Access versus utilization: determinants of insecticide-treated net non-use in Myanmar

Abstract

Background

Vector control has played a pivotal role in malaria control and elimination efforts, with insecticide-treated nets (ITNs) recognized as one of the most effective and widely accepted strategies. This study assessed ITN use and identified factors associated with non-use among individuals with access to ITNs in Myanmar.

Methods

Data were drawn from the nationally representative 2015–2016 Myanmar Demographic and Health Survey. Access to ITNs was defined as having at least one ITN per two household members, and ITN use as having slept under an ITN the night before the survey. Descriptive statistics and multivariable logistic regression models were conducted using the “svyset” command in STATA to account for the two-stage stratified cluster sampling design.

Results

Among 6,140 individuals with access to ITNs, approximately one-third (31.6%, 95% CI 28.3%–35.0%) reported not using them. Factors associated with higher odds of ITN non-use were age 15–34 years (adjusted odds ratio [aOR]: 1.31; 95% CI 1.07–1.61) and ≥ 50 years (aOR: 1.33; 95% CI 1.07–1.67), rural residence (aOR: 1.82; 95% CI 1.10–3.01), and belonging to the fourth wealth quintile, representing higher socioeconomic status (aOR: 1.74; 95% CI 1.06–2.85).

Conclusions

Despite having access to ITNs at that time, a substantial proportion of individuals in Myanmar did not use them. These historical findings highlight behavioural and contextual barriers that existed before recent health system disruptions due to political unrest and COVID-19. Although present-day challenges differ, understanding past determinants of ITN non-use remains valuable for designing behaviour change communication (BCC) strategies, especially in regions where ITN distribution remains feasible.

Background

Countries in the Greater Mekong Subregion have made significant progress toward malaria elimination by 2030 [1]. Notably, China achieved malaria-free certification in 2021 [2, 3]. However, Myanmar has experienced setbacks. Following the COVID-19 pandemic, political unrest led to a deterioration of the healthcare system and hampered malaria control efforts. As a result, the annual number of reported malaria cases rose sharply, from 79,000 in 2021 to 228,554 in 2023, a nearly threefold increase [2]. This resurgence has implications for neighbouring countries due to ongoing cross-border malaria transmission. For instance, Thailand reported 9,169 malaria cases in 2023, also a threefold increase compared to 2021, of which approximately 80% were classified as imported cases [2, 4]. These trends underscore the urgent need for strengthened surveillance systems and intensified vector control strategies to prevent further transmission within and beyond national borders.

Malaria transmission requires both an infective Anopheles mosquito and a susceptible human host. Preventing this interaction is critical to breaking the transmission cycle, from patient to mosquito, and from mosquito to uninfected individuals. In Myanmar, the primary vectors include Anopheles minimus and Anopheles dirus, both highly efficient in transmitting Plasmodium parasites [5–7]. The core vector control strategy in Myanmar is the distribution of insecticide-treated nets (ITNs), primarily in the form of long-lasting insecticidal nets (LLINs) pre-treated with pyrethroids. ITNs are distributed through mass campaigns every two years, typically at a coverage ratio of one net per two persons. In 2023, a total of 837,377 ITNs were distributed nationwide [2]. Top-up distributions also targeted high-risk groups such as young children, pregnant women, and migrant workers. Other vector control interventions, such as indoor residual spraying (IRS), were implemented only in specific circumstances, such as localized outbreaks. Despite widespread distribution, ITN utilisation has historically been suboptimal. Reported usage rates varied: 50–54% among migrant populations [8, 9], 15% in the general population [10], 44–45% among children under five [11, 12], and 18% among pregnant women [13]. Reasons for non-use included personal preferences for conventional untreated nets, logistical challenges such as difficulties setting up nets in forest environments, and misconceptions like fears of allergic reactions to insecticides [9, 14–16]. Additionally, socio-demographic factors such as age, sex, and socioeconomic status also influenced ITN use [15–18]. To maximise the impact of ITNs in reducing malaria transmission, it is essential to understand and address the factors associated with their non-use. Tailored behaviour change communication (BCC) strategies that encourage consistent and appropriate ITN use are needed.

