Impact of the Timed and Targeted Counselling Model on Maternal Health Continuum of Care Outcomes in Northern Uganda: Protocol of a Quasi-Experimental Study

Impact of the Timed and Targeted Counselling Model on Maternal Health Continuum of Care Outcomes in Northern Uganda: Protocol of a Quasi-Experimental Study | Research Square window.SnipcartSettings = { analytics: { enabled: false } }; (function() { var accessVector = localStorage.getItem('access_vector') || ''; window.dataLayer = window.dataLayer || []; if (accessVector) { window.dataLayer.push({ user: { profile: { profileInfo: { snid: accessVector } } } }); } })(); (function(w,d,s,l,i){w[l]=w[l]||[];w[l].push({'gtm.start':new Date().getTime(),event:'gtm.js'});var f=d.getElementsByTagName(s)[0],j=d.createElement(s),dl=l!='dataLayer'?'&l='+l:'';j.async=true;j.src='https://www.googletagmanager.com/gtm.js?id='+i+dl;f.parentNode.insertBefore(j,f);})(window,document,'script','dataLayer','GTM-K279D39R'); Browse Preprints In Review Journals COVID-19 Preprints AJE Video Bytes Research Tools Research Promotion AJE Professional Editing AJE Rubriq About Preprint Platform In Review Editorial Policies Our Team Advisory Board Help Center Sign In Submit a Preprint Cite Share Download PDF Study protocol Impact of the Timed and Targeted Counselling Model on Maternal Health Continuum of Care Outcomes in Northern Uganda: Protocol of a Quasi-Experimental Study Douglas Zibugu, Jessica S Gubbels, John Bosco Asiimwe, Gerards Sanne This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-4177199/v1 This work is licensed under a CC BY 4.0 License Status: Posted Version 1 posted You are reading this latest preprint version Abstract Background About 287,000 women died during and following pregnancy and childbirth in 2020 worldwide. Almost 95% of all these maternal deaths occurred in low and lower middle-income countries, and most could have been prevented. The timed and targeted counselling behavioural change approach, implemented by community health workers targeting mothers and their male caregivers (e.g., their husband, brother or father), is expected to positively impact overall maternal health. This study aims to assess the impact of timed and targeted counselling on the continuum of care outcomes in Northern Uganda. Methods This study will employ a cross-sectional quasiexperimental design, with retrospective data collection comparing an intervention group to a control group. The main outcome measures are antenatal care, place of delivery and postnatal care. The study employs a two-stage sampling procedure purposively selecting the Oyam District, including two strata of subcounties: Aber (Treatment) and Otwal (Control). The required sample size consisted of 456 mothers per treatment group (i.e., 912 in total). The study participant selection criterion will be mothers who have given birth between the 2nd the 12th month of the study area. Propensity score matching will be used to control for confounders and improve causal inference. Sensitivity analysis will be carried out to test the robustness of the results to unmeasured confounders in the propensity score match. After regression, postmodel estimation tests such as the Akaike information criterion, the link test and the Wald test will be carried out. Discussion This study is the first to evaluate the impact of timed and targeted counselling on maternal health in Northern Uganda. These findings will be used to modify the implementation of the timed and targeted counselling approach, thereby enhancing its impact, efficiency, and effectiveness. Protocol Registration This study protocol was registered under the Makerere University School of Social Sciences Research Ethics Committee (MAKS REC) under MAKSSREC 10.2023.710 (registration date 30th of November 2023) and the Uganda National Council for Science and Technology (UNCST HS3826ES). Timed and Targeted Counselling Community Health Workers Antenatal Care Postnatal Care Maternal Health Figures Figure 1 Figure 2 Background Maternal health refers to the health of women during pregnancy, childbirth and the postnatal period. Each stage should be a positive experience, ensuring women and their babies reach their full potential for health and well-being( 1 ). About 287 000 women died during and following pregnancy and childbirth in 2020. Almost 95% of all these maternal deaths occurred in low and lower middle-income countries, and most could have been prevented( 2 ). In Uganda, the maternal mortality rate is 336 deaths per 100,000 live births, which is higher than the UN’s target to reduce the global maternal mortality ratio to less than 70 per 100,000 live births( 3 , 4 ). Furthermore, the ANC attendance rate in Uganda stands at 59.9% for four or more visits( 5 ). This inadequate utilization of ANC has been attributed to factors such as long wait times at health facilities, reliance on family or traditional healers for advice, and delayed initiation of care ( 7 ). Compared with home birth, childbirth in a health facility attended by skilled birth attendants is associated with improved maternal and child health outcomes, including lower rates of maternal morbidity and mortality( 8 , 9 ). However, in Uganda, only 76.6% of pregnant women deliver in a health facility ( 6 ). The choice of the place of delivery is often influenced by factors such as social, cultural and environmental factors ( 10 ). However, regarding Uganda specifically, little is known about factors that influence the use of health facilities for delivery, especially in rural areas ( 10 ). Unfortunately, in Uganda, the knowledge of professionals and policy makers regarding the uptake of early postnatal care (PNC) is also low, hindering the formulation of effective policies aimed at reducing maternal mortality ( 11 ). The first 7 postnatal days account for approximately 65% of maternal deaths, with half (50%) of these deaths occurring within the first postnatal day ( 12 ). However, in Uganda, only approximately 22.5% of new mothers attend at least one PNC visit within six weeks ( 6 ). Timed and targeted counselling (ttC) is a family-inclusive behavioural change communication approach that targets families expecting a child, especially those most vulnerable and marginalized ( 13 ). ttC encompasses a wide range of life-saving health practices through appropriately timed messages delivered through interactive storytelling. It applies a dialogue counselling methodology between community health workers (CHWs) and pregnant women’s households based on the assessment of current needs and practices and the negotiation of progressive improvements. Importantly, ttC seeks to engage both parents as decision-makers, embracing a family-inclusive and gender-transformative model of child health and development in which the positive contribution of male caregivers is emphasized. ttC can be delivered by a range of cadres, including officially recognized CHWs and guide mothers or volunteers ( 13 ). In Northern Uganda, the ttC package is currently implemented by CHWs, based on a facilitator’s manual. To simplify the ttC message, the CHWs use interactive stories to draw different scenarios of problems, best practices and possible solutions faced during and immediately after the pregnancy period. These messages are timed, given at specific intervals and targeted, in order for the message to come neither too early nor too late for the mother, throughout the pregnancy and the postnatal period, to effectively influence the decision making in the households both for the mother and their male caregivers ( 13 ). 1.1 Conceptual Framework The conceptual framework for the study (shown in Fig. 1 below) shows how maternal health outcomes are influenced by various factors. These outcomes, including ANC, place of delivery and postnatal care, are not only directly affected by demographic factors, CHW interactions, male involvement, conflict-affected regions, and ttC behavioral approaches but also indirectly affected by intermediate variables such as mental health problems (e.g., anxiety, depression, stress, body image issues) and physical health problems (e.g., vaginal bleeding, convulsions or fits, severe headaches with blurred vision, fever, and excessive weakness). Northern Uganda is a conflict-affected region, which is critical for this study due to its high poverty rates( 14 ), cultural diversity, limited healthcare access, poor health outcomes, and the presence of conflict-affected individuals( 15 ). Furthermore, the conceptual framework is based on two models to understand the indirect impact on maternal health. These two models, i.e., the Health Belief Model (HBM) ( 16 ) and Andersen’s Behavioral Model ( 17 , 18 ), will support in investigating the sociocognitive factors that may mediate the relationship between the implementation of ttC and changes in the maternal health continuum of care outcomes. The adaptations to this conceptual framework based on the 2 models above are premised on the evolution of the health promotion and disease prevention models that have undergone changes over time. There has been a shift from fear-based approaches to reward-based and self-regulatory strategies, which has highlighted the reciprocal interplay between individual capabilities, motivation, and environmental determinants of health behavior( 19 ). However, interventions targeting health-related behaviors