Tackling malaria transmission in sub-Saharan Africa. The Lancet Global Health. 2018;6: e598–e599. doi: 10.1016/S2214-109X(18)30197-9 [DOI] [PMC free article] [PubMed] [Google Scholar]
9.Nzobo BJ. Prevalence of asymptomatic malaria infection and use of different malaria control measures among primary school children in Morogoro Municipality, Tanzania. 2015; 7. [DOI] [PMC free article] [PubMed]
10.Ezenduka CC, Falleiros DR, Godman BB. Evaluating the Treatment Costs for Uncomplicated Malaria at a Public Healthcare Facility in Nigeria and the Implications. Pharmacoecon Open. 2017;1: 185–194. doi: 10.1007/s41669-017-0021-8 [DOI] [PMC free article] [PubMed] [Google Scholar]
11.Conteh L, Shuford K, Agboraw E, Kont M, Kolaczinski J. Costs and Cost-Effectiveness of Malaria Control Interventions: A Systematic Literature Review | Elsevier Enhanced Reader. Value Health. 2021; 24(8):1213–1222. 2021 [cited 10 Dec 2021]. doi: 10.1016/j.jval.2021.01.013 [DOI] [PMC free article] [PubMed] [Google Scholar]
12.Mueller DH, Wiseman V, Bakusa D, Morgah K, Daré A, Tchamdja P. Cost-effectiveness analysis of insecticide-treated net distribution as part of the Togo Integrated Child Health Campaign. Malar J. 2008;7: 73. doi: 10.1186/1475-2875-7-73 [DOI] [PMC free article] [PubMed] [Google Scholar]
13.Bhatt S, Weiss DJ, Cameron E, Bisanzio D, Mappin B, Dalrymple U, et al. The effect of malaria control on Plasmodium falciparum in Africa between 2000 and 2015. NATURE. 2015;526: 207–211. doi: 10.1038/nature15535 [DOI] [PMC free article] [PubMed] [Google Scholar]
14.Ankomah A, Adebayo SB, Arogundade ED, Anyanti J, Nwokolo E, Inyang U, et al. The Effect of Mass Media Campaign on the Use of Insecticide-Treated Bed Nets among Pregnant Women in Nigeria. Malaria Research and Treatment. 2014;2014: 1–7. doi: 10.1155/2014/694863 [DOI] [PMC free article] [PubMed] [Google Scholar]
15.Ye Y, Patton E, Kilian A, Dovey S, Eckert E. Can universal insecticide-treated net campaigns achieve equity in coverage and use? the case of northern Nigeria. Malaria Journal. 2012;11: 32. doi: 10.1186/1475-2875-11-32 [DOI] [PMC free article] [PubMed] [Google Scholar]
16.Kilian A, Lawford H, Ujuju CN, Abeku TA, Nwokolo E, Okoh F, et al. The impact of behaviour change communication on the use of insecticide treated nets: a secondary analysis of ten post-campaign surveys from Nigeria. Malar J. 2016;15: 422. doi: 10.1186/s12936-016-1463-7 [DOI] [PMC free article] [PubMed] [Google Scholar]
17.Hill J, Dellicour S, Bruce J, Ouma P, Smedley J, Otieno P, et al. Effectiveness of Antenatal Clinics to Deliver Intermittent Preventive Treatment and Insecticide Treated Nets for the Control of Malaria in Pregnancy in Kenya. von Seidlein L, editor. PLoS ONE. 2013;8: e64913. doi: 10.1371/journal.pone.0064913 [DOI] [PMC free article] [PubMed] [Google Scholar]
18.Theiss-Nyland K, Koné D, Karema C, Ejersa W, Webster J, Lines J. The relative roles of ANC and EPI in the continuous distribution of LLINs: a qualitative study in four countries. Health Policy and Planning. 2017;32: 467–475. doi: 10.1093/heapol/czw158 [DOI] [PubMed] [Google Scholar]
19.Mathanga DP, Luman ET, Campbell CH, Silwimba C, Malenga G. Integration of insecticide-treated net distribution into routine immunization services in Malawi: a pilot study. Tropical Medicine & International Health. 2009;14: 792–801. doi: 10.1111/j.1365-3156.2009.02295.x [DOI] [PubMed] [Google Scholar]
20.Olapeju B, Choiriyyah I, Lynch M, Acosta A, Blaufuss S, Filemyr E, et al. Age and gender trends in insecticide-treated net use in sub-Saharan Africa: a multi-country analysis. Malaria Journal. 2018;17: 423. doi: 10.1186/s12936-018-2575-z [DOI] [PMC free article] [PubMed] [Google Scholar]
21.Ankomah A, Adebayo SB, Arogundade ED, Anyanti J, Nwokolo E, Ladipo O, et al. Determinants of insecticide-treated net ownership and utilization among pregnant women in Nigeria. BMC Public Health. 2012;12: 105. doi: 10.1186/1471-2458-12-105 [DOI] [PMC free article] [PubMed] [Google Scholar]
22.Babalola OJ, Sambo MN, Idris SH, Ajayi I-OO, Ajumobi O, Nguku P. Factors associated with utilization of LLINs among women of child-bearing age in Igabi, Kaduna State, Nigeria. Malar J. 2019;18: 412. doi: 10.1186/s12936-019-3046-x [DOI] [PMC free article] [PubMed] [Google Scholar]
23.Baume CA, Marin MC. Gains in awareness, ownership and use of insecticide-treated nets in Nigeria, Senegal, Uganda and Zambia. Malaria Journal. 2008;7: 153. doi: 10.1186/1475-2875-7-153 [DOI] [PMC free article] [PubMed] [Google Scholar]
24.Ng’ang’a PN, Aduogo P, Mutero CM. Long lasting insecticidal mosquito nets (LLINs) ownership, use and coverage following mass distribution campaign in Lake Victoria basin, Western Kenya. BMC Public Health. 2021;21: 1046. doi: 10.1186/s12889-021-11062-7 [DOI] [PMC free article] [PubMed] [Google Scholar]
25.Sina OJ. An Assessment of the use of Insecticide Treated Net among under Five in Ekiti State as a Means of Prevention of Malaria. PRM. 2018;2. doi: 10.31031/PRM.2018.02.000544 [DOI] [Google Scholar]
26.Mensah EA, Anto F. Individual and Community Factors Associated with Household Insecticide-Treated Bednet Usage in the Sunyani West District of Ghana Two Years after Mass Distribution. Journal of Environmental and Public Health. 2020;2020: 1–7. doi: 10.1155/2020/7054383 [DOI] [PMC free article] [PubMed] [Google Scholar]
27.Nkoka O, Chipeta MS, Chuang Y-C, Fergus D, Chuang K-Y. A comparative study of the prevalence of and factors associated with insecticide-treated nets usage among children under 5 years of age in households that already own nets in Malawi. Malaria Journal. 2019;18: 43. doi: 10.1186/s12936-019-2667-4 [DOI] [PMC free article] [PubMed] [Google Scholar]
28.Smith DL, Guerra CA, Snow RW, Hay SI. Standardizing estimates of the Plasmodium falciparum parasite rate. Malar J. 2007;6: 131. doi: 10.1186/1475-2875-6-131 [DOI] [PMC free article] [PubMed] [Google Scholar]
29.Bl S, Sa R. Multilevel analysis of factors associated with assistance during delivery in rural Nigeria: implications for reducing rural-urban inequity in skilled care at delivery. BMC pregnancy and childbirth. 2018;18. doi: 10.1186/s12884-018-2074-9 [DOI] [PMC free article] [PubMed] [Google Scholar]
30.Women who have not utilized health Service for Delivery in Nigeria: who are they and where do they live? | BMC Pregnancy and Childbirth | Full Text. [cited 2 Aug 2022]. https://bmcpregnancychildbirth.biomedcentral.com/articles/10.1186/s12884-019-2242-6 [DOI] [PMC free article] [PubMed]
31.Aheto JMK, Pannell O, Dotse-Gborgbortsi W, Trimner MK, Tatem AJ, Rhoda DA, et al. Multilevel analysis of predictors of multiple indicators of childhood vaccination in Nigeria. PLOS ONE. 2022;17: e0269066. doi: 10.1371/journal.pone.0269066 [DOI] [PMC free article] [PubMed] [Google Scholar]
32.Aheto JMK, Dagne GA. Multilevel modeling, prevalence, and predictors of hypertension in Ghana: Evidence from Wave 2 of the World Health Organization’s Study on global AGEing and adult health. Health Science Reports. 2021;4: e453. doi: 10.1002/hsr2.453 [DOI] [PMC free article] [PubMed] [Google Scholar]
33.Habyarimana F, Ramroop S. Prevalence and Risk Factors Associated with Malaria among Children Aged Six Months to 14 Years Old in Rwanda: Evidence from 2017 Rwanda Malaria Indicator Survey. Int J Environ Res Public Health. 2020; 13. doi: 10.3390/ijerph17217975 [DOI] [PMC free article] [PubMed] [Google Scholar]
34.Gahutu J-B, Steininger C, Shyirambere C, Zeile I, Cwinya-Ay N, Danquah I, et al. Prevalence and risk factors of malaria among children in southern highland Rwanda. Malaria Journal. 2011;10: 134. doi: 10.1186/1475-2875-10-134 [DOI] [PMC free article] [PubMed] [Google Scholar]
35.Yusuf OB, Adeoye BW, Oladepo OO, Peters DH, Bishai D. Poverty and fever vulnerability in Nigeria: a multilevel analysis. Malaria Journal. 2010;9: 235. doi: 10.1186/1475-2875-9-235 [DOI] [PMC free article] [PubMed] [Google Scholar]
36.Oyibo W, Ntadom G, Uhomoibhi P, Oresanya O, Ogbulafor N, Ajumobi O, et al. Geographical and temporal variation in reduction of malaria infection among children under 5 years of age throughout Nigeria. BMJ Glob Health. 2021;6: e004250. doi: 10.1136/bmjgh-2020-004250 [DOI] [PMC free article] [PubMed] [Google Scholar]

