Factors associated with the use of mosquito bed nets: Results from two cross-sectional household surveys in Zambézia Province, Mozambique

Malaria Journal, Volume 15, No. 1, Year 2016

Interpretation

Background: Malaria remains a major threat to some 3.2 billion persons globally. Malaria contributes heavily to the overall disease burden in Mozambique and is considered endemic. A cornerstone of Mozambique’s vector control strategy has been to strive for universal coverage of insecticide-treated nets (ITN). Methods: The study is a population-based cross-sectional survey of female heads-of-household in Zambézia Province, Mozambique conducted during August-September, 2010 and April-May, 2014. Analyses accounted for a stratified two-stage cluster sample design. Outcomes of interest included sleeping under a mosquito net during the previous night. Descriptive statistics were calculated for three oversampled districts and for the entire province. Multivariable logistic regression analysis was used to estimate factors associated with both changes over time and increased mosquito bed net usage. Results: Of the 3916 households interviewed in 2010 and 3906 households in 2014, 64.3 % were in possession of at least one mosquito bed net. A higher proportion of households in Namacurra (90 %) reported possession of a mosquito net, compared to Alto Molócuè (77 %) and Morrumbala (34 %), respectively in 2014. Of pregnant respondents, 58.6 % reported sleeping under a mosquito net the previous night in 2010 compared to 68.4 % in 2014. Fifty percent of children 0-59 months slept under a mosquito net the previous night in 2010 compared to 60 % in 2014. Factors associated with use of a mosquito net for female head-of-household respondents were higher education, understanding Portuguese, larger household size, having electricity in the household, and larger household monthly income. As travel time to a health facility increased (per 1 h), respondents had 13 % lower odds of sleeping under a mosquito net (OR 0.87; 95 % CI 0.74-1.01, p = 0.07). Pregnant women in 2014 had a 2.4 times higher odds of sleeping under a bed net if they lived in Namacurra compared to Alto Molócuè (95 % CI 0.91-6.32, p = 0.002 for district). Higher maternal education, living in Namacurra, and acquisition of mosquito bed nets were associated with a child 0-59 months reporting sleeping under the net in the previous night in 2014. Conclusions: Intensified focus on the poorest, least educated, and most distant from health services is needed to improve equity of ITN availability and usage. Additionally, while some districts have already surpassed goals in terms of coverage and utilization of ITN, renewed emphasis should be placed on bringing all geographic regions of the province closer to meeting these targets. The design and implementation of this study are detailed elsewhere [19–22]. Briefly, survey teams completed interviews in 262 enumeration areas (EA) across Zambézia Province. A large representative sample (201 EA) was obtained from three diverse districts (Alto Molócuè, Namacurra, and Morrumbala) in order to increase the precision of survey results while minimizing costs. To further maintain a degree of generalizability across the province, a sample of households were selected for interview throughout the remaining 14 districts (Fig. 1). Map of Zambézia Province with enumeration areas surveyed. *Oversampled districts highlighted, Alto Molócuè, Morrumbala, and Namacurra Map credit: Charlotte Buehler; May 27 2015; Vanderbilt Institute for Global Health; Projection: WGS 1984 Web Mercator Auxiliary Sphere At both baseline and endline the same questionnaire was utilized. While survey responses were not collected from the same households in both surveys, the same sampling methodology was utilized with interviewers returning to the same EAs as in baseline. The Ogumaniha survey tool collects information on over 500 variables in eight dimensions and was developed by a multi-disciplinary team of researchers from Vanderbilt University and the University Eduardo Mondlane in Maputo. The survey designers borrowed many questions and scales deemed appropriate from previous national surveys in Mozambique and other international surveys such as the Demographic Health Survey (DHS) and Multiple Indicator Cluster Survey (MICS). Survey questions covered household demographics; economic status; health knowledge, attitudes and practices; access to health and malaria-related services and products; access to improved water and sanitation; nutritional intake; and others. In both surveys, fourteen mobile teams consisting of a team leader and four interviewers administered the survey face-to-face using mobile phones with an electronic questionnaire installed for data collection. Local authorities were notified prior to the arrival of the survey team. From topographic maps, the survey teams divided the EAs into four quadrants. Starting in the center of the assigned quadrant, interviewers selected a direction, then chose the first household in that direction (i.e. starting point), and approached the nearest four households for interview. Female interviewers conducted the survey with female heads-of-household, defined as the only or principal wife of the immediate family of the household. The female head-of-household was chosen as it was felt she was the most likely person to be familiar with the health and care taking of the entire family. In polygamous families, the eldest wife was selected. Interviewers were trained to conduct interviews in Portuguese or in one of the five predominant local languages. Household surveys were conducted at the end of rainy season, representing the peak malaria transmission period for that year. All analyses accounted for a stratified two-stage cluster sample design. The three outcomes of interest included whether the female heads-of-household, pregnant female heads-of-household, or children aged 0–59 months slept under a mosquito bed net during the previous night. Descriptive statistics were calculated for three oversampled districts and for the entire province. Continuous variables were reported as weighted estimates of median (interquartile range [IQR]) and categorical variables were reported as weighted percentages, with each observation being weighted by the inverse of the household or child sampling probability. Multivariable logistic regression analysis with robust covariance estimation to account for clustering was used to estimate factors associated with bed net usage for the three groups of interest. Only households from the oversampled districts were included in the regressions. Covariates were identified a priori and they included: age, education, Portuguese understanding, household size, district, whether all bed nets were donations, whether all bed nets were purchased, monthly income, travel time to health facility, household electricity, bed net distribution at current pregnancy (pregnant group only), and recent fever in child (child group only). Family income was reported in meticais (MZN) (1USD ≈ 36MZN in August 2010 and 1USD ≈ 31MZN in April 2014). If there was evidence of non-linearity (Wald test p < 0.10), continuous variables were modeled using restricted cubic splines [23, 24]. Missing values of covariates were accounted for using multiple imputation techniques. R-software 3.2.2 was used for analyses. Participation in the household surveys was completely voluntary, no incentive was provided for participation. At enrollment written informed consent was obtained. Approvals for study implementation were obtained at the national level from the National Directorate for Public Health of the Ministry of Health (Direcção Nacional de Saúde Publica) and at the Provincial level from the Provincial Health Directorate of Zambézia Province (Direcção Provincial de Saúde-Zambézia). Survey protocol, questionnaires, and informed consent documents were approved by both the Inter-institutional Committee for Bioethics in Health-Zambézia (Comité Inter-institucional de Bioética em Saude-Zambézia) and the Vanderbilt University Institutional Review Board (IRB).