have often yielded modest effects, indicating the need to draw upon a wider range of theories that incorporate social, cultural, and economic factors( 20 ). Therefore, our study strategically incorporates the HBM and Andersen’s behavioural model, and this deliberate integration aims to investigate whether emphasizing the factors outlined in these models within the community application of the ttC model will result in more effective outcomes, specifically contributing to the enhancement of overall maternal health in the implementation area. 1.1.2 Health Belief Model The HBM considers factors such as perceived susceptibility, severity, benefits, barriers, and cues to action to predict an individual’s health behaviors( 16 ). In the context of this maternal health study, this translates to how these mothers perceive their susceptibility to maternal health complications/issues, the severity of such issues, the benefits of engaging in recommended health behaviours (such as those promoted during the ttC), the barriers these women perceive and the cues that prompt them to action. While the HBM provides valuable insights into individual beliefs and decision-making processes, it has theoretical limitations, such as undefined variable ordering and limited practical application ( 21 ). Based on these limitations, Anderson’s behavioral model has also been included in the conceptual framework to enhance the robustness of the research. 1.1.3 Andersen’s Behavioral Model Andersen's behavioural model complements the HBM by considering broader factors such as individuals’ personal characteristics and beliefs (predisposing factors), available resources and opportunities (enabling factors), and perceived needs and motivations (need factors). In the current study, this model helps us to understand how individual characteristics, access to resources and perceived needs shape healthcare utilization and contributes to our understanding of the contextual factors influencing maternal health to assess access inequality and inform policy development for equitable access to care ( 17 , 18 ). HBM focuses on the individual’s cognitive factors, and Andersen’s behavioral model focuses on the individual’s characteristics. By reinforcing the HBM with Andersen's behavioral model, the study will thus leverage the strengths of both models, providing a better picture of the interplay among these psychological and belief dimensions. This will lead to a better and more effective ttC model in the unique sociocultural and economic context of Northern Uganda. 1.1.1 General Objective The main objective of the study will be to assess the socioeconomic impact of ttC on the maternal health continuum of care outcomes in Northern Uganda. 1.1.1.1 Specific objectives 1. To investigate the effect of ttC on ANC utilization among pregnant mothers. 2. To evaluate the influence of ttC implementation on the place of delivery among pregnant women. 3. To assess the impact of ttC on PNC utilization among mothers. 1.1.2 Hypotheses The study will be guided by the following hypothesis. 1. ttC implementation will lead to an increase in the utilization of ANC among pregnant women. 2. ttC will result in a greater proportion of pregnant women delivering at health facilities. 3. ttC will lead to increased PNC utilization among mothers. 1.1.3 Research questions to be examined How do demographic factors, CHW interactions, male involvement, and ttC directly impact the maternal health continuum of care outcomes in Northern Uganda? Does emphasizing the factors outlined in the HBM and Andersen’s behavioural model within the ttC model result in an effective maternal health continuum of care outcomes in Northern Uganda? Methods 2.1 Research Design A cross-sectional quasiexperimental research design with retrospective data collection comparing the intervention group to the control group will be used. The intervention group will receive ttC, and the control group will not receive ttC. To control for potential confounding factors, propensity score matching (PSM) will be used as the preferred method to improve causal inference. PSM will match each treated unit with a nontreated unit of similar characteristics. The preferred characteristics for matching in this study were education level, birth parity, income and age of the participants( 22 ). 2.2 Research Area The study area will be the northern region of Uganda, specifically the Oyam district, which was selected because ttC is implemented in the Aber subcounty of the district. Northern Uganda in particular is an important region for studying maternal health due to the high need for maternal health interventions, limited access to healthcare services, cultural diversity, high poverty rates ( 14 ), and the relevance for conflict-affected populations ( 15 ). Taking the Oyam district as the sampling frame, two subcounties, Aber and Otwal, were chosen for reaching out to mothers who would have given birth within the past 2–12 months. Aber is the treatment subcounty, and Otwal is the control subcounty because ttC is implemented in the former but not in the latter. Significantly, both subcounties were chosen because they do not share borders with each other; thus, limited spillovers are expected from the treatment subcounty to the control subcounty. 2.3 ttC intervention Before implementation of the ttC approach in the communities, the CHWs were first trained with the ttC package, using a developed standardized manual on what to do and what targeted messages should be given to the household and the right timing for each. They were then grouped and supervised administratively by district health officials. CHWs then identified households with pregnant women or likely households with reproductive-aged women and encouraged them to register and notify the CHW as soon as a pregnancy case popped up. When a pregnancy is identified by the CHW, they visit these pregnant households in a timely manner during the pregnancy and postnatally with targeted messages to encourage the pregnant women to attend ANC services and plan to give birth in the presence of skilled personnel and attend to PNC. They further encourage male caregivers to be involved in the whole cycle from pregnancy through birth to nurturing the baby by providing both emotional and logistical support. CHWs make four visits during pregnancy before birth and seven visits postnatally in the first 2 years after birth, as explained in Table 1 , which is adapted from the ttC manual( 13 ), and in an earlier study on the effects of implementing the ttC model on pregnancy outcomes and newborn survival in rural Uganda( 23 ). Table 1 below summarizes and provides a concise and visually organized reference for the key components and objectives of the ttC approach. Table 1 The overview of key components of the ttC approach. Timing (outcome focused on) Targeted Counselling given by CHW Between 2–3 months of pregnancy (ANC) The CHW encourage the pregnant woman to go for ANC within the first trimester and identify the home care variability in the household that might be needed by the pregnant woman. The CHWs encourage male caregivers to accompany the women for ANCs. They encourage good nutrition in pregnancy, uptake of iron/folic acid tablets, one extra meal a day for the pregnant woman, handwashing with soap, and having 4 or more ANCs Between 4–5 months of pregnancy (ANC) This visit is more focused on the disease burden especially on HIV and AIDs, Tuberculosis and other STIs testing and prevention awareness. Between 6–7 months of pregnancy (ANC, Skilled Birth Attendance) This is meant to promote health facility delivery, assisting the family in drawing up a birth plan, including having skilled personnel available during birth and talk about available family planning measures to encourage 2-year period child spacing. Between the 8–9 months of pregnancy (ANC, Skilled Birth Attendance) This is to review the birth plan and reiterate the importance of essential newborn care practices. The CHW also encourages exclusive breastfeeding and timely seeking for danger signs. During the 1st week of life postnatal (PNC) The CHW encourages essential maternal and neonatal care, access to postnatal, and postpartum care and timely seeking for danger signs. 1 month postnatal (PNC) Growth monitoring, encouraging vaccination against preventable diseases and care seeking in case of fever. After the 5th month of life (PNC) CHW encourages complementary feeding and the importance of a balanced diet for the baby between the 6th to the 9th month. At the 9th month of life (PNC) CHW encourages breast feeding along with complementary foods between the 9th to the 12th month and encourages uptake of micronutrients such as vitamin A supplements. 10th Visit (PNC) Encouraging iron rich foods, vitamin A supplements, deworming, holistic child development 11th Visit (PNC) Giving three to four meals to the child a day, child sleeping under a mosquito bednet. Note: CHW = Community Health Worker, ANC = Antenatal Care, HIV = Human Immunodeficiency Virus, AIDS = Acquired Immunodeficiency Syndrome, STIs = Sexually Transmitted Infections, PNC = Postnatal Care, ttC = Timed and Targeted Counselling 2.4 Study population The study population/sampling frame is composed of respondents at the household level who will be selected from 2 subcounties, one with mothers who have given birth between 2–12 months of age and who participated in the ttC model sessions (with 876 mothers in the treatment group forming part of the sampling frame) or mothers whose access to ttC sessions was limited to the other subcounty of interest (with 1355 mothers in the control group also forming part of the sampling frame). Thus, the total sampling frame/study population will have 2231 mothers from which the sample will be drawn. 