PLoS One. doi: 10.1371/journal.pone.0274160.r001

Decision Letter 0

Clement Ameh Yaro

4 Apr 2022

PONE-D-22-02975Use of insecticide treated nets in children under five and children of school age in Nigeria: evidence from a secondary data analysis of demographic health surveyPLOS ONE

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Additional Editor Comments:

EDITOR’S COMMENTS

Dear Authors,

The manuscript requires major revision. The authors should critically attend all the comments raised by the reviewers. The multivariate analysis needs to be re-examined. Also, the conclusion should be rewritten to capture the findings of the study.

FIRST REVIEWER

This paper examined the usage of insecticide-treated nets in children under five and school-going children in Nigeria using a secondary analysis of demographic and health survey data. Children are important aspect of malaria elimination program across the world and this paper presents some concerns that the authors need to addressed.

Major comments

1. As the world moves toward elimination, it would bring more valuable information to look into how stratification of malaria prevalence among children at sub-national level would look like under hypo, meso, and hyper endemic regions and how the usages of ITN net were distributed in such stratification.

2. What is the mechanism that could link the sex of the child being female and using ITN in a household? On the other hand, gender of the head of the household is not associated with the ITN use, While sex of the child is of little value for informing policy, I would suggest the authors to use a valid theoretical framework or literature to select variables for the model.

3. The multivariate model showed that malaria endemicity is a predictor for the ITN usage. NCMP usually puts much efforts to bring down the incidence and prevalence in malaria endemic regions, hence this finding suggested that interventions to increase the usage of ITN are working well. Please discuss this point in the paper.

4. The conclusion of “increase availability and access to ITN has resulted … “ is not supported by the findings in the paper. There is no results in the paper showing that the ITN availability or access to ITN increases.

Minor comments

1. The introduction section can be shortened by moving paragraphs between lines 83 and 110 to the methods section.

2. Table 3: put 1 or indicate as “reference” in the reference categories, rather than indicating with a symbol.(less...)

SECOND REVIEWER

Major comments:

1. Why it is called retrospective cross-sectional study?

2. Result session:

There were 32087 U5C in table 1, but slept under ITN (16,671) and not slept (5,769).

There were 54692 children of school age in table 1, but slept under ITN (21,690) and not slept (15,812).

Table 3 should be multivariable logistics regression and use AOR. Need to add reference group.

Please see the table for multivariable logistics regression from other international publication.

3. What is your implication of the study for Nigeria? What is the different information provided by your analysis compared to NDHS findings?

4. Conclusion should provide the specific input to program.

[Note: HTML markup is below. Please do not edit.]

Reviewers' comments:

Reviewer's Responses to Questions

Comments to the Author

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Reviewer #1: No

Reviewer #2: Partly

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2. Has the statistical analysis been performed appropriately and rigorously?

Reviewer #1: Yes

Reviewer #2: No

**********

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Reviewer #1: Yes

Reviewer #2: Yes

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Reviewer #1: Yes

Reviewer #2: Yes

**********

5. Review Comments to the Author

Please use the space provided to explain your answers to the questions above. You may also include additional comments for the author, including concerns about dual publication, research ethics, or publication ethics. (Please upload your review as an attachment if it exceeds 20,000 characters)

Reviewer #1: This paper examined the usage of insecticide-treated nets in children under five and school-going children in Nigeria using a secondary analysis of demographic and health survey data. Children are important aspect of malaria elimination program across the world and this paper presents some concerns that the authors need to addressed.

Major comments

Minor comments

1. The introduction section can be shortened by moving paragraphs between lines 83 and 110 to the methods section.

2. Table 3: put 1 or indicate as “reference” in the reference categories, rather than indicating with a symbol.

Reviewer #2: Reviewer’s Comments

Major comments:

1. Why it is called retrospective cross-sectional study?

2. Result session:

There were 32087 U5C in table 1, but slept under ITN (16,671) and not slept (5,769).

There were 54692 children of school age in table 1, but slept under ITN (21,690) and not slept (15,812).

Table 3 should be multivariable logistics regression and use AOR. Need to add reference group.

Please see the table for multivariable logistics regression from other international publication.

3. What is your implication of the study for Nigeria? What is the different information provided by your analysis compared to NDHS findings?

4. Conclusion should provide the specific input to program.

**********

6. PLOS authors have the option to publish the peer review history of their article (what does this mean?). If published, this will include your full peer review and any attached files.

If you choose “no”, your identity will remain anonymous but your review may still be made public.

Do you want your identity to be public for this peer review? For information about this choice, including consent withdrawal, please see our Privacy Policy.