AI Digest

Study Justification:

– Malaria is a major threat globally, contributing heavily to the disease burden in Mozambique.
– Mozambique’s vector control strategy aims for universal coverage of insecticide-treated nets (ITN).
– Understanding factors associated with ITN usage is crucial for improving malaria prevention efforts.

Study Highlights:

– The study conducted two cross-sectional surveys in Zambézia Province, Mozambique in 2010 and 2014.
– The surveys included female heads-of-household and focused on ITN usage.
– Factors associated with ITN usage were analyzed, including education, household size, income, and proximity to health facilities.
– Results showed variations in ITN possession and usage across districts and demographic groups.

Study Recommendations:

– Intensify efforts to improve ITN availability and usage among the poorest, least educated, and those living far from health services.
– Emphasize bringing all geographic regions of the province closer to meeting ITN coverage and utilization targets.

Key Role Players:

– Ministry of Health (National Directorate for Public Health)
– Provincial Health Directorate of Zambézia Province
– Inter-institutional Committee for Bioethics in Health-Zambézia
– Researchers from Vanderbilt University and the University Eduardo Mondlane

Cost Items for Planning Recommendations:

– ITN procurement and distribution
– Education and awareness campaigns
– Training for healthcare workers
– Monitoring and evaluation activities
– Research and data analysis
– Coordination and management of interventions

Strength of Evidence

The strength of evidence for this abstract is 7 out of 10.
The evidence in the abstract is moderately strong. The study is a population-based cross-sectional survey conducted in Zambézia Province, Mozambique. The survey utilized a large representative sample and accounted for a stratified two-stage cluster sample design. Descriptive statistics were calculated for three oversampled districts and for the entire province. Multivariable logistic regression analysis was used to estimate factors associated with mosquito bed net usage. However, the abstract does not provide information on the sample size, response rate, or the specific methodology used for data collection and analysis. To improve the strength of the evidence, the abstract should include these details.