2.5 Study Instruments The study will use a cross-sectional primary data source. Structured household questionnaires will be administered to the study participants by the research assistants to gather quantitative data. The questionnaire included different data types, such as categorical, numerical, and scale options, to facilitate the quantification of information. For validity and reliability, the questionnaire will have embedded skips and rules to ease data collection and perform real-time data cleaning. The study tools/instruments to be used will also include the CHW household registers that register all the mothers and the ttC register that includes the targeted sessions and number of visits. These will provide the contact and location from which participants will be sampled and the data collected retrospectively. Tools will be uploaded to Kobo Kollect, a data collection application that will be loaded on phone devices and administered. The Kobo loaded questionnaire will also have mandatory questions marked for emphasis and will have an embedded translation into the local language to ease data collection. A field pretest of the structured questionnaire will be performed before the final roll out. 2.6 Data Management To ensure the integrity and accuracy of the collected data, a robust data management strategy will be implemented. A team of 25 research assistants will be trained and given pre- and posttraining tests to test their knowledge with regard to the topic of study and data collection and management processes. The data will be checked for errors at the end of every day, and the research assistant team will meet daily after fieldwork to synchronize their data to a secure server to not only act as a backup but also to allow the principal investigator to check for validity and accuracy of the tools using the GPS on the Kobo tools loaded on the phones and the time stamps on the tools showing the start and end time for each tool. There will also be ad hoc checks in the field to see that the data are not being manipulated into the system but are actual data. The data collectors will also be given a unique identifier to enhance the traceability of each tool to individual entrants. Inaccurate information will therefore be easily traced to the research assistant who submitted it to the system, the records will be deleted, and the researcher will stop contributing to the data collection. 2.7 Sample size calculation The sample size will be estimated using power analysis from a two-sided Z test adopted from Charan and Biswas ( 24 )with a 1.5 design effect and a 10% nonresponse rate( 25 ). This will take into account the desired level of statistical significance, effect size, and expected variability. The formula is as follows: $$n=\frac{2({Z}_{\alpha /2}+{Z}_{\beta }{)}^{2}P(1-P)}{({P}_{1}-{P}_{2}{)}^{2}}$$ where: \({Z}_{\alpha /2}\) = \({Z}_{0.05/2}\) = \({Z}_{0.025}\) =1.96 is the critical value (from the Z table) at type 1 error, which is the critical value for the chosen level of significance \({Z}_{\beta }\) = \({Z}_{0.20}\) = 0.842 is the critical value for the chosen power from the standard normal distribution at 80% \({P}_{1}\) = 71.5% is the estimated proportion of the population with the outcome of interest in the control group. This figure is derived by averaging the proportions observed in two previously related studies ( 23 , 25 )examining one of the outcomes of interest, facility-based deliveries without intervention. \({P}_{2}\) = 81.5% is the estimated proportion of the population with the outcome of interest in the intervention group. Like P1, this is also obtained by averaging proportions from the same two related studies( 23 , 25 ), but in this case, focusing on facility-based delivery with intervention. \(P\) = 76.5% is the pooled prevalence [prevalence in case group \({(P}_{1})\) + prevalence in control group \({ (P}_{2})\) ]/2 \({P}_{1}-{P}_{2}\) = -10% is the difference in the proportion of events in the two groups. The sample size calculated using the power analysis and taking into consideration the design effect and nonresponse yielded 465 participants in each of the groups (control (465) and treatment (465)) at 80% power and at the 5% level of significance. 2.8 Sampling procedure To reach the above calculated sample size, a two-stage sampling procedure will be used. The first stage will be purposive sampling of Oyam District in Northern Uganda due to the ttC implemented within some subcounties in the district, its poor health outcomes and limited access to health care, which all make it an ideal location for the study. The second stage will involve stratified sampling, where the 2 strata subcounties of Aber and Otwal will provide the sampling unit for women of reproductive age (between 18 and 49 years) who have given birth between 2 and 12 months of age. The subcounties in Oyam District will be stratified based on whether the treatment (ttC implementation) was applied (Aber Subcounty) or not (Otwal Subcounty). Stratified sampling will be carried out within these subcounties to ensure a representative sample for later generalization of findings to other similar settings. The participants will be randomly selected from each stratum to allow for precise estimates within subgroups and improve the overall representativeness of the sample. The study aims to reach a total of 930 mothers (from the control (465) and treatment (465)) as per the sample size calculation above. The flow of this sampling process is shown in Fig. 2 below. By justifying and implementing this comprehensive sampling methodology, the study will aim to enhance the reliability and validity of the research findings. 2.9 Study variables The primary outcome variables for the study of the impact of ttC on the maternal health continuum of care will be ANC utilization, skilled birth attendance and PNC. The ttC variable is nominally defined by whether the household received ttC. Table 2 Study variables and their definitions. No Variable Definition of Variable Dependent Variables 1 ANC This is defined as visits to a skilled health personnel during pregnancy. 2 Place of delivery This refers to the location where a pregnant woman gives birth, such as at the health facility or at home. 3 PNC This refers to the care given to the mother immediately after birth. This should be atleast one Post-Natal Care visit within the first 6 weeks after birth. Independent Variables 1 CHW These are trained personnel that provide household level health education to reproductive age women, pregnant women, new mothers together with their male caregivers. These Community Health Workers interaction with pregnancy women and their male caregivers is assessed quantitatively by categorical and scalar answer options. 2 Male Involvement This is defined by male caregivers actively supporting their families and caring for their women in order to access better health services. This is assessed with the partner attending counselling sessions, accompanying pregnant mother for Antenatal Care, and Post-Natal Care and providing both emotional and financial support using categorical and scalar answer options to questions. 3 Socio-Demographic factors This refers to the social and demographic characteristics of the population and how they influence maternal health that is age, education level, parity and marital status of the mothers assessed using numeric, scalar and categorical options to the questions. 4 TTC This is a behavioral change methodology that targets pregnant women with a wide range of life-saving health practices through appropriately timed messages delivered using interactive storytelling. This will either be implemented in the treatment subcounty or not, in the control subcounty for the study and will be assessed using a categorical question of whether it was received or not. Intermediate Variables 1 Health Belief Model This model focuses on individual beliefs and attitudes towards maternal health such as how the mothers perceive their susceptibility to maternal health complications/issues, the severity of such issues, the benefits of engaging in recommended health behaviours (such as those promoted during the counselling), the barriers these women perceive and the cues that prompt them to action. This will be assessed using scalar and categorical answer options to the questions. 2 Andersen’s Behavioral Model This model highlights the determinants of health care utilization such as predisposing, enabling and need factors. This model thus helps us to understand how mother’s individual characteristics, access to resources and perceived needs shape their healthcare utilization. This will be assessed using categorical and scalar options to the questions. Note: ANC = antenatal care, PNC = postnatal care, ttC = timed and targeted counselling, CHW = community health worker. 