Reviewer #1: No

Reviewer #2: Yes: Thae Maung Maung

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PLoS One. 2022 Sep 29;17(9):e0274160. doi: 10.1371/journal.pone.0274160.r002

Author response to Decision Letter 0

21 Apr 2022

21st April 2022

Manuscript Reference#: PONE-D-22-02975

Title: Use of insecticide treated nets in children under five and children of school age in Nigeria: evidence from a secondary data analysis of demographic health survey

RE: Revised manuscript submission and response to reviewer’s comments

Dear Editor,

This letter is in reference to your email dated April 5, 2022, with reviewers’ comments. We are very much delighted that the manuscript is potentially acceptable for publication in PLOS ONE once the revisions are made.

We appreciate the reviewers’ insightful comments which significantly improved the manuscript. In the current form the manuscript would make valuable contribution to the literature on this increasingly important topic.

Please find for your kind consideration the following:

1) A section-by-section response to the comments and suggestions of the reviewers (below).

2) The revised manuscript, provided as a marked-up copy and a clean copy.

We hope the changes made on the manuscript meet with your favourable consideration. Please do not hesitate to get in touch if you require any further information.

Chinazo Ujuju

Corresponding Author

Response to Reports

Reviewer #1:

Major comments

Response: Thank you very much for this vital comment. The manuscript presents usage of ITN at sub national level both by geopolitical zone and state level. The use of ITN in hypo, meso and hyper endemic areas is also presented.

Response: Previous studies included sex as a key socio-demographic factor for health-related outcomes. Similarly, we selected sex of household head and child to examine the association with ITN use. As suggested, literatures that were considered in selecting the variables has been indicated.

The following literatures included sex as one of the determinants of ITN use in children:

• Olapeju B, Choiriyyah I, Lynch M, Acosta A, Blaufuss S, Filemyr E, et al. Age and gender trends in insecticide-treated net use in sub-Saharan Africa: a multi-country analysis. Malar J [Internet]. 2018 Dec [cited 2021 Nov 10];17(1):423. Available from: https://malariajournal.biomedcentral.com/articles/10.1186/s12936-018-2575-z

• Nkoka O, Chipeta MS, Chuang Y-C, Fergus D, Chuang K-Y. A comparative study of the prevalence of and factors associated with insecticide-treated nets usage among children under 5 years of age in households that already own nets in Malawi. Malaria Journal [Internet]. 2019 Dec [cited 2021 Oct 8];18(1):43. Available from: https://malariajournal.biomedcentral.com/articles/10.1186/s12936-019-2667-4

• Mensah EA, Anto F. Individual and Community Factors Associated with Household Insecticide-Treated Bednet Usage in the Sunyani West District of Ghana Two Years after Mass Distribution. Journal of Environmental and Public Health. 2020;2020: 1–7. doi:10.1155/2020/7054383

Response: Thank you very much for this vital comment. This has been addressed in the manuscript.

Response: The conclusion has been revised accordingly Thank you.

Minor comments

1. The introduction section can be shortened by moving paragraphs between lines 83 and 110 to the methods section.

Response: The introduction section has been shortened Thank you.

2. Table 3: put 1 or indicate as “reference” in the reference categories, rather than indicating with a symbol.(less...)

Response: Table has been revised accordingly Thank you.

SECOND REVIEWER

Major comments:

1. Why it is called retrospective cross-sectional study?

Response: The demographic health survey was conducted in 2018 while secondary analysis for the manuscript was done in 2021. We adjudged the study to be retrospective. However, we have removed “retrospective” for clarity.

2. Result session:

There were 32087 U5C in table 1, but slept under ITN (16,671) and not slept (5,769).

Response: Table 2 was based on children in household who own at least 1 ITN. This variable has been added to Table 1 and also shows number of children in household without ITN for clarity Thank you.

3. There were 54692 children of school age in table 1, but slept under ITN (21,690) and not slept (15,812).

Response: Table 2 was based on children in households who own at least 1 ITN. This variable has been added to Table 1 and also show number of children in household without ITN.

Table 3 should be multivariable logistics regression and use AOR. Need to add reference group.

Response: Reference group has been added.

Please see the table for multivariable logistics regression from other international publication.

Response: Table has been revised accordingly Thank you.

3. What is your implication of the study for Nigeria? What is the different information provided by your analysis compared to NDHS findings?

Response: Response has been included in the manuscript Thank you.

4. Conclusion should provide the specific input to program

Response: Conclusion revised.

Attachment

Submitted filename: Response to reviewers.docx

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PLoS One. doi: 10.1371/journal.pone.0274160.r003

Decision Letter 1

Clement Ameh Yaro

23 Jun 2022

PONE-D-22-02975R1Use of insecticide treated nets in children under five and children of school age in Nigeria: evidence from a secondary data analysis of demographic health surveyPLOS ONE

Dear Dr. Ujuju,

Please submit your revised manuscript by Aug 07 2022 11:59PM. If you will need more time than this to complete your revisions, please reply to this message or contact the journal office at plosone@plos.org. When you're ready to submit your revision, log on to https://www.editorialmanager.com/pone/ and select the 'Submissions Needing Revision' folder to locate your manuscript file.

Please include the following items when submitting your revised manuscript:

A rebuttal letter that responds to each point raised by the academic editor and reviewer(s). You should upload this letter as a separate file labeled 'Response to Reviewers'.
A marked-up copy of your manuscript that highlights changes made to the original version. You should upload this as a separate file labeled 'Revised Manuscript with Track Changes'.
An unmarked version of your revised paper without tracked changes. You should upload this as a separate file labeled 'Manuscript'.

We look forward to receiving your revised manuscript.

Kind regards,

Clement Ameh Yaro, Ph.D

Academic Editor

PLOS ONE

Additional Editor Comments:

Manuscript Number: PONE-D-22-02975R1

Title: Use of insecticide treated nets in children under five and children of school age in Nigeria: evidence from a secondary data analysis of demographic health survey.

EDITOR’S COMMENTS

Dear Authors,

The manuscript requires major revision. The authors should attend to all the comments raised by the reviewers. The comments are very important and shouldn’t be neglected if the manuscript should be considered for publication.

REVIEWER 1

The authors have addressed my comments and concerns well and adequately. No further comments from my end.

REVIEWER 2

Major comments:

I would like to suggest to reconsider the analysis because your study objective is to determine the use of ITN in under five and children of school age in Nigeria.

In the result, you mentioned that “more than 80% of CU5 and school children used ITN”. This finding magnify your result of use of ITN because you used the denominator of those who had at least 1 ITN. It cannot be reflect the whole study population of CU5 and school children in Nigeria.

Therefore, your study objective should focus the access and use of ITN. You cannot omit “access” indicator to improve the ITN utilization implementation in Nigeria.

Minor comments:

1. Why it is called retrospective cross-sectional study?

R1: Please remove “retrospective” for the clarity. Here is the sample that used in other manuscript.

“This study analysed the secondary data from MDHS 2015-16, which is a cross-sectional study.”

2. Result session:

There were 32087 U5C in table 1, but slept under ITN (16,671) and not slept (5,769).

Response: Table 2 was based on children in household who own at least 1 ITN. This variable has been added to Table 1 and also shows number of children in household without ITN for clarity Thank you.

R1: Thank you for clarification. Please add total N in the table 1 and 2. I would like to suggest to remove the “Never slept” column for clear vision and eyeballing of the table.

In the tables, Pvalue should be <0.001. No need to describe <0.0001.