Abstract

The design and implementation of this study are detailed elsewhere [19–22]. Briefly, survey teams completed interviews in 262 enumeration areas (EA) across Zambézia Province. A large representative sample (201 EA) was obtained from three diverse districts (Alto Molócuè, Namacurra, and Morrumbala) in order to increase the precision of survey results while minimizing costs. To further maintain a degree of generalizability across the province, a sample of households were selected for interview throughout the remaining 14 districts (Fig. 1). Map of Zambézia Province with enumeration areas surveyed. *Oversampled districts highlighted, Alto Molócuè, Morrumbala, and Namacurra Map credit: Charlotte Buehler; May 27 2015; Vanderbilt Institute for Global Health; Projection: WGS 1984 Web Mercator Auxiliary Sphere At both baseline and endline the same questionnaire was utilized. While survey responses were not collected from the same households in both surveys, the same sampling methodology was utilized with interviewers returning to the same EAs as in baseline. The Ogumaniha survey tool collects information on over 500 variables in eight dimensions and was developed by a multi-disciplinary team of researchers from Vanderbilt University and the University Eduardo Mondlane in Maputo. The survey designers borrowed many questions and scales deemed appropriate from previous national surveys in Mozambique and other international surveys such as the Demographic Health Survey (DHS) and Multiple Indicator Cluster Survey (MICS). Survey questions covered household demographics; economic status; health knowledge, attitudes and practices; access to health and malaria-related services and products; access to improved water and sanitation; nutritional intake; and others. In both surveys, fourteen mobile teams consisting of a team leader and four interviewers administered the survey face-to-face using mobile phones with an electronic questionnaire installed for data collection. Local authorities were notified prior to the arrival of the survey team. From topographic maps, the survey teams divided the EAs into four quadrants. Starting in the center of the assigned quadrant, interviewers selected a direction, then chose the first household in that direction (i.e. starting point), and approached the nearest four households for interview. Female interviewers conducted the survey with female heads-of-household, defined as the only or principal wife of the immediate family of the household. The female head-of-household was chosen as it was felt she was the most likely person to be familiar with the health and care taking of the entire family. In polygamous families, the eldest wife was selected. Interviewers were trained to conduct interviews in Portuguese or in one of the five predominant local languages. Household surveys were conducted at the end of rainy season, representing the peak malaria transmission period for that year. All analyses accounted for a stratified two-stage cluster sample design. The three outcomes of interest included whether the female heads-of-household, pregnant female heads-of-household, or children aged 0–59 months slept under a mosquito bed net during the previous night. Descriptive statistics were calculated for three oversampled districts and for the entire province. Continuous variables were reported as weighted estimates of median (interquartile range [IQR]) and categorical variables were reported as weighted percentages, with each observation being weighted by the inverse of the household or child sampling probability. Multivariable logistic regression analysis with robust covariance estimation to account for clustering was used to estimate factors associated with bed net usage for the three groups of interest. Only households from the oversampled districts were included in the regressions. Covariates were identified a priori and they included: age, education, Portuguese understanding, household size, district, whether all bed nets were donations, whether all bed nets were purchased, monthly income, travel time to health facility, household electricity, bed net distribution at current pregnancy (pregnant group only), and recent fever in child (child group only). Family income was reported in meticais (MZN) (1USD ≈ 36MZN in August 2010 and 1USD ≈ 31MZN in April 2014). If there was evidence of non-linearity (Wald test p < 0.10), continuous variables were modeled using restricted cubic splines [23, 24]. Missing values of covariates were accounted for using multiple imputation techniques. R-software 3.2.2 was used for analyses. Participation in the household surveys was completely voluntary, no incentive was provided for participation. At enrollment written informed consent was obtained. Approvals for study implementation were obtained at the national level from the National Directorate for Public Health of the Ministry of Health (Direcção Nacional de Saúde Publica) and at the Provincial level from the Provincial Health Directorate of Zambézia Province (Direcção Provincial de Saúde-Zambézia). Survey protocol, questionnaires, and informed consent documents were approved by both the Inter-institutional Committee for Bioethics in Health-Zambézia (Comité Inter-institucional de Bioética em Saude-Zambézia) and the Vanderbilt University Institutional Review Board (IRB).

Materials

N/A

Innovations

Based on the information provided, here are some potential innovations that could be used to improve access to maternal health:

1. Mobile Health Clinics: Implementing mobile health clinics that can travel to remote areas, providing maternal health services and distributing mosquito bed nets to pregnant women and families with young children.

2. Community Health Workers: Training and deploying community health workers who can educate and raise awareness about the importance of using mosquito bed nets for pregnant women and children, as well as provide antenatal care and support.

3. Telemedicine: Utilizing telemedicine technology to connect pregnant women in remote areas with healthcare professionals who can provide guidance and support, including information on the use of mosquito bed nets.

4. Public-Private Partnerships: Collaborating with private sector companies to increase the availability and affordability of mosquito bed nets, making them more accessible to pregnant women and families in need.

5. Behavior Change Communication: Developing targeted behavior change communication campaigns to promote the use of mosquito bed nets among pregnant women and families, emphasizing the importance of protecting against malaria during pregnancy.

6. Maternal Health Vouchers: Introducing maternal health vouchers that can be used to access essential maternal health services, including the provision of mosquito bed nets, particularly for pregnant women in low-income communities.

7. Strengthening Health Systems: Investing in the overall strengthening of health systems, including improving infrastructure, training healthcare workers, and ensuring the availability of essential supplies and equipment, including mosquito bed nets.

It’s important to note that these are just potential recommendations and would need to be further evaluated and tailored to the specific context and needs of the community in Zambézia Province, Mozambique.

AI Innovations Description

The study mentioned in the description focuses on factors associated with the use of mosquito bed nets in Zambézia Province, Mozambique, with the goal of improving access to maternal health. The study found that while progress has been made in increasing mosquito bed net ownership and usage, there are still disparities in coverage and utilization across different districts and population groups.