2.10 Data analysis 2.10.1 Univariate Analysis After the data have been cleaned, they will be exported to STATA for analysis and Excel for better visualization. At the univariate level, descriptive and summary statistics will be drawn using the frequencies, measures of central tendency and dispersion with proportions to provide a descriptive analysis of the context in relation to each of the factors, such as demographics and behavioral characteristics. 2.10.2 Bivariate analysis At the bivariate level, propensity score matching will then be used to determine the effect of ttC on ANC, PNC, and place of delivery. The data will be used to estimate the propensity scores by logistic regression using a logit model to remove the effects of confounding factors by comparing outcomes between treated and control participants who share a similar propensity score( 26 ). The participants will then be matched using the estimated scores. The greedy nearest neighbor method will be used to implement pair matching( 26 ). Furthermore, a calliper restriction with a width equal to 0.2( 27 ) of the logit of the propensity score will be used. After matching, the standardized mean difference (SMD) of 0.1 will be used to measure the balance between the covariates after matching( 28 ) Finally, after the selection of the covariates above 0.1 SMD, a logistic regression will then be rerun to study the impact of ttC on ANC, PNC and place of delivery while controlling for other variables. Furthermore, sensitivity analysis will be performed to test for the robustness of the results to the unmeasured confounders in PSM. The Pearson chi-square test will be carried out to compare maternal outcomes between the control and case groups. A test for multicollinearity will be performed to check the variance information factor between two or more predictor variables to ensure statistical significance in the regressions. 2.10.3 Multivariate Analysis At the multivariate level, logistic regression will be used to describe the data. It is appropriate to use logistic regression for predictive analysis whenever the dependent variable is binary. This will be used to explain the relationship between the variables ( 29 ). After model fitting, postestimation tests such as the Akaike information criterion (AIC) will be performed to test for goodness of fit ( 30 ). The link test will also be used to test for goodness of fit. The Wald test will be used to test the magnitude of the causal inference and test for the significance of the coefficients, whether to retain them or not. Discussion This study protocol describes how the current study will evaluate the impact of the ttC model on the maternal health continuum of care outcomes in Northern Uganda using a cross-sectional quasiexperimental design and employing propensity score matching to control for selection bias. This study will integrate the HBM, which emphasizes individuals' decisions on health behaviors based on perceived threat, benefits, barriers, and self-efficacy( 31 ), with Andersen's behavioral model, which identifies predisposing, enabling, need, reinforcing, and health-care utilization factors influencing health behavior( 18 ), to obtain a comprehensive understanding of whether emphasizing these factors within the ttC model will result in a more effective maternal health continuum of care outcomes. Northern Uganda is a critical region for this study due to its high poverty rates( 13 ), cultural diversity, limited healthcare access, poor health outcomes, and presence of conflict-affected individuals( 14 ). This research will generate new and original evidence on the impact of male involvement, demographic factors, and CHWs on the overall maternal health continuum of care outcomes. The results will inform resource allocation and target the most effective factors to promote healthy behaviors. 3.1 Strengths and limitations This research addresses a global challenge of maternal health and how a behavioural change communication model, ttC, could impact maternal mortality. Propensity score matching to remove confounding factors allows the exploration of relationships between various variables and maternal health. Having northern Uganda as the area of study gives more focus to conflict-affected regions whose results could be adapted to other similar settings. However, there may be response bias due to self-reporting, which may affect the information gathered. This limitation will be addressed by providing confidentiality assurance to the study participants to allow them to answer more truthfully. This is not a randomized controlled trial, and data are being collected retrospectively, postnatal. This means that there is no control over the quality of the training to the VHTs, the counselling given to the mothers or the quality of information stored in the birth cards, the ttC registers or the VHT registers. To mitigate this challenge, the researcher will emphasize in the training to research assistants that a birth must be ascertained through tracing the records at the health facilities and triangulating with the CHW registers and birth cards in possession of the mothers to have more reliable and accurate study data collected. Abbreviations AIC: Akaike Information Criterion; AIDs: Acquired Immunodeficiency Syndrome; ANC: Antenatal Care; CHW: Community Health Worker; HBM: Health Belief Model; HIV: Human Immunodeficiency Virus; IRB: Institutional Review Board; NNM: Greedy Nearest Neighbor; PNC: Postnatal Care; PSM: Propensity Score Matching; SMD: Standardized Mean Difference; TB: Tuberculosis; ttC: Timed and Targeted Counselling; UNCST: Uganda National Council for Science and Technology; VIF: Variance Inflation Factor Declarations Availability of data and materials Not applicable Ethics approval and consent to participate This study is being conducted in Northern Uganda, considering cultural and social aspects. Ethical approval was obtained from the Institutional Review Board (IRB) at Makerere University School of Social Sciences (MAKSSREC 10.2023.710), and the study was registered under the Uganda National Council for Science and Technology (UNCST HS3826ES) to safeguard participants. The consent procedures will be carried out in the local language by trained field team members using translated consent forms for those with limited education. The field team will undergo extensive training on research ethics, consent procedures, interviewer techniques, and instrument usage. The principal investigator will lead the field team, composed of locally recruited individuals fluent in the local language with prior experience in data collection among similar populations. The study will not involve special populations, and data analysis will be conducted in-country. Consent for publication Not Applicable Availability of data and materials Not Applicable Competing interests The authors declare that they have no competing interests. Funding This study is being supported by the Department of Health Promotion, School of Nutrition and Translational Research in Metabolism (NUTRIM), Maastricht University. It is crucial to note that the content presented in this study is the sole responsibility of the authors and not the university. Authors' contributions ZD conceptualized, drafted, and revised the manuscript. SG, JBA, and JG helped draft the manuscript and provided valuable feedback for revision, including editing the manuscript. All the authors have read and approved the final manuscript. Acknowledgements Special acknowledgement is given to the biostatistician of Oyam District, Sara Awor, for providing the district demographic statistics on births. Author Details Department of Health Promotion, School of Nutrition and Translational Research in Metabolism (NUTRIM), Maastricht University, 6211 MD Maastricht, The Netherlands. School of Statistics & Planning, Makerere University, Kampala, Uganda. References Maternal health [Internet]. [cited 2024 Mar 27]. Available from: https://www.who.int/health-topics/maternal-health Maternal mortality [Internet]. [cited 2024 Mar 27]. 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Available from: https://www.statisticshowto.com/propensity-score-matching/ Babughirana G, Gerards S, Mokori A, Charles Baigereza I, Baba Magala A, Kwikiriza R, et al. Effects of timed and targeted counselling by community health workers on maternal and household practices, and pregnancy and newborn outcomes in rural Uganda. Sex Reprod Healthc. 2023 Jun 1;36:100845. Charan J, Biswas T. How to Calculate Sample Size for Different Study Designs in Medical Research? Indian J Psychol Med. 2013;35(2):121–6. Ekirapa-Kiracho E, Muhumuza Kananura R, Tetui M, Namazzi G, Mutebi A, George A, et al. Effect of a participatory multisectoral maternal and newborn intervention on maternal health service utilization and newborn care practices: a quasi-experimental study in three rural Ugandan districts. Glob Health Action. 2017 Sep 5;10(sup4):1363506. Austin PC, Xin Yu AY, Vyas MV, Kapral MK. Applying Propensity Score Methods in Clinical Research in Neurology. Neurology. 