3. There were 54692 children of school age in table 1, but slept under ITN (21,690) and not slept (15,812).

Response: Table 2 was based on children in households who own at least 1 ITN. This variable has been added to Table 1 and also show number of children in household without ITN.

R1: OK.

Table 3 should be multivariable logistics regression and use AOR. Need to add reference group.

Response: Reference group has been added.

Please see the table for multivariable logistics regression from other international publication.

Response: Table has been revised accordingly Thank you.

R1: Please mention multivariable logistic regression if you have adjusted the other factors. Please describe AOR instead of OR in the table 3 and in text.* is not the reference value. It is the reference group.

Generally, we describe “1” or Ref in the table. See in the below figure.

3. What is your implication of the study for Nigeria? What is the different information provided by your analysis compared to NDHS findings?

Response: Response has been included in the manuscript Thank you.

4. Conclusion should provide the specific input to program

Response: Conclusion revised.

R1: Please mention study strength and limitation instead of study limitation on line 301. It is mixed for strength and limitation.

REVIEWER 3

The study examined the use of insecticide treated nets and its correlates among children under five and children of school age in Nigeria using the 2018 Nigeria demographic and health survey data. They attempt to address very important public health challenge, especially in sub-Sahara African countries like Nigeria. Their decision to consider both the under 5s and those aged 5-14 is commendable. Studies of this nature are relevant to policymakers and other stakeholder for informed decision making. However, I have a reservation about ignoring the hierarchical structure of the DHS data used in their binary logistic regression models presented.

The background to the study looks good.

Like any other DHS data, the Nigeria DHS data is hierarchical in nature where we have children nested within households, and household nested within clusters (i.e., communities) but the authors did not explain how they account for the hierarchical structure of the data used in this study. Assuming this was not explored during their modelling stage using multilevel (i.e., mixed effect) regression analysis, it could lead to spurious statistical significance with its associated misleading interpretations. Fortunately, we have statistical software packages that allow easy implementation of the multilevel binary logistic regression analysis. Authors are encouraged to explore this and compare the results with the single level binary regression to improve the quality of their results in the manuscript.

Also, since the authors considered both children <5s and 5-14 years and analysed the data separately for these two (2) groups, it could be very useful to pool (i.e., combined) this data and conduct another analysis to assess the correlates for the pooled dataset. This result can then to compared to the separate analysis to inform policy decisions.

https://journals.plos.org/plosone/article?id=10.1371/journal.pone.0257944 for guidance. Also, is not acceptable to present a p-value=0.000 in a manuscript. This should be written as <0.01 or <0.001 in case of p-value=0.0000.

In the conclusion, I struggled to link the analysis done by the authors to their conclusion that “Based on the findings of this study, increased availability and access to ITN has resulted in an increased use by children in rural areas and poor households”. Clearly, this cannot be supported with the data available. I have noted that the previous Reviewer #1 raised same query in item 4 which the authors stated that they have revised in the conclusion and yet they actually did not revise this. I encourage the authors to pay attention to all queries raised and address them to the best of their ability.

The manuscript will also benefit from some few proofreading to improve the message.

REVIEWER 4

Comments:

1. How collinearity test was conducted?

2. How were the variables selected in the final model?

3. Did the authors adjust for the survey design and cluster effect? Please explain.

4. In table 3, both crude and adjusted odds ratio should be shown.

[Note: HTML markup is below. Please do not edit.]

Reviewers' comments:

Reviewer's Responses to Questions

Comments to the Author

1. If the authors have adequately addressed your comments raised in a previous round of review and you feel that this manuscript is now acceptable for publication, you may indicate that here to bypass the “Comments to the Author” section, enter your conflict of interest statement in the “Confidential to Editor” section, and submit your "Accept" recommendation.

Reviewer #1: All comments have been addressed

Reviewer #2: (No Response)

Reviewer #3: (No Response)

Reviewer #4: (No Response)

**********

2. Is the manuscript technically sound, and do the data support the conclusions?

Reviewer #1: Yes

Reviewer #2: Partly

Reviewer #3: Partly

Reviewer #4: No

**********

3. Has the statistical analysis been performed appropriately and rigorously?

Reviewer #1: Yes

Reviewer #2: No

Reviewer #3: Yes

Reviewer #4: No

**********

4. Have the authors made all data underlying the findings in their manuscript fully available?

Reviewer #1: Yes

Reviewer #2: Yes

Reviewer #3: Yes

Reviewer #4: No

**********

5. Is the manuscript presented in an intelligible fashion and written in standard English?

Reviewer #1: Yes

Reviewer #2: Yes

Reviewer #3: Yes

Reviewer #4: Yes

**********

6. Review Comments to the Author

Reviewer #1: The authors have addressed my comments and concerns well and adequately. No further comments from my end.

Reviewer #2: Major comments:

I would like to suggest to reconsider the analysis because your study objective is to determine the use of ITN in under five and children of school age in Nigeria.

Therefore, your study objective should focus the access and use of ITN. You cannot omit “access” indicator to improve the ITN utilization implementation in Nigeria.

Minor comments:

1. Why it is called retrospective cross-sectional study?

R1: Please remove “retrospective” for the clarity. Here is the sample that used in other manuscript.

“This study analysed the secondary data from MDHS 2015-16, which is a cross-sectional study.”

2. Result session:

There were 32087 U5C in table 1, but slept under ITN (16,671) and not slept (5,769).

Response: Table 2 was based on children in household who own at least 1 ITN. This variable has been added to Table 1 and also shows number of children in household without ITN for clarity Thank you.

R1: Thank you for clarification. Please add total N in the table 1 and 2. I would like to suggest to remove the “Never slept” column for clear vision and eyeballing of the table.

In the tables, Pvalue should be <0.001. No need to describe <0.0001.

3. There were 54692 children of school age in table 1, but slept under ITN (21,690) and not slept (15,812).

Response: Table 2 was based on children in households who own at least 1 ITN. This variable has been added to Table 1 and also show number of children in household without ITN.

R1: OK.

Table 3 should be multivariable logistics regression and use AOR. Need to add reference group.

Response: Reference group has been added.

Please see the table for multivariable logistics regression from other international publication.

Response: Table has been revised accordingly Thank you.

Generally, we describe “1” or Ref in the table. See in the below figure.

3. What is your implication of the study for Nigeria? What is the different information provided by your analysis compared to NDHS findings?

Response: Response has been included in the manuscript Thank you.

4. Conclusion should provide the specific input to program

Response: Conclusion revised.

R1: Please mention study strength and limitation instead of study limitation on line 301. It is mixed for strength and limitation.

Reviewer #3: The study examined the use of insecticide treated nets and its correlates among children under five and children of school age in Nigeria using the 2018 Nigeria demographic and health survey data. They attempt to address very important public health challenge, especially in sub-Sahara African countries like Nigeria. Their decision to consider both the under 5s and those aged 5-14 is commendable. Studies of this nature are relevant to policymakers and other stakeholder for informed decision making. However, I have a reservation about ignoring the hierarchical structure of the DHS data used in their binary logistic regression models presented.

The background to the study looks good.

Furthermore, the reference categories, the adjusted odds ratios, and p-values in Table 3 are not properly presented, and they should present them in a standard format. The authors should see Table 4 in the publication https://journals.plos.org/plosone/article?id=10.1371/journal.pone.0257944 for guidance. Also, is not acceptable to present a p-value=0.000 in a manuscript. This should be written as <0.01 or <0.001 in case of p-value=0.0000.