National-level health surveys in Myanmar are infrequent. To date, only two surveys have included malaria-related indicators, both conducted in 2015–2016. However, the final report and dataset from the Malaria Indicator Survey remain unavailable to the public [19]. As a result, the 2015–2016 Demographic and Health Survey (DHS) is the only publicly accessible, nationally representative dataset with reliable malaria-related information [20]. Three previous studies have used DHS data to examine ITN ownership and use in Myanmar: one focused on the general population [10]; another on caregivers of children under five [21]; and a third on pregnant women [13]. However, all three studies analysed ITN use among all surveyed individuals, regardless of whether they had access to an ITN. Since ITN ownership does not always translate into usage, relying solely on population-level ownership data may underestimate true usage rates among those who have the means to use one. Given these gaps, this study used historical DHS data to assess actual ITN use and identify factors associated with non-use among individuals with adequate access. These findings offer insight into behavioural determinants before recent disruptions and may inform strategies for improving ITN use when stable distribution is feasible.

Methods

Study design

This study employed a secondary data analysis of the publicly available, nationally representative dataset from the 2015–2016 Myanmar Demographic and Health Survey (MDHS) [20]. The analysis used standardised definitions for outcome variables, with participants selected according to predefined inclusion criteria relevant to the study objectives. Because the dataset was collected in 2015–2016, the findings represent historical patterns of ITN use.

Myanmar demographic and health survey

The 2015–2016 MDHS remains the only DHS conducted in the country. Its primary objective was to provide nationally representative estimates of key demographic and health indicators, including those related to malaria, to support evidence-based health policy and planning.

The survey employed a two-stage stratified sampling design, drawing on the most recent national population census as the sampling frame. In the first stage, a total of 441 clusters (enumeration areas) were selected with probability proportional to size to ensure adequate representation across Myanmar’s seven states and eight regions and to capture both urban and rural areas. In the second stage, a fixed number of 30 households were systematically selected from each cluster, yielding a total sample of 13,260 households.

Eligible participants included both women and men aged 15–49 years who were either permanent residents of the selected households or visitors who had stayed in the household the night before the interview. Women were interviewed in all selected households, while men were interviewed in every second household. This approach aligns with standard DHS methodology, which focuses on adults of reproductive and working age to collect key demographic and health indicators. In addition to individual responses, eligible participants also provided information on household characteristics and other household members.

Data were collected using standardized DHS questionnaires that were adapted slightly to reflect Myanmar’s cultural and contextual factors. Three types of questionnaires were administered: a household questionnaire, and individual questionnaires for women and men. These instruments were translated into Burmese through a back-translation process to ensure accuracy and clarity. The questionnaires collected information on basic demographics, household characteristics, and health-related topics, including both communicable and non-communicable diseases. Specific to malaria, the survey collected data on ITN ownership and use, including the number of ITNs owned and whether household members slept under an ITN the night before the survey.

Field data collection was preceded by a training-of-trainers session, followed by a pre-test and refresher course. Fourteen trainers were trained and subsequently responsible for training 148 field personnel drawn from both governmental and non-governmental organisations. These personnel were selected for their local language proficiency and trained to serve as supervisors, field editors, interviewers, and reserve interviewers. Their training included in-class exercises, quizzes, and monitored field practice. Data collection took place between December 2015 and July 2016, using paper-based questionnaires. Field supervision was carried out by state and regional public health officials following a standardised supervisory protocol. Completed forms were double-entered into digital databases using a dual data entry system to allow for cross-verification and minimise data entry errors [20].

Sample and sampling

The present study utilised the Household Member Recode (PR) file from the 2015–2016 MDHS, which was obtained from the DHS Program website. This dataset included information reported by surveyed individuals on behalf of themselves and other members of the household, including children under five and guests who spent the night prior to the survey in the household. The target population for this analysis comprised individuals residing in households with adequate access to ITNs, defined as at least one net per two people. After applying this criterion, a total of 6,140 individuals from 2,700 households were retained for analysis, regardless of their sociodemographic characteristics.

Variables

To determine sufficient access to ITNs, the total number of ITNs in each household was divided by twice the number of household members, following World Health Organization (WHO) recommendations that one ITN is expected to protect up to two people [2]. Households with a resulting ratio of less than one were excluded from the analysis. ITN non-use was defined as a de facto household member (i.e., someone who slept in the household the night before the survey) reporting that they did not sleep under an ITN the previous night.