2021 Nov 2;97(18):856–63. Austin PC. Optimal caliper widths for propensity-score matching when estimating differences in means and differences in proportions in observational studies. Pharm Stat. 2011 Mar;10(2):150–61. Zhang Z, Kim HJ, Lonjon G, Zhu Y. Balance diagnostics after propensity score matching. Ann Transl Med. 2019 Jan;7(1):16. Statistics Solutions [Internet]. [cited 2024 Mar 24]. What is Logistic Regression? Available from: https://www.statisticssolutions.com/free-resources/directory-of-statistical-analyses/what-is-logistic-regression/ Busemeyer JR, Diederich A. Chapter 4 - Estimation and Testing of Computational Psychological Models. In: Glimcher PW, Fehr E, editors. Neuroeconomics (Second Edition) [Internet]. San Diego: Academic Press; 2014 [cited 2024 Mar 22]. p. 49–61. Available from: https://www.sciencedirect.com/science/article/pii/B9780124160088000048 Abraham C, Sheeran P. The Health Belief Model. In 2015. Additional Declarations No competing interests reported. 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Also discoverable on Platform About Our Team In Review Editorial Policies Advisory Board Help Center Resources Author Services Accessibility API Access RSS feed Manage Cookie Preferences © Research Square 2026 | ISSN 2693-5015 (online) Privacy Policy Terms of Service Do Not Sell My Personal Information {"props":{"pageProps":{"initialData":{"identity":"rs-4177199","acceptedTermsAndConditions":true,"allowDirectSubmit":true,"archivedVersions":[],"articleType":"Study protocol","associatedPublications":[],"authors":[{"id":286428101,"identity":"390bbf2e-eaa8-45c1-ba44-365a973725aa","order_by":0,"name":"Douglas Zibugu","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAA0klEQVRIiWNgGAWjYDACZh4wWd8PohIKSNDCOLMBpMWAKGugWjYcANHEaJFv5z0m+bXNmtn4/OrEDw8MGOT5xQ7g18LYzJcmLduWzmZ24+1mCaDDDGfOTsCvhZmZx0xasu0wj9mNsxtAWhIMbhPQwgbVImE84+zmH0Rp4QFqkfzYdtjAgL93G3G2SDDzJVsznEtPkLjBu80iwUCCsF/k+88evPmjzDqBv//s5ps/Kmzk+aUJaAEBSGxKgFVKEFYOAow/QCT/AeJUj4JRMApGwcgDAHO1PZMDIad1AAAAAElFTkSuQmCC","orcid":"","institution":"Maastricht University","correspondingAuthor":true,"prefix":"","firstName":"Douglas","middleName":"","lastName":"Zibugu","suffix":""},{"id":286428102,"identity":"57dcba60-d6a4-4bba-b8fb-2bbb740aa6bf","order_by":1,"name":"Jessica S Gubbels","email":"","orcid":"","institution":"Maastricht University","correspondingAuthor":false,"prefix":"","firstName":"Jessica","middleName":"S","lastName":"Gubbels","suffix":""},{"id":286428104,"identity":"683dba09-d97c-4e1d-a0f7-4aa5a7d9f01f","order_by":2,"name":"John Bosco Asiimwe","email":"","orcid":"","institution":"Makerere University","correspondingAuthor":false,"prefix":"","firstName":"John","middleName":"Bosco","lastName":"Asiimwe","suffix":""},{"id":286428105,"identity":"c561eed5-28f8-46ac-9231-0a6bd7e05d20","order_by":3,"name":"Gerards Sanne","email":"","orcid":"","institution":"Maastricht University","correspondingAuthor":false,"prefix":"","firstName":"Gerards","middleName":"","lastName":"Sanne","suffix":""}],"badges":[],"createdAt":"2024-03-27 15:35:48","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-4177199/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-4177199/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":54161380,"identity":"2ba98bf5-eaf9-4f70-be35-6dd6f6987e52","added_by":"auto","created_at":"2024-04-05 13:05:39","extension":"png","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":343874,"visible":true,"origin":"","legend":"\u003cp\u003eThe conceptual framework for studying the impact of ttC on the maternal health continuum of care outcomes.\u003c/p\u003e\n\u003cp\u003e\u003cem\u003eNote: ttC= Timed and targeted counselling\u003c/em\u003e\u003c/p\u003e","description":"","filename":"floatimage4.png","url":"https://assets-eu.researchsquare.com/files/rs-4177199/v1/dbdf983ad439d6eed107a550.png"},{"id":54161360,"identity":"4e138bdb-d610-47b7-82a8-87fc01a7ed03","added_by":"auto","created_at":"2024-04-05 13:05:39","extension":"png","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":297888,"visible":true,"origin":"","legend":"\u003cp\u003eThe flow recruitment diagram\u003c/p\u003e","description":"","filename":"floatimage5.png","url":"https://assets-eu.researchsquare.com/files/rs-4177199/v1/50b39c98994d55d3261e1c2f.png"},{"id":60923728,"identity":"e151e10c-7911-4717-a49f-727d3940d5b1","added_by":"auto","created_at":"2024-07-23 15:24:05","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":1262847,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-4177199/v1/009c6d78-9b45-45d2-ab1b-c35559dacbde.pdf"}],"financialInterests":"No competing interests reported.","formattedTitle":"Impact of the Timed and Targeted Counselling Model on Maternal Health Continuum of Care Outcomes in Northern Uganda: Protocol of a Quasi-Experimental Study","fulltext":[{"header":"Background","content":"\u003cp\u003eMaternal health refers to the health of women during pregnancy, childbirth and the postnatal period. Each stage should be a positive experience, ensuring women and their babies reach their full potential for health and well-being(\u003cspan\u003e1\u003c/span\u003e). About 287 000 women died during and following pregnancy and childbirth in 2020. Almost 95% of all these maternal deaths occurred in low and lower middle-income countries, and most could have been prevented(\u003cspan\u003e2\u003c/span\u003e).\u003c/p\u003e\n\u003cp\u003eIn Uganda, the maternal mortality rate is 336 deaths per 100,000 live births, which is higher than the UN\u0026rsquo;s target to reduce the global maternal mortality ratio to less than 70 per 100,000 live births(\u003cspan\u003e3\u003c/span\u003e, \u003cspan\u003e4\u003c/span\u003e). Furthermore, the ANC attendance rate in Uganda stands at 59.9% for four or more visits(\u003cspan\u003e5\u003c/span\u003e). This inadequate utilization of ANC has been attributed to factors such as long wait times at health facilities, reliance on family or traditional healers for advice, and delayed initiation of care (\u003cspan\u003e7\u003c/span\u003e). Compared with home birth, childbirth in a health facility attended by skilled birth attendants is associated with improved maternal and child health outcomes, including lower rates of maternal morbidity and mortality(\u003cspan\u003e8\u003c/span\u003e, \u003cspan\u003e9\u003c/span\u003e). However, in Uganda, only 76.6% of pregnant women deliver in a health facility (\u003cspan\u003e6\u003c/span\u003e). The choice of the place of delivery is often influenced by factors such as social, cultural and environmental factors (\u003cspan\u003e10\u003c/span\u003e). However, regarding Uganda specifically, little is known about factors that influence the use of health facilities for delivery, especially in rural areas (\u003cspan\u003e10\u003c/span\u003e). Unfortunately, in Uganda, the knowledge of professionals and policy makers regarding the uptake of early postnatal care (PNC) is also low, hindering the formulation of effective policies aimed at reducing maternal mortality (\u003cspan\u003e11\u003c/span\u003e). The first 7 postnatal days account for approximately 65% of maternal deaths, with half (50%) of these deaths occurring within the first postnatal day (\u003cspan\u003e12\u003c/span\u003e). However, in Uganda, only approximately 22.5% of new mothers attend at least one PNC visit within six weeks (\u003cspan\u003e6\u003c/span\u003e).\u003c/p\u003e\n\u003cp\u003eTimed and targeted counselling (ttC) is a family-inclusive behavioural change communication approach that targets families expecting a child, especially those most vulnerable and marginalized (\u003cspan\u003e13\u003c/span\u003e). ttC encompasses a wide range of life-saving health practices through appropriately timed messages delivered through interactive storytelling. It applies a dialogue counselling methodology between community health workers (CHWs) and pregnant women\u0026rsquo;s households based on the assessment of current needs and practices and the negotiation of progressive improvements. Importantly, ttC seeks to engage both parents as decision-makers, embracing a family-inclusive and gender-transformative model of child health and development in which the positive contribution of male caregivers is emphasized. ttC can be delivered by a range of cadres, including officially recognized CHWs and guide mothers or volunteers (\u003cspan\u003e13\u003c/span\u003e).\u003c/p\u003e\n\u003cp\u003eIn Northern Uganda, the ttC package is currently implemented by CHWs, based on a facilitator\u0026rsquo;s manual. To simplify the ttC message, the CHWs use interactive stories to draw different scenarios of problems, best practices and possible solutions faced during and immediately after the pregnancy period. These messages are timed, given at specific intervals and targeted, in order for the message to come neither too early nor too late for the mother, throughout the pregnancy and the postnatal period, to effectively influence the decision making in the households both for the mother and their male caregivers (\u003cspan\u003e13\u003c/span\u003e).\u003c/p\u003e\n\u003cdiv id=\"Sec2\"\u003e\n \u003ch2\u003e1.1 Conceptual Framework\u003c/h2\u003e\n \u003cp\u003eThe conceptual framework for the study (shown in Fig.\u0026nbsp;1 below) shows how maternal health outcomes are influenced by various factors. These outcomes, including ANC, place of delivery and postnatal care, are not only directly affected by demographic factors, CHW interactions, male involvement, conflict-affected regions, and ttC behavioral approaches but also indirectly affected by intermediate variables such as mental health problems (e.g., anxiety, depression, stress, body image issues) and physical health problems (e.g., vaginal bleeding, convulsions or fits, severe headaches with blurred vision, fever, and excessive weakness). Northern Uganda is a conflict-affected region, which is critical for this study due to its high poverty rates(\u003cspan\u003e14\u003c/span\u003e), cultural diversity, limited healthcare access, poor health outcomes, and the presence of conflict-affected individuals(\u003cspan\u003e15\u003c/span\u003e).