The manuscript will also benefit from some few proofreading to improve the message.

Reviewer #4: Comments:

1. How collinearity test was conducted?

2. How were the variables selected in the final model?

3. Did the authors adjust for the survey design and cluster effect? Please explain.

4. In table 3, both crude and adjusted odds ratio should be shown.

**********

7. PLOS authors have the option to publish the peer review history of their article (what does this mean?). If published, this will include your full peer review and any attached files.

If you choose “no”, your identity will remain anonymous but your review may still be made public.

Do you want your identity to be public for this peer review? For information about this choice, including consent withdrawal, please see our Privacy Policy.

Reviewer #1: No

Reviewer #2: Yes: Thae Maung Maung

Reviewer #3: Yes: Justice Moses Aheto

Reviewer #4: No

**********

PLoS One. 2022 Sep 29;17(9):e0274160. doi: 10.1371/journal.pone.0274160.r004

Author response to Decision Letter 1

6 Aug 2022

REVIEWER 1

The authors have addressed my comments and concerns well and adequately. No further comments from my end.

Noted

REVIEWER 2

Major comments:

I would like to suggest to reconsider the analysis because your study objective is to determine the use of ITN in under five and children of school age in Nigeria.

We presume our analysis was done to answer the objectives of the study. The stratification of the analysis into two categories children under five and children of school age meets the objective of our study. Separating the two categories was intentional to ensure that future public health decisions can consider children under five and school aged children independently.

Thank you for pointing out this very crucial mis-representation of the findings. The population of children under five and children of school age who slept under ITN was 74.3% and 57.8%% respectively and has been included in the result. The result also presented ITN use at state level and described states that had more than 80% ITN coverage in children under five and children above five ( as you presented above). NMEP set vector control utilization target of 80% and this has only been measured at national level using DHS. This manuscript presented state level analysis and the result described states that reached the 80% utilization target for ITN.

You need to discuss about the % of access to ITN, and use of ITN in total population, and use of ITN in those who had access. Need to compare 2 use of ITN and consider the implications of the study finding. Therefore, your study objective should focus the access and use of ITN. You cannot omit “access” indicator to improve the ITN utilization implementation in Nigeria.

Our study population is children under five and children of school age. Access to ITN is a standard malaria indicator determined at household level. Access to ITN is calculated by dividing the sum of all potential ITN users in the sample by the total number of individuals who spent the previous night in surveyed households. This is based on the assumption that 2 people can sleep under one ITN. As such a household with 4 residents will require 2 ITN. (Reference Kilian, A, H. Koenker, and L. Paintain. 2013. "Estimating population access to insecticide-treated nets from administrative data: correction factor is needed." Malaria journal 12(1): 259. https://malariajournal.biomedcentral.com/articles/10.1186/1475-2875-12-259). As the study did not focus on the entire household but on children under five and children of school age, determining household access to ITN is beyond the scope of this study.

To determine use of ITN in the study population which is children under five and children of school age, it is not appropriate to ask whether a child living in a household that do not have an ITN slept under ITN. As such the study explored use of ITN in households that have at least one ITN. That shows the child had access to ITN. This is a standard way of determining use of ITN in DHS analysis, presented in the DHS report and is available in several literatures. In addition, our literature already documents efforts by the Nigerian government to increase access. We choose to streamline the focus of this analysis such that our public health message will be clear which is the use of ITN. We have revised the manuscript further to have the introduction and discussion focus on utilization of ITN.

Minor comments:

1. Why it is called retrospective cross-sectional study?

R1: Please remove “retrospective” for the clarity. Here is the sample that used in other manuscript.

“This study analysed the secondary data from MDHS 2015-16, which is a cross-sectional study.”

Retrospective has been removed

2. Result session:

There were 32087 U5C in table 1, but slept under ITN (16,671) and not slept (5,769).

R1: Thank you for clarification. Please add total N in the table 1 and 2. I would like to suggest to remove the “Never slept” column for clear vision and eyeballing of the table.

In the tables, P value should be <0.001. No need to describe <0.0001.

Total for table 1 and 2 added, P value revised and column on never slept removed from table 2

3. There were 54692 children of school age in table 1, but slept under ITN (21,690) and not slept (15,812).

R1: OK.

Table 3 should be multivariable logistics regression and use AOR. Need to add reference group.

Reference group has been added.

Please see the table for multivariable logistics regression from other international publication.

Table has been revised accordingly

Generally, we describe “1” or Ref in the table. See in the below figure.

3. What is your implication of the study for Nigeria? What is the different information provided by your analysis compared to NDHS findings?

Response has been included under strength of the manuscript

4. Conclusion should provide the specific input to program

Conclusion has specific input to program and recommendation

R1: Please mention study strength and limitation instead of study limitation on line 301. It is mixed for strength and limitation.

Study strength and limitation mentioned as recommended

REVIEWER 3

The background to the study looks good.

We have considered the suggestion and multilevel logistic regression analysis has been implemented. It’s Important to mention that the study has benefited from implementing a multilevel regression with the models defined at different levels of analysis.

We didn’t think the pooled analysis will be of much public health importance. Children under 5 are critical age in public health and school age children are also significant especially with the increased prevalence of malaria reported in the age group. We are not aware of any significance analysis of data for under 14 children can add to public health. The suggestion is noted and will be considered in future studies.

This suggestion is noted and table 3 has been revised

Apologies for the oversight. The conclusion has been revised

The manuscript will also benefit from some few proofreading to improve the message.

Manuscript has been reviewed and errors addressed

REVIEWER 4

Comments:

1. How collinearity test was conducted?

Variance inflation factor has been calculated to determine the extent of collinearity. Multilevel logistic regression analysis was used which ensures that correlation effect was reduced in the estimation of model parameters.

2. How were the variables selected in the final model?

The variables were selected based on association in the bivariate analysis while the model applied the forward method in variable inclusion in the multilevel logistic regression.

3. Did the authors adjust for the survey design and cluster effect? Please explain.

The design of the DHS survey has adjusted for this effect. Also, the use of multilevel regression analysis ensures that the nested structure of DHS data is taking into effect in the analysis and the variability of the data at different levels of sample selections was measure and presented in table 3.

4. In table 3, both crude and adjusted odds ratio should be shown.

Given that we have now conducted multilevel logic regression, stratified the model and considered different levels of sampling in the analysis we presume that presenting crude odds ratio may not be necessary in the analysis.

Attachment

Submitted filename: Draft Rebuttal Letter.docx

Click here for additional data file.^{(20.4KB, docx)}

PLoS One. doi: 10.1371/journal.pone.0274160.r005

Decision Letter 2

Clement Ameh Yaro

24 Aug 2022

Use of insecticide treated nets in children under five and children of school age in Nigeria: evidence from a secondary data analysis of demographic health survey

PONE-D-22-02975R2

Dear Dr. Ujuju,

We’re pleased to inform you that your manuscript has been judged scientifically suitable for publication and will be formally accepted for publication once it meets all outstanding technical requirements.

Within one week, you’ll receive an e-mail detailing the required amendments. When these have been addressed, you’ll receive a formal acceptance letter and your manuscript will be scheduled for publication.