A total of seven independent variables were included in the analysis. Three of these, place of residence (urban or rural), wealth quintile, and sex of the household head, were extracted directly from the DHS dataset. Age was categorized into five groups: under 5 years, 5–14 years, 15–34 years, 35–49 years, and 50 years or older. Geographic regions were grouped into four categories based on their location: (1) delta and lowland areas, including Ayeyarwady, Yangon, and Bago Regions, as well as Mon and Kayin States; (2) hilly areas, comprising Kachin, Kayah, Chin, and Shan States; (3) coastal regions, including Rakhine State and Tanintharyi Region; and (4) plains regions, including Magway, Mandalay, and Sagaing Regions, along with Nay Pyi Taw Union Territory. Household size was categorized into three groups: 1–4 members, 5–8 members, and more than 8 members. Access to mass media was coded as"yes"if the household owned at least one communication device, such as a radio or television.

Data analysis

Descriptive statistics were used to summarize the background characteristics of the total study population, with frequencies and percentages presented accordingly. The proportions of ITN use and non-use were calculated based on responses from individuals who reported whether they had slept under an insecticide-treated net the night before the survey. To identify factors associated with ITN non-use among individuals with access, both univariable (simple) and multivariable logistic regression models were employed. Each independent variable was first analysed using univariable logistic regression. All variables were included in the multivariable model regardless of their statistical significance to ensure comprehensive adjustment for potential covariates. Results were presented as crude odds ratios (cOR) and adjusted odds ratios (aOR), along with their corresponding 95% confidence intervals (95% CI). Statistical significance was assessed based on whether the 95% CI excluded the null value of 1. All analyses were conducted using STATA version 17 (STATA Corp LLC, College Station, TX, USA). The complex two-stage stratified cluster sampling design was accounted for using the “svyset” command, and all statistical analyses were performed using the “svy:” prefix to apply appropriate survey weights and design-based variance estimation. Survey weights provided in the DHS dataset were applied to ensure nationally representative estimates.

Results

Background characteristics of surveyed individuals

Among the 6,140 individuals included in the study, the largest age group was 15–34 years, accounting for 26.4% of the sample. Most participants resided in the plain region (27.6%) and in rural areas (83.6%) at the time of the survey. A majority of households were headed by males (79.3%), and 68.7% of respondents reported having access to mass media, such as radio or television. Most individuals belonged to the lower three wealth quintiles (Table 1).

Table 1.

Background characteristics of surveyed individuals with access to ITNs (n = 6140)

Characteristic	Number	Percentage (%)
Age
Under 5 years	458	7.5
5–14 years	1261	20.5
15–34 years	1618	26.4
35–49 years	1206	19.6
50 + years	1595	26.0
Don’t know/missing	2	0.02
Region
Delta and Lowland	1449	23.6
Hills	1605	26.1
Coastal	1393	22.7
Plains	1693	27.6
Residence
Urban	1007	16.4
Rural	5134	83.6
Wealth quintile
First (poorest)	1368	22.3
Second	1448	23.6
Third	1240	20.2
Fourth	1168	19.0
Fifth (richest)	917	14.9
Number of household members
1–4	3221	52.5
5–8	2674	43.5
More than 8	245	4.0
Sex of household head
Male	4866	79.3
Female	1274	20.7
Access to mass media
Yes	4218	68.7
No	1922	31.3

Non-use of insecticide-treated nets

Among individuals with sufficient access to ITNs, approximately one-third (1,939 individuals; 31.6%; 95% CI 28.3%–35.0%) reported not sleeping under an ITN the night before the survey.

Within this group, higher proportions of non-use were observed among individuals aged 50 years and older (34.5%), those living in hilly regions (35.4%), rural residents (34.1%), individuals in the “richer” wealth quintile (36.5%), those from households with 1–4 members (33.7%), male-headed households (31.8%), and those without access to mass media (31.7%) (Table 2).

Table 2.

Factors associated with non-use of ITNs among individuals with access (n = 6140)