\u003c/p\u003e\n \u003cp\u003eFurthermore, the conceptual framework is based on two models to understand the indirect impact on maternal health. These two models, i.e., the Health Belief Model (HBM) (\u003cspan\u003e16\u003c/span\u003e) and Andersen\u0026rsquo;s Behavioral Model (\u003cspan\u003e17\u003c/span\u003e, \u003cspan\u003e18\u003c/span\u003e), will support in investigating the sociocognitive factors that may mediate the relationship between the implementation of ttC and changes in the maternal health continuum of care outcomes.\u003c/p\u003e\n \u003cp\u003eThe adaptations to this conceptual framework based on the 2 models above are premised on the evolution of the health promotion and disease prevention models that have undergone changes over time. There has been a shift from fear-based approaches to reward-based and self-regulatory strategies, which has highlighted the reciprocal interplay between individual capabilities, motivation, and environmental determinants of health behavior(\u003cspan\u003e19\u003c/span\u003e). However, interventions targeting health-related behaviors have often yielded modest effects, indicating the need to draw upon a wider range of theories that incorporate social, cultural, and economic factors(\u003cspan\u003e20\u003c/span\u003e). Therefore, our study strategically incorporates the HBM and Andersen\u0026rsquo;s behavioural model, and this deliberate integration aims to investigate whether emphasizing the factors outlined in these models within the community application of the ttC model will result in more effective outcomes, specifically contributing to the enhancement of overall maternal health in the implementation area.\u003c/p\u003e\n \u003cdiv id=\"Sec3\"\u003e\n \u003ch2\u003e1.1.2 Health Belief Model\u003c/h2\u003e\n \u003cp\u003eThe HBM considers factors such as perceived susceptibility, severity, benefits, barriers, and cues to action to predict an individual\u0026rsquo;s health behaviors(\u003cspan\u003e16\u003c/span\u003e). In the context of this maternal health study, this translates to how these mothers perceive their susceptibility to maternal health complications/issues, the severity of such issues, the benefits of engaging in recommended health behaviours (such as those promoted during the ttC), the barriers these women perceive and the cues that prompt them to action.\u003c/p\u003e\n \u003cp\u003eWhile the HBM provides valuable insights into individual beliefs and decision-making processes, it has theoretical limitations, such as undefined variable ordering and limited practical application (\u003cspan\u003e21\u003c/span\u003e). Based on these limitations, Anderson\u0026rsquo;s behavioral model has also been included in the conceptual framework to enhance the robustness of the research.\u003c/p\u003e\n \u003c/div\u003e\n \u003cdiv id=\"Sec4\"\u003e\n \u003ch2\u003e1.1.3 Andersen\u0026rsquo;s Behavioral Model\u003c/h2\u003e\n \u003cp\u003eAndersen\u0026apos;s behavioural model complements the HBM by considering broader factors such as individuals\u0026rsquo; personal characteristics and beliefs (predisposing factors), available resources and opportunities (enabling factors), and perceived needs and motivations (need factors). In the current study, this model helps us to understand how individual characteristics, access to resources and perceived needs shape healthcare utilization and contributes to our understanding of the contextual factors influencing maternal health to assess access inequality and inform policy development for equitable access to care (\u003cspan\u003e17\u003c/span\u003e, \u003cspan\u003e18\u003c/span\u003e). HBM focuses on the individual\u0026rsquo;s cognitive factors, and Andersen\u0026rsquo;s behavioral model focuses on the individual\u0026rsquo;s characteristics. By reinforcing the HBM with Andersen\u0026apos;s behavioral model, the study will thus leverage the strengths of both models, providing a better picture of the interplay among these psychological and belief dimensions. This will lead to a better and more effective ttC model in the unique sociocultural and economic context of Northern Uganda.\u003c/p\u003e\n \u003c/div\u003e\n \u003cdiv id=\"Sec5\"\u003e\n \u003ch2\u003e1.1.1 General Objective\u003c/h2\u003e\n \u003cp\u003eThe main objective of the study will be to assess the socioeconomic impact of ttC on the maternal health continuum of care outcomes in Northern Uganda.\u003c/p\u003e\n \u003cdiv id=\"Sec6\"\u003e\n \u003ch2\u003e1.1.1.1 Specific objectives\u003c/h2\u003e\n \u003cp\u003e\u003cspan\u003e1. To investigate the effect of ttC on ANC utilization among pregnant mothers.\u003cbr\u003e\u003c/span\u003e\u003cspan\u003e2. To evaluate the influence of ttC implementation on the place of delivery among pregnant women.\u003cbr\u003e\u003c/span\u003e\u003cspan\u003e3. To assess the impact of ttC on PNC utilization among mothers.\u003cbr\u003e\u003c/span\u003e\u003c/p\u003e\n \u003c/div\u003e\n \u003c/div\u003e\n \u003cdiv id=\"Sec7\"\u003e\n \u003ch2\u003e1.1.2 Hypotheses\u003c/h2\u003e\n \u003cp\u003eThe study will be guided by the following hypothesis.\u003c/p\u003e\n \u003cp\u003e\u003cspan\u003e1. ttC implementation will lead to an increase in the utilization of ANC among pregnant women.\u003cbr\u003e\u003c/span\u003e\u003cspan\u003e2. ttC will result in a greater proportion of pregnant women delivering at health facilities.\u003cbr\u003e\u003c/span\u003e\u003cspan\u003e3. ttC will lead to increased PNC utilization among mothers.\u003cbr\u003e\u003c/span\u003e\u003c/p\u003e\n \u003c/div\u003e\n \u003cdiv id=\"Sec8\"\u003e\n \u003ch2\u003e1.1.3 Research questions to be examined\u003c/h2\u003e\n \u003cul\u003e\n \u003cli\u003e\n \u003cp\u003eHow do demographic factors, CHW interactions, male involvement, and ttC directly impact the maternal health continuum of care outcomes in Northern Uganda?\u003c/p\u003e\n \u003c/li\u003e\n \u003cli\u003e\n \u003cp\u003eDoes emphasizing the factors outlined in the HBM and Andersen\u0026rsquo;s behavioural model within the ttC model result in an effective maternal health continuum of care outcomes in Northern Uganda?\u003c/p\u003e\n \u003c/li\u003e\n \u003c/ul\u003e\n \u003c/div\u003e\n\u003c/div\u003e"},{"header":"Methods","content":"\u003cdiv id=\"Sec10\" class=\"Section2\"\u003e \u003ch2\u003e2.1 Research Design\u003c/h2\u003e \u003cp\u003eA cross-sectional quasiexperimental research design with retrospective data collection comparing the intervention group to the control group will be used. The intervention group will receive ttC, and the control group will not receive ttC. To control for potential confounding factors, propensity score matching (PSM) will be used as the preferred method to improve causal inference. PSM will match each treated unit with a nontreated unit of similar characteristics. The preferred characteristics for matching in this study were education level, birth parity, income and age of the participants(\u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e22\u003c/span\u003e).\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec11\" class=\"Section2\"\u003e \u003ch2\u003e2.2 Research Area\u003c/h2\u003e \u003cp\u003eThe study area will be the northern region of Uganda, specifically the Oyam district, which was selected because ttC is implemented in the Aber subcounty of the district. Northern Uganda in particular is an important region for studying maternal health due to the high need for maternal health interventions, limited access to healthcare services, cultural diversity, high poverty rates (\u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e14\u003c/span\u003e), and the relevance for conflict-affected populations (\u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e15\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eTaking the Oyam district as the sampling frame, two subcounties, Aber and Otwal, were chosen for reaching out to mothers who would have given birth within the past 2\u0026ndash;12 months. Aber is the treatment subcounty, and Otwal is the control subcounty because ttC is implemented in the former but not in the latter. Significantly, both subcounties were chosen because they do not share borders with each other; thus, limited spillovers are expected from the treatment subcounty to the control subcounty.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec12\" class=\"Section2\"\u003e \u003ch2\u003e2.3 ttC intervention\u003c/h2\u003e \u003cp\u003eBefore implementation of the ttC approach in the communities, the CHWs were first trained with the ttC package, using a developed standardized manual on what to do and what targeted messages should be given to the household and the right timing for each. They were then grouped and supervised administratively by district health officials. CHWs then identified households with pregnant women or likely households with reproductive-aged women and encouraged them to register and notify the CHW as soon as a pregnancy case popped up.