An invoice for payment will follow shortly after the formal acceptance. To ensure an efficient process, please log into Editorial Manager at http://www.editorialmanager.com/pone/, click the 'Update My Information' link at the top of the page, and double check that your user information is up-to-date. If you have any billing related questions, please contact our Author Billing department directly at authorbilling@plos.org.

If your institution or institutions have a press office, please notify them about your upcoming paper to help maximize its impact. If they’ll be preparing press materials, please inform our press team as soon as possible -- no later than 48 hours after receiving the formal acceptance. Your manuscript will remain under strict press embargo until 2 pm Eastern Time on the date of publication. For more information, please contact onepress@plos.org.

Kind regards,

Clement Ameh Yaro, Ph.D

Academic Editor

PLOS ONE

Additional Editor Comments (optional):

The manuscript requires minor revision before consideration for publication, the authors should kindly respond to all the comments raised by the reviewers.

REVIEWER 1

The authors did a great job and provided satisfactory revisions to the paper which significantly improved the message in the manuscript.

However, the authors did not discuss the policy relevance of the significant unobserved household and community level residual (random) effects observed in their results. This should be presented briefly at the discussion section. The authors can benefit from the discussion of the publications below to address this issue and support their findings with same papers. I am happy to provide a quick review within a day after the authors address this.

References

1) https://journals.plos.org/plosone/article/authors?id=10.1371/journal.pone.0269066

2) https://onlinelibrary.wiley.com/doi/full/10.1002/hsr2.453

REVIEWER 2

Thanks for addressing my comments. I would still suggest to show the crude odds ratio. If the authors do not want to show them in the main manuscript, please add a supplementary table showing the crude odds ratio. This will help the readers and future audiences specailly who want to conduct a systematic review and meta-analyses.

REVIEWER 3

Welldone on your research. Please see below my comment to improve the quality of your paper.

1) The independent variables are not robust enough. If your aim is to include variables that would likely influence the utilization of ITNs then some very important variables captured in 2018 NDHS should not be left out bearing in mind that promoting ITN use in Nigeria was a mass national campaign which was publicized through various media channels. It would have been expected that more individual, socio-demographic and media variables (found in the NDHS) such as child birth order, preceding birth interval, mother's educational level, father's educational level, listening to radio, watching TV, reading newspaper are included in the analysis.

The chosen independent variables does not offer a good understanding of the factors contributing to ITN in Nigeria. For instance, child sex - does it really matter if the child is male or female to use the provided ITN? Head of household's sex - most households (99%) in Nigeria are headed by men, when ITN was obtained - how does this contribute to their usage?

In addition, wealth index as used in NDHS serves as an indicator of wealth that is consistent with expenditure and income measures. It was represented as a score of household assets via the principle components analysis method (PCA), this means that household ownership of radio and television is included in the wealth index estimation and so should not be a standalone variable. Therefore, given the set of variables used, it does not come as a surprise that rural residence and wealth index were significantly associated with ITN use.

2) Why include school age children (5 - 14)? The ITN campaign was targeted at children under-5 in Nigeria to whom free ITN was given. School age children (5 - 14) were not given free ITN, they would have to purchase the ITN and this can influence their usage especially if the household is poor. Having both groups of children in the same analysis given the difference in access to the ITN would bias the study findings. I recommend using only children under-5 years for this study if reference is to be made to the ITN campaign in Nigeria OR clearly include this disparity in access to ITN between children under-5 and school age children as a study limitation which readers should approach with caution.

3) It is worth stating that the ITN mass campaign in Nigeria might have influenced the result of this study given that this has been largely focused on poor households in rural areas.

4) The cross-sectional nature of the study design is a limitation on the study's ability to establish a causal relationship between the observed predisposing factors and ITN use in Nigeria.

5) As part of the study limitation, it is worth mentioning the effect of residual confounding as a result of unmeasured co-variates such as climatic factors, season of the year, topography, biomes, etc. These factors influence the prevalence of malaria in a region and thus the use of ITNs.

6) Your study revealed an increase in use of ITN among under-5 children living in poor households, rural areas and meso/hyper endemic areas of Nigeria. Please provide a well-balanced discussion around the contribution of these THREE identified factors to ITN use in Nigeria not only on meso/hyper endemic areas.

Reviewers' comments:

Reviewer's Responses to Questions

Comments to the Author

Reviewer #3: (No Response)

Reviewer #4: (No Response)

Reviewer #5: (No Response)

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2. Is the manuscript technically sound, and do the data support the conclusions?

Reviewer #3: Yes

Reviewer #4: Yes

Reviewer #5: Yes

**********

3. Has the statistical analysis been performed appropriately and rigorously?

Reviewer #3: Yes

Reviewer #4: Yes

Reviewer #5: Yes

**********

4. Have the authors made all data underlying the findings in their manuscript fully available?

Reviewer #3: Yes

Reviewer #4: Yes

Reviewer #5: Yes

**********

5. Is the manuscript presented in an intelligible fashion and written in standard English?

Reviewer #3: Yes

Reviewer #4: Yes

Reviewer #5: Yes

**********

6. Review Comments to the Author

Reviewer #3: The authors did a great job and provided satisfactory revisions to the paper which significantly improved the message in the manuscript.

References

1)https://journals.plos.org/plosone/article/authors?id=10.1371/journal.pone.0269066

2)https://onlinelibrary.wiley.com/doi/full/10.1002/hsr2.453

Reviewer #4: Thanks for addressing my comments. I would still suggest to show the crude odds ratio. If the authors do not want to show them in the main manuscript, please add a supplementary table showing the crude odds ratio. This will help the readers and future audiences specailly who want to conduct a systematic review and meta-analyses.

Reviewer #5: Welldone on your research. Please see below my comment to improve the quality of your paper.

Therefore, given the set of variables used, it does not come as a surprise that rural residence and wealth index were significantly associated with ITN use.

3) It is worth stating that the ITN mass campaign in Nigeria might have influenced the result of this study given that this has been largely focused on poor households in rural areas.

4) The cross-sectional nature of the study design is a limitation on the study's ability to establish a causal relationship between the observed predisposing factors and ITN use in Nigeria.

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Reviewer #3: Yes: Justice Moses Aheto

Reviewer #4: No

Reviewer #5: No

**********

PLoS One. doi: 10.1371/journal.pone.0274160.r006

Acceptance letter

Clement Ameh Yaro

19 Sep 2022

PONE-D-22-02975R2

Use of insecticide treated nets in children under five and children of school age in Nigeria: evidence from a secondary data analysis of demographic health survey

Dear Dr. Ujuju:

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Associated Data

This section collects any data citations, data availability statements, or supplementary materials included in this article.

Supplementary Materials

S1 File. Supplementary file containing Table 3.

Factors associated with ITN use in CU5 and children of school age.

(DOCX)

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Data Availability Statement

The data underlying the results presented in this study are available on DHS program website https://www.dhsprogram.com/data/.