Based on the findings of the study, the following recommendations can be made to develop innovations and improve access to maternal health:

1. Target the poorest and least educated: The study highlights the need to focus on the most vulnerable populations, such as those with lower education levels and lower income. Innovations should be designed to specifically address the barriers faced by these groups in accessing and utilizing mosquito bed nets.

2. Improve access to health services: The study found that as travel time to a health facility increased, the odds of sleeping under a mosquito net decreased. Innovations should aim to improve access to health facilities, particularly in remote areas, by implementing mobile health services or community-based interventions.

3. Increase education and awareness: The study found that higher education levels were associated with increased mosquito bed net usage. Innovations should focus on providing education and raising awareness about the importance of using mosquito bed nets for pregnant women and children. This can be done through community health education programs, targeted messaging campaigns, and partnerships with local schools and community organizations.

4. Strengthen distribution channels: The study found that some districts had already surpassed goals in terms of coverage and utilization of mosquito bed nets. Innovations should focus on strengthening the distribution channels to ensure that mosquito bed nets reach all geographic regions of the province and are readily available to those in need. This can be done through partnerships with local health facilities, community health workers, and NGOs.

5. Monitor and evaluate progress: Innovations should include a robust monitoring and evaluation component to track progress and identify areas for improvement. This can involve the use of data collection tools, such as mobile phone surveys, to gather real-time information on mosquito bed net ownership and usage. Regular monitoring and evaluation can help identify gaps in coverage and inform targeted interventions.

By implementing these recommendations, it is possible to develop innovations that can improve access to maternal health by increasing the ownership and usage of mosquito bed nets, ultimately reducing the burden of malaria and improving the health outcomes of pregnant women and children in Mozambique.

AI Innovations Methodology

Based on the provided information, here are some potential recommendations for improving access to maternal health:

1. Increase education and awareness: Implement programs that focus on educating pregnant women and their families about the importance of maternal health and the use of mosquito bed nets. This can be done through community health workers, health education campaigns, and targeted messaging.

2. Improve availability and distribution of mosquito bed nets: Ensure that mosquito bed nets are readily available and accessible to pregnant women and their families, especially in areas with high malaria prevalence. This can be achieved through partnerships with local health facilities, NGOs, and government agencies to distribute bed nets to pregnant women and households.

3. Strengthen healthcare infrastructure: Invest in improving healthcare facilities, particularly in remote areas, to reduce travel time and increase access to maternal health services. This can include building or upgrading health clinics, training healthcare workers, and providing essential equipment and supplies.

4. Address socio-economic factors: Addressing socio-economic factors such as household income and electricity access can contribute to improving access to maternal health. Implement income-generating programs and initiatives to alleviate poverty and provide access to basic amenities like electricity, which can support the use of mosquito bed nets.

To simulate the impact of these recommendations on improving access to maternal health, a methodology could include the following steps:

1. Define indicators: Identify specific indicators that measure access to maternal health, such as the percentage of pregnant women using mosquito bed nets, the distance to the nearest health facility, or the percentage of pregnant women receiving prenatal care.

2. Collect baseline data: Gather data on the current status of the indicators in the target population. This can be done through surveys, interviews, or existing data sources.

3. Develop a simulation model: Create a simulation model that incorporates the identified recommendations and their potential impact on the indicators. This model should consider factors such as population size, geographical distribution, and socio-economic characteristics.

4. Input data and run simulations: Input the baseline data into the simulation model and run multiple simulations to estimate the potential impact of the recommendations. This can involve adjusting variables such as the distribution of mosquito bed nets, the availability of healthcare facilities, and the socio-economic conditions.

5. Analyze results: Analyze the simulation results to determine the projected changes in the indicators. This can include calculating the percentage increase in bed net usage, the reduction in travel time to health facilities, or the improvement in prenatal care coverage.

6. Validate and refine the model: Validate the simulation results by comparing them with real-world data or expert opinions. Refine the model based on feedback and make adjustments as necessary.

7. Communicate findings: Present the simulation findings in a clear and concise manner, highlighting the potential impact of the recommendations on improving access to maternal health. This can be done through reports, presentations, or visualizations to facilitate decision-making and resource allocation.

It is important to note that the methodology for simulating the impact may vary depending on the specific context and available data.

Author

Dima Health

Statistics:

Citations: 27

Identifiers:

DOI: 10.1186/s12936-016-1250-5

Research Areas:

Environmental, Health System and Policy, Infectious Diseases, Maternal Access, Maternal and Child Health, Quality of Care, Sexual and Reproductive Health, Social Determinants

Study Countries:

Mozambique

Study Design:

Cross Sectional Study, Grounded Theory

Study Approach:

Quantitative

Participants Gender:

Female