\u003c/p\u003e \u003cp\u003eWhen a pregnancy is identified by the CHW, they visit these pregnant households in a timely manner during the pregnancy and postnatally with targeted messages to encourage the pregnant women to attend ANC services and plan to give birth in the presence of skilled personnel and attend to PNC. They further encourage male caregivers to be involved in the whole cycle from pregnancy through birth to nurturing the baby by providing both emotional and logistical support. CHWs make four visits during pregnancy before birth and seven visits postnatally in the first 2 years after birth, as explained in Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e, which is adapted from the ttC manual(\u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e13\u003c/span\u003e), and in an earlier study on the effects of implementing the ttC model on pregnancy outcomes and newborn survival in rural Uganda(\u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e23\u003c/span\u003e). Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e below summarizes and provides a concise and visually organized reference for the key components and objectives of the ttC approach.\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab1\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 1\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eThe overview of key components of the ttC approach.\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"2\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eTiming (outcome focused on)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eTargeted Counselling given by CHW\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eBetween 2\u0026ndash;3 months of pregnancy \u003cb\u003e(ANC)\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eThe CHW encourage the pregnant woman to go for ANC within the first trimester and identify the home care variability in the household that might be needed by the pregnant woman.\u003c/p\u003e \u003cp\u003eThe CHWs encourage male caregivers to accompany the women for ANCs.\u003c/p\u003e \u003cp\u003eThey encourage good nutrition in pregnancy, uptake of iron/folic acid tablets, one extra meal a day for the pregnant woman, handwashing with soap, and having 4 or more ANCs\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eBetween 4\u0026ndash;5 months of pregnancy \u003cb\u003e(ANC)\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eThis visit is more focused on the disease burden especially on HIV and AIDs, Tuberculosis and other STIs testing and prevention awareness.\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eBetween 6\u0026ndash;7 months of pregnancy \u003cb\u003e(ANC, Skilled Birth Attendance)\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eThis is meant to promote health facility delivery, assisting the family in drawing up a birth plan, including having skilled personnel available during birth and talk about available family planning measures to encourage 2-year period child spacing.\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eBetween the 8\u0026ndash;9 months of pregnancy \u003cb\u003e(ANC, Skilled Birth Attendance)\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eThis is to review the birth plan and reiterate the importance of essential newborn care practices. The CHW also encourages exclusive breastfeeding and timely seeking for danger signs.\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eDuring the 1st week of life postnatal \u003cb\u003e(PNC)\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eThe CHW encourages essential maternal and neonatal care, access to postnatal, and postpartum care and timely seeking for danger signs.\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e1 month postnatal \u003cb\u003e(PNC)\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eGrowth monitoring, encouraging vaccination against preventable diseases and care seeking in case of fever.\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAfter the 5th month of life \u003cb\u003e(PNC)\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eCHW encourages complementary feeding and the importance of a balanced diet for the baby between the 6th to the 9th month.\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAt the 9th month of life \u003cb\u003e(PNC)\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eCHW encourages breast feeding along with complementary foods between the 9th to the 12th month and encourages uptake of micronutrients such as vitamin A supplements.\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e10th Visit \u003cb\u003e(PNC)\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eEncouraging iron rich foods, vitamin A supplements, deworming, holistic child development\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e11th Visit \u003cb\u003e(PNC)\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eGiving three to four meals to the child a day, child sleeping under a mosquito bednet.\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003ctfoot\u003e \u003ctr\u003e\u003ctd colspan=\"2\"\u003e\u003cem\u003eNote: CHW\u0026thinsp;=\u0026thinsp;Community Health Worker, ANC\u0026thinsp;=\u0026thinsp;Antenatal Care, HIV\u0026thinsp;=\u0026thinsp;Human Immunodeficiency Virus, AIDS\u0026thinsp;=\u0026thinsp;Acquired Immunodeficiency Syndrome, STIs\u0026thinsp;=\u0026thinsp;Sexually Transmitted Infections, PNC\u0026thinsp;=\u0026thinsp;Postnatal Care, ttC\u0026thinsp;=\u0026thinsp;Timed and Targeted Counselling\u003c/em\u003e\u003c/td\u003e\u003c/tr\u003e \u003c/tfoot\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec13\" class=\"Section2\"\u003e \u003ch2\u003e2.4 Study population\u003c/h2\u003e \u003cp\u003eThe study population/sampling frame is composed of respondents at the household level who will be selected from 2 subcounties, one with mothers who have given birth between 2\u0026ndash;12 months of age and who participated in the ttC model sessions (with 876 mothers in the treatment group forming part of the sampling frame) or mothers whose access to ttC sessions was limited to the other subcounty of interest (with 1355 mothers in the control group also forming part of the sampling frame). Thus, the total sampling frame/study population will have 2231 mothers from which the sample will be drawn.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec14\" class=\"Section2\"\u003e \u003ch2\u003e2.5 Study Instruments\u003c/h2\u003e \u003cp\u003eThe study will use a cross-sectional primary data source. Structured household questionnaires will be administered to the study participants by the research assistants to gather quantitative data. The questionnaire included different data types, such as categorical, numerical, and scale options, to facilitate the quantification of information. For validity and reliability, the questionnaire will have embedded skips and rules to ease data collection and perform real-time data cleaning. The study tools/instruments to be used will also include the CHW household registers that register all the mothers and the ttC register that includes the targeted sessions and number of visits. These will provide the contact and location from which participants will be sampled and the data collected retrospectively.\u003c/p\u003e \u003cp\u003eTools will be uploaded to Kobo Kollect, a data collection application that will be loaded on phone devices and administered. The Kobo loaded questionnaire will also have mandatory questions marked for emphasis and will have an embedded translation into the local language to ease data collection. A field pretest of the structured questionnaire will be performed before the final roll out.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec15\" class=\"Section2\"\u003e \u003ch2\u003e2.6 Data Management\u003c/h2\u003e \u003cp\u003eTo ensure the integrity and accuracy of the collected data, a robust data management strategy will be implemented. A team of 25 research assistants will be trained and given pre- and posttraining tests to test their knowledge with regard to the topic of study and data collection and management processes. The data will be checked for errors at the end of every day, and the research assistant team will meet daily after fieldwork to synchronize their data to a secure server to not only act as a backup but also to allow the principal investigator to check for validity and accuracy of the tools using the GPS on the Kobo tools loaded on the phones and the time stamps on the tools showing the start and end time for each tool. There will also be ad hoc checks in the field to see that the data are not being manipulated into the system but are actual data. The data collectors will also be given a unique identifier to enhance the traceability of each tool to individual entrants. Inaccurate information will therefore be easily traced to the research assistant who submitted it to the system, the records will be deleted, and the researcher will stop contributing to the data collection.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec16\" class=\"Section2\"\u003e \u003ch2\u003e2.7 Sample size calculation\u003c/h2\u003e \u003cp\u003eThe sample size will be estimated using power analysis from a two-sided Z test adopted from Charan and Biswas (\u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e24\u003c/span\u003e)with a 1.5 design effect and a 10% nonresponse rate(\u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e25\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eThis will take into account the desired level of statistical significance, effect size, and expected variability.