[pone.0274160.ref001] 1.WHO. World malaria report 2020: 20 years of global progress and challenges. Geneva: World Health Organization; 2020. Licence: CC BY-NC-SA 3.0 IGO. 2021 update. Geneva: World Health Organization; 2021. https://apps.who.int/iris/handle/10665/342995

[pone.0274160.ref002] 2.National Population Commission (NPC) [Nigeria] and ICF. 2019. Nigeria Demographic and Health Survey 2018. Abuja, Nigeria, and Rockville, Maryland, USA: NPC and ICF. https://dhsprogram.com/pubs/pdf/FR359/FR359.pdf

[pone.0274160.ref003] 3.Sarpong N, Owusu-Dabo E, Kreuels B, Fobil JN, Segbaya S, Amoyaw F, et al. Prevalence of malaria parasitaemia in school children from two districts of Ghana earmarked for indoor residual spraying: a cross-sectional study. Malar J. 2015;14: 260. doi: 10.1186/s12936-015-0772-6 [DOI] [PMC free article] [PubMed] [Google Scholar]

[pone.0274160.ref004] 4.Yapi RB, Hürlimann E, Houngbedji CA, Ndri PB, Silué KD, Soro G, et al. Infection and Co-infection with Helminths and Plasmodium among School Children in Côte d’Ivoire: Results from a National Cross-Sectional Survey. Stothard JR, editor. PLoS Negl Trop Dis. 2014;8: e2913. doi: 10.1371/journal.pntd.0002913 [DOI] [PMC free article] [PubMed] [Google Scholar]

[pone.0274160.ref005] 5.Walldorf JA, Cohee LM, Coalson JE, Bauleni A, Nkanaunena K, Kapito-Tembo A, et al. School-Age Children Are a Reservoir of Malaria Infection in Malawi. Snounou G, editor. PLoS ONE. 2015;10: e0134061. doi: 10.1371/journal.pone.0134061 [DOI] [PMC free article] [PubMed] [Google Scholar]

[pone.0274160.ref006] 6.Hart A, Abinye B. The prevalence of malaria parasitaemia in children 0–11 years in twon-brass. World Journal of Pharmaceutical and Medical Research 2020;6:25–29. 2020 [cited 26 Nov 2021]. https://journals.indexcopernicus.com/search/article?articleId=2473132 [Google Scholar]

[pone.0274160.ref007] 7.Noland GS, Graves PM, Sallau A, Eigege A, Emukah E, Patterson AE, et al. Malaria prevalence, anemia and baseline intervention coverage prior to mass net distributions in Abia and Plateau States, Nigeria. BMC Infect Dis. 2014;14: 168. doi: 10.1186/1471-2334-14-168 [DOI] [PMC free article] [PubMed] [Google Scholar]

[pone.0274160.ref008] 8.Cohee L, Laufer M. Tackling malaria transmission in sub-Saharan Africa. The Lancet Global Health. 2018;6: e598–e599. doi: 10.1016/S2214-109X(18)30197-9 [DOI] [PMC free article] [PubMed] [Google Scholar]

[pone.0274160.ref009] 9.Nzobo BJ. Prevalence of asymptomatic malaria infection and use of different malaria control measures among primary school children in Morogoro Municipality, Tanzania. 2015; 7. [DOI] [PMC free article] [PubMed]

[pone.0274160.ref010] 10.Ezenduka CC, Falleiros DR, Godman BB. Evaluating the Treatment Costs for Uncomplicated Malaria at a Public Healthcare Facility in Nigeria and the Implications. Pharmacoecon Open. 2017;1: 185–194. doi: 10.1007/s41669-017-0021-8 [DOI] [PMC free article] [PubMed] [Google Scholar]

[pone.0274160.ref011] 11.Conteh L, Shuford K, Agboraw E, Kont M, Kolaczinski J. Costs and Cost-Effectiveness of Malaria Control Interventions: A Systematic Literature Review | Elsevier Enhanced Reader. Value Health. 2021; 24(8):1213–1222. 2021 [cited 10 Dec 2021]. doi: 10.1016/j.jval.2021.01.013 [DOI] [PMC free article] [PubMed] [Google Scholar]

[pone.0274160.ref012] 12.Mueller DH, Wiseman V, Bakusa D, Morgah K, Daré A, Tchamdja P. Cost-effectiveness analysis of insecticide-treated net distribution as part of the Togo Integrated Child Health Campaign. Malar J. 2008;7: 73. doi: 10.1186/1475-2875-7-73 [DOI] [PMC free article] [PubMed] [Google Scholar]

[pone.0274160.ref013] 13.Bhatt S, Weiss DJ, Cameron E, Bisanzio D, Mappin B, Dalrymple U, et al. The effect of malaria control on Plasmodium falciparum in Africa between 2000 and 2015. NATURE. 2015;526: 207–211. doi: 10.1038/nature15535 [DOI] [PMC free article] [PubMed] [Google Scholar]

[pone.0274160.ref014] 14.Ankomah A, Adebayo SB, Arogundade ED, Anyanti J, Nwokolo E, Inyang U, et al. The Effect of Mass Media Campaign on the Use of Insecticide-Treated Bed Nets among Pregnant Women in Nigeria. Malaria Research and Treatment. 2014;2014: 1–7. doi: 10.1155/2014/694863 [DOI] [PMC free article] [PubMed] [Google Scholar]

[pone.0274160.ref015] 15.Ye Y, Patton E, Kilian A, Dovey S, Eckert E. Can universal insecticide-treated net campaigns achieve equity in coverage and use? the case of northern Nigeria. Malaria Journal. 2012;11: 32. doi: 10.1186/1475-2875-11-32 [DOI] [PMC free article] [PubMed] [Google Scholar]

[pone.0274160.ref016] 16.Kilian A, Lawford H, Ujuju CN, Abeku TA, Nwokolo E, Okoh F, et al. The impact of behaviour change communication on the use of insecticide treated nets: a secondary analysis of ten post-campaign surveys from Nigeria. Malar J. 2016;15: 422. doi: 10.1186/s12936-016-1463-7 [DOI] [PMC free article] [PubMed] [Google Scholar]

[pone.0274160.ref017] 17.Hill J, Dellicour S, Bruce J, Ouma P, Smedley J, Otieno P, et al. Effectiveness of Antenatal Clinics to Deliver Intermittent Preventive Treatment and Insecticide Treated Nets for the Control of Malaria in Pregnancy in Kenya. von Seidlein L, editor. PLoS ONE. 2013;8: e64913. doi: 10.1371/journal.pone.0064913 [DOI] [PMC free article] [PubMed] [Google Scholar]

[pone.0274160.ref018] 18.Theiss-Nyland K, Koné D, Karema C, Ejersa W, Webster J, Lines J. The relative roles of ANC and EPI in the continuous distribution of LLINs: a qualitative study in four countries. Health Policy and Planning. 2017;32: 467–475. doi: 10.1093/heapol/czw158 [DOI] [PubMed] [Google Scholar]

[pone.0274160.ref019] 19.Mathanga DP, Luman ET, Campbell CH, Silwimba C, Malenga G. Integration of insecticide-treated net distribution into routine immunization services in Malawi: a pilot study. Tropical Medicine & International Health. 2009;14: 792–801. doi: 10.1111/j.1365-3156.2009.02295.x [DOI] [PubMed] [Google Scholar]

[pone.0274160.ref020] 20.Olapeju B, Choiriyyah I, Lynch M, Acosta A, Blaufuss S, Filemyr E, et al. Age and gender trends in insecticide-treated net use in sub-Saharan Africa: a multi-country analysis. Malaria Journal. 2018;17: 423. doi: 10.1186/s12936-018-2575-z [DOI] [PMC free article] [PubMed] [Google Scholar]