Characteristic	ITN non-use	cOR	95% CI	aOR	95% CI	Characteristic
	n	row %
Age
Under 5 years	126	27.6	Ref.		Ref.
5–14 years	392	31.1	1.18	0.95–1.47	1.20	0.97–1.48
15–34 years	535	33.1	1.30	1.06–1.59	1.31	1.07–1.61
35–49 years	334	27.7	1.01	0.80–1.27	1.02	0.82–1.27
50 + years	550	34.5	1.38	1.10–1.74	1.33	1.07–1.67
Don’t know/missing	2	100	–	–
Region
Delta and lowland	454	31.3	0.94	0.64–1.37	1.01	0.70–1.48
Hills	568	35.4	1.13	0.76–1.67	1.13	0.76–1.68
Coastal	456	32.7	Ref.		Ref.
Plains	461	27.2	0.77	0.46–1.28	0.80	0.47–1.35
Residence
Urban	189	18.8	Ref.		Ref.
Rural	1750	34.1	2.24	1.48–3.40	1.82	1.10–3.01
Wealth quintiles
First (poorest)	428	31.3	1.98	1.30–3.01	1.31	0.76–2.26
Second	481	33.3	2.16	1.43–3.27	1.45	0.87–2.39
Third	431	34.8	2.31	1.45–3.67	1.59	0.92–2.76
Fourth	426	36.5	2.49	1.57–3.94	1.74	1.06–2.85
Fifth (richest)	172	18.7	Ref.		Ref.
Number of household members
1–4	1087	33.7	Ref.		Ref.
5–8	803	30.0	0.84	0.67–1.06	0.87	0.68–1.09
More than 8	49	20.0	0.49	0.22–1.09	0.53	0.23–1.20
Sex of household head
Male	1550	31.8	Ref.		Ref.
Female	389	30.5	0.94	0.77–1.15	0.94	0.76–1.15
Access to mass media
Yes	1330	31.5	Ref.		Ref.
No	608	31.7	1.01	0.78–1.29	0.91	0.70–1.18

cOR: crude odds ratio; aOR: adjusted odds ratio; 95% CI 95% confidence interval; Ref: reference category

Factors associated with non-use of ITNs

In the multivariable logistic regression analysis, three factors were significantly associated with higher odds of ITN non-use among individuals with access. Compared to children under five, individuals aged 15–34 years (aOR: 1.31; 95% CI 1.07–1.61) and those aged 50 years and above (aOR: 1.33; 95% CI 1.07–1.67) were more likely to report not using ITNs. Individuals residing in rural areas had significantly higher odds of ITN non-use compared to their urban counterparts (aOR: 1.82; 95% CI 1.10–3.01). Additionally, respondents in the fourth wealth quintile (classified as “richer”) showed increased odds of non-use (aOR: 1.74; 95% CI 1.06–2.85) compared to those in other wealth groups. Other variables, including geographic region, household size, sex of the household head, and access to mass media, were not significantly associated with ITN non-use in the adjusted model (Table 2).

Discussion

In this study, more than one-third of individuals with sufficient access to ITNs did not use them the night before the survey. This proportion was slightly higher than estimates reported among migrant and general populations in Thailand and Myanmar during a similar period [10, 16, 22], but lower than estimates from some specific regions within Myanmar [18, 23, 24]. These findings indicate that ownership of ITNs did not necessarily translate into consistent use [14, 16, 25]. Ensuring nightly use of ITNs is essential for maximal protection against malaria, particularly in malaria-receptive areas with high vectorial capacity, where even a single infective mosquito bite can initiate transmission. These findings reflect historical patterns of ITN non-use (2015–2016), prior to the substantial disruptions caused by COVID-19 and political instability, which have since affected both ITN distribution and healthcare access in Myanmar. Although the dataset is nearly a decade old, understanding behavioural determinants of non-use during a relatively stable period remains important. Such insights may be particularly relevant for areas that are not heavily affected by conflict and where ITN distribution and utilisation programmes remain feasible. In these settings, addressing behavioural barriers to ITN use remains a critical component of malaria control efforts.

The influence of age on ITN use has been consistently reported in multiple studies, including those conducted in low-income settings [10, 16, 26]. Furthermore, individuals working in forested environments may have lacked the opportunity to use nets, despite their willingness to do so [9, 14, 16]. Cultural norms may also have played a role; in many Asian contexts, older adults deferred ITN use to younger family members, believing themselves to be less vulnerable or less deserving of preventive resources. Although WHO recommended a distribution ratio of one ITN per two people, this guideline was not always practical [27]. In households where members slept separately, the standard ratio could leave some individuals without a net. For example, a household with three members might be allocated two nets in accordance with the guidelines; however, if each member slept in a separate room or location, an additional ITN would have been needed. Therefore, distribution strategies should be adapted to reflect the sleeping arrangements and specific needs of households.