[pone.0274160.ref021] 21.Ankomah A, Adebayo SB, Arogundade ED, Anyanti J, Nwokolo E, Ladipo O, et al. Determinants of insecticide-treated net ownership and utilization among pregnant women in Nigeria. BMC Public Health. 2012;12: 105. doi: 10.1186/1471-2458-12-105 [DOI] [PMC free article] [PubMed] [Google Scholar]

[pone.0274160.ref022] 22.Babalola OJ, Sambo MN, Idris SH, Ajayi I-OO, Ajumobi O, Nguku P. Factors associated with utilization of LLINs among women of child-bearing age in Igabi, Kaduna State, Nigeria. Malar J. 2019;18: 412. doi: 10.1186/s12936-019-3046-x [DOI] [PMC free article] [PubMed] [Google Scholar]

[pone.0274160.ref023] 23.Baume CA, Marin MC. Gains in awareness, ownership and use of insecticide-treated nets in Nigeria, Senegal, Uganda and Zambia. Malaria Journal. 2008;7: 153. doi: 10.1186/1475-2875-7-153 [DOI] [PMC free article] [PubMed] [Google Scholar]

[pone.0274160.ref024] 24.Ng’ang’a PN, Aduogo P, Mutero CM. Long lasting insecticidal mosquito nets (LLINs) ownership, use and coverage following mass distribution campaign in Lake Victoria basin, Western Kenya. BMC Public Health. 2021;21: 1046. doi: 10.1186/s12889-021-11062-7 [DOI] [PMC free article] [PubMed] [Google Scholar]

[pone.0274160.ref025] 25.Sina OJ. An Assessment of the use of Insecticide Treated Net among under Five in Ekiti State as a Means of Prevention of Malaria. PRM. 2018;2. doi: 10.31031/PRM.2018.02.000544 [DOI] [Google Scholar]

[pone.0274160.ref026] 26.Mensah EA, Anto F. Individual and Community Factors Associated with Household Insecticide-Treated Bednet Usage in the Sunyani West District of Ghana Two Years after Mass Distribution. Journal of Environmental and Public Health. 2020;2020: 1–7. doi: 10.1155/2020/7054383 [DOI] [PMC free article] [PubMed] [Google Scholar]

[pone.0274160.ref027] 27.Nkoka O, Chipeta MS, Chuang Y-C, Fergus D, Chuang K-Y. A comparative study of the prevalence of and factors associated with insecticide-treated nets usage among children under 5 years of age in households that already own nets in Malawi. Malaria Journal. 2019;18: 43. doi: 10.1186/s12936-019-2667-4 [DOI] [PMC free article] [PubMed] [Google Scholar]

[pone.0274160.ref028] 28.Smith DL, Guerra CA, Snow RW, Hay SI. Standardizing estimates of the Plasmodium falciparum parasite rate. Malar J. 2007;6: 131. doi: 10.1186/1475-2875-6-131 [DOI] [PMC free article] [PubMed] [Google Scholar]

[pone.0274160.ref029] 29.Bl S, Sa R. Multilevel analysis of factors associated with assistance during delivery in rural Nigeria: implications for reducing rural-urban inequity in skilled care at delivery. BMC pregnancy and childbirth. 2018;18. doi: 10.1186/s12884-018-2074-9 [DOI] [PMC free article] [PubMed] [Google Scholar]

[pone.0274160.ref030] 30.Women who have not utilized health Service for Delivery in Nigeria: who are they and where do they live? | BMC Pregnancy and Childbirth | Full Text. [cited 2 Aug 2022]. https://bmcpregnancychildbirth.biomedcentral.com/articles/10.1186/s12884-019-2242-6 [DOI] [PMC free article] [PubMed]

[pone.0274160.ref031] 31.Aheto JMK, Pannell O, Dotse-Gborgbortsi W, Trimner MK, Tatem AJ, Rhoda DA, et al. Multilevel analysis of predictors of multiple indicators of childhood vaccination in Nigeria. PLOS ONE. 2022;17: e0269066. doi: 10.1371/journal.pone.0269066 [DOI] [PMC free article] [PubMed] [Google Scholar]

[pone.0274160.ref032] 32.Aheto JMK, Dagne GA. Multilevel modeling, prevalence, and predictors of hypertension in Ghana: Evidence from Wave 2 of the World Health Organization’s Study on global AGEing and adult health. Health Science Reports. 2021;4: e453. doi: 10.1002/hsr2.453 [DOI] [PMC free article] [PubMed] [Google Scholar]

[pone.0274160.ref033] 33.Habyarimana F, Ramroop S. Prevalence and Risk Factors Associated with Malaria among Children Aged Six Months to 14 Years Old in Rwanda: Evidence from 2017 Rwanda Malaria Indicator Survey. Int J Environ Res Public Health. 2020; 13. doi: 10.3390/ijerph17217975 [DOI] [PMC free article] [PubMed] [Google Scholar]

[pone.0274160.ref034] 34.Gahutu J-B, Steininger C, Shyirambere C, Zeile I, Cwinya-Ay N, Danquah I, et al. Prevalence and risk factors of malaria among children in southern highland Rwanda. Malaria Journal. 2011;10: 134. doi: 10.1186/1475-2875-10-134 [DOI] [PMC free article] [PubMed] [Google Scholar]

[pone.0274160.ref035] 35.Yusuf OB, Adeoye BW, Oladepo OO, Peters DH, Bishai D. Poverty and fever vulnerability in Nigeria: a multilevel analysis. Malaria Journal. 2010;9: 235. doi: 10.1186/1475-2875-9-235 [DOI] [PMC free article] [PubMed] [Google Scholar]

[pone.0274160.ref036] 36.Oyibo W, Ntadom G, Uhomoibhi P, Oresanya O, Ogbulafor N, Ajumobi O, et al. Geographical and temporal variation in reduction of malaria infection among children under 5 years of age throughout Nigeria. BMJ Glob Health. 2021;6: e004250. doi: 10.1136/bmjgh-2020-004250 [DOI] [PMC free article] [PubMed] [Google Scholar]

PERMALINK

Use of insecticide treated nets in children under five and children of school age in Nigeria: Evidence from a secondary data analysis of demographic health survey

Chinazo N Ujuju

Chukwu Okoronkwo

Okefu Oyale Okoko

Adekunle Akerele

Chibundo N Okorie

Samson Babatunde Adebayo

Roles

Abstract

Background and objective

Methods

Results

Conclusions

Introduction

Methods

Target population

ITN campaign in Nigeria

Variables

Outcome variables

Independent variables

Statistical analysis

Ethical consideration

Results

Univariate

Table 1. Demographic characteristics of CU5 (0–4) years and children of school age (5–14 years).

Bivariate

Table 2. Use of ITN in CU5 and children of school age living in households with at least one ITN by demographic characteristics.

Multivariate results

Table 3. Multilevel logistic regression on socio and demographic factors associated with ITN use in CU5 and children of school age in households owning at least one ITN.

Discussions

Study strength

Study limitation

Conclusion

Supporting information

Acknowledgments

Data Availability

Funding Statement

References

Decision Letter 0

Clement Ameh Yaro

Roles

Author response to Decision Letter 0

Decision Letter 1

Clement Ameh Yaro

Roles

Author response to Decision Letter 1

Decision Letter 2

Clement Ameh Yaro

Roles

Acceptance letter

Clement Ameh Yaro

Roles

Associated Data

Supplementary Materials

Data Availability Statement

ACTIONS

PERMALINK

RESOURCES

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