Malaria transmission has historically been more prevalent in rural regions, where competent vectors such as An. dirus and An. minimus thrive due to favourable environmental conditions [28]. Consequently, individuals living in rural areas were more likely to be familiar with malaria and the associated preventive interventions. Nevertheless, ITN use in these areas remained suboptimal, as previously reported [9, 17, 18]. This may have been due to occupation-related factors; for instance, many rural residents engaged in forest-related work, where setting up or using a net was impractical [9, 16, 22]. In contrast, urban residents often had greater access to healthcare and mass media, which has been shown to positively influence ITN use [29, 30]. In addition to ITNs, context-specific alternatives such as forest-adapted hammock nets should be considered, alongside the promotion of complementary personal protective measures, such as the use of mosquito repellents [31, 32]. Some individuals also preferred alternative mosquito control methods, such as burning rubbish or using mosquito coils, rather than sleeping under ITNs [33, 34].

Although ITN distribution was determined by malaria risk stratification rather than individuals’ economic status, personal behaviour and attitudes toward ITN use may still have been influenced by wealth. Wealthier individuals often lived in houses with structural protections such as screened windows and might have perceived ITNs as unnecessary. Moreover, wealthier households may have felt less concerned about the financial implications of illness, including malaria, due to their ability to afford treatment [10, 17]. These individuals also sometimes preferred to use more aesthetically pleasing or expensive nets based on personal preference. Seasonal discomfort, such as heat during the hot season, could further have reduced ITN use [16]. Therefore, targeted BCC campaigns that emphasized the continued importance of ITNs, even for those who perceived themselves as less vulnerable, were essential.

This study had several strengths and limitations. A key strength lay in the use of a nationally representative dataset that included thousands of individuals, providing valuable insights into ITN use across diverse population groups in Myanmar. Given the scarcity of reliable national-level data in the country, the 2015–2016 MDHS remained the most comprehensive source available. However, the data were collected several years ago, which limited the generalisability of the findings to the current context. That said, given the ongoing political instability and its impact on the health system, substantial changes in ITN use patterns were not anticipated in the immediate term. Although the MDHS was intended to achieve national representativeness, certain conflict-affected or geographically inaccessible regions (e.g., parts of Rakhine and Kayin States) may have been underrepresented. Given the likelihood of higher malaria transmission in these areas, it is possible that findings missed behaviours in some areas with higher malaria burdens. One limitation was the potential for measurement bias, as the DHS utilised standardized questionnaires that may not have fully captured the nuanced realities of ITN use, potentially resulting in either over- or underestimation. Additionally, as this study relied on secondary data, certain potentially important variables could not be included in the analysis, such as the time since ITNs were received, washing or maintenance practices, and participants’ previous history of malaria. Future research, particularly studies using mixed-methods or qualitative approaches, was recommended to explore the contextual barriers to ITN use and to provide a deeper understanding of behavioural and environmental factors influencing non-use.

Conclusions

Despite having adequate access to insecticide-treated nets during the survey period (2015–2016), a substantial proportion of individuals in Myanmar did not consistently use them. This analysis of historical data highlights population groups, such as working-age adults, older individuals, rural residents, and those in higher wealth quintiles, who were more likely to report non-use. While present-day ITN access has been affected by political instability and health system disruptions, understanding past behavioural and contextual barriers remains valuable for informing future malaria control programmes when stable ITN distribution becomes feasible. In particular, tailored BCC approaches could help address these barriers in regions where ITN distribution and use remain possible.

Abbreviations

aOR

Adjusted odds ratio

BCC

Behaviour change communication

CI

Confidence interval

cOR

Crude odds ratio

DHS

Demographic and Health Survey

ITN

Insecticide-treated net

IRS

Indoor residual spraying

LLIN

Long-lasting insecticidal net

WHO

World Health Organization

Author contributions

KMW, KLS, and PLA were involved in the conception and design of the study. KLS analysed the data. KMW and PLA drafted the manuscript. All authors provided critical reviews of the manuscript and approved the final version.

Funding

Open access funding provided by Mahidol University. JS, LC, WN, and PLA are supported by the National Institute of Allergy and Infectious Diseases (NIAID), National Institutes of Health (NIH), USA (U19AI181583 and TW011509).

Data availability

The dataset used in this study is publicly available from the DHS Program website (https://dhsprogram.com). Access to the data was granted upon request for academic and research purposes. The authors did not have any special access privileges that others would not have.

Declarations

Ethics approval and consent to participate

This study is a secondary analysis of publicly available, de-identified data from the 2015–2016 MDHS. The original survey protocols, including participant recruitment, informed consent, and ethical approval, were reviewed and approved by the Ethics Review Committee of the Department of Medical Research, Ministry of Health, Myanmar, and the Institutional Review Board of ICF. No additional ethical approval was required for this secondary data analysis.

Consent for publication

Not applicable.

Competing interests

The authors declare no competing interests.