\u003c/p\u003e \u003cp\u003eThe formula is as follows:\u003cdiv id=\"Equa\" class=\"Equation\"\u003e\u003cdiv format=\"TEX\" class=\"mathdisplay\" id=\"FileID_Equa\" name=\"EquationSource\"\u003e\n$$n=\\frac{2({Z}_{\\alpha /2}+{Z}_{\\beta }{)}^{2}P(1-P)}{({P}_{1}-{P}_{2}{)}^{2}}$$\u003c/div\u003e\u003c/div\u003e\u003c/p\u003e \u003cp\u003ewhere:\u003c/p\u003e \u003cp\u003e \u003cspan class=\"InlineEquation\"\u003e \u003cspan class=\"mathinline\"\u003e\\({Z}_{\\alpha /2}\\)\u003c/span\u003e \u003c/span\u003e = \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\({Z}_{0.05/2}\\)\u003c/span\u003e\u003c/span\u003e = \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\({Z}_{0.025}\\)\u003c/span\u003e\u003c/span\u003e =1.96 is the critical value (from the Z table) at type 1 error, which is the critical value for the chosen level of significance\u003c/p\u003e \u003cp\u003e \u003cspan class=\"InlineEquation\"\u003e \u003cspan class=\"mathinline\"\u003e\\({Z}_{\\beta }\\)\u003c/span\u003e \u003c/span\u003e =\u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\({Z}_{0.20}\\)\u003c/span\u003e\u003c/span\u003e = 0.842 is the critical value for the chosen power from the standard normal distribution at 80%\u003c/p\u003e \u003cp\u003e \u003cspan class=\"InlineEquation\"\u003e \u003cspan class=\"mathinline\"\u003e\\({P}_{1}\\)\u003c/span\u003e \u003c/span\u003e= 71.5% is the estimated proportion of the population with the outcome of interest in the control group. This figure is derived by averaging the proportions observed in two previously related studies (\u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e23\u003c/span\u003e, \u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e25\u003c/span\u003e)examining one of the outcomes of interest, facility-based deliveries without intervention.\u003c/p\u003e \u003cp\u003e \u003cspan class=\"InlineEquation\"\u003e \u003cspan class=\"mathinline\"\u003e\\({P}_{2}\\)\u003c/span\u003e \u003c/span\u003e = 81.5% is the estimated proportion of the population with the outcome of interest in the intervention group. Like P1, this is also obtained by averaging proportions from the same two related studies(\u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e23\u003c/span\u003e, \u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e25\u003c/span\u003e), but in this case, focusing on facility-based delivery with intervention.\u003c/p\u003e \u003cp\u003e \u003cspan class=\"InlineEquation\"\u003e \u003cspan class=\"mathinline\"\u003e\\(P\\)\u003c/span\u003e \u003c/span\u003e= 76.5% is the pooled prevalence [prevalence in case group \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\({(P}_{1})\\)\u003c/span\u003e\u003c/span\u003e + prevalence in control group\u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\({ (P}_{2})\\)\u003c/span\u003e\u003c/span\u003e]/2\u003c/p\u003e \u003cp\u003e \u003cspan class=\"InlineEquation\"\u003e \u003cspan class=\"mathinline\"\u003e\\({P}_{1}-{P}_{2}\\)\u003c/span\u003e \u003c/span\u003e= -10% is the difference in the proportion of events in the two groups.\u003c/p\u003e \u003cp\u003eThe sample size calculated using the power analysis and taking into consideration the design effect and nonresponse yielded 465 participants in each of the groups (control (465) and treatment (465)) at 80% power and at the 5% level of significance.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec17\" class=\"Section2\"\u003e \u003ch2\u003e2.8 Sampling procedure\u003c/h2\u003e \u003cp\u003eTo reach the above calculated sample size, a two-stage sampling procedure will be used. The first stage will be purposive sampling of Oyam District in Northern Uganda due to the ttC implemented within some subcounties in the district, its poor health outcomes and limited access to health care, which all make it an ideal location for the study.\u003c/p\u003e \u003cp\u003eThe second stage will involve stratified sampling, where the 2 strata subcounties of Aber and Otwal will provide the sampling unit for women of reproductive age (between 18 and 49 years) who have given birth between 2 and 12 months of age. The subcounties in Oyam District will be stratified based on whether the treatment (ttC implementation) was applied (Aber Subcounty) or not (Otwal Subcounty). Stratified sampling will be carried out within these subcounties to ensure a representative sample for later generalization of findings to other similar settings. The participants will be randomly selected from each stratum to allow for precise estimates within subgroups and improve the overall representativeness of the sample. The study aims to reach a total of 930 mothers (from the control (465) and treatment (465)) as per the sample size calculation above. The flow of this sampling process is shown in Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e2\u003c/span\u003e below.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003eBy justifying and implementing this comprehensive sampling methodology, the study will aim to enhance the reliability and validity of the research findings.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec18\" class=\"Section2\"\u003e \u003ch2\u003e2.9 Study variables\u003c/h2\u003e \u003cp\u003eThe primary outcome variables for the study of the impact of ttC on the maternal health continuum of care will be ANC utilization, skilled birth attendance and PNC. The ttC variable is nominally defined by whether the household received ttC.\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab2\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 2\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eStudy variables and their definitions.\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"3\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eNo\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eVariable\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eDefinition of Variable\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003ctr\u003e \u003cth align=\"left\" colspan=\"3\" nameend=\"c3\" namest=\"c1\"\u003e \u003cp\u003eDependent Variables\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eANC\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eThis is defined as visits to a skilled health personnel during pregnancy.\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003ePlace of delivery\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eThis refers to the location where a pregnant woman gives birth, such as at the health facility or at home.\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003ePNC\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eThis refers to the care given to the mother immediately after birth. This should be atleast one Post-Natal Care visit within the first 6 weeks after birth.\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c3\" namest=\"c2\"\u003e \u003cp\u003e\u003cb\u003eIndependent Variables\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eCHW\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eThese are trained personnel that provide household level health education to reproductive age women, pregnant women, new mothers together with their male caregivers. These Community Health Workers interaction with pregnancy women and their male caregivers is assessed quantitatively by categorical and scalar answer options.\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eMale Involvement\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eThis is defined by male caregivers actively supporting their families and caring for their women in order to access better health services. This is assessed with the partner attending counselling sessions, accompanying pregnant mother for Antenatal Care, and Post-Natal Care and providing both emotional and financial support using categorical and scalar answer options to questions.\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eSocio-Demographic factors\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eThis refers to the social and demographic characteristics of the population and how they influence maternal health that is age, education level, parity and marital status of the mothers assessed using numeric, scalar and categorical options to the questions.\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eTTC\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eThis is a behavioral change methodology that targets pregnant women with a wide range of life-saving health practices through appropriately timed messages delivered using interactive storytelling. This will either be implemented in the treatment subcounty or not, in the control subcounty for the study and will be assessed using a categorical question of whether it was received or not.\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c3\" namest=\"c2\"\u003e \u003cp\u003e\u003cb\u003eIntermediate Variables\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eHealth Belief Model\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eThis model focuses on individual beliefs and attitudes towards maternal health such as how the mothers perceive their susceptibility to maternal health complications/issues, the severity of such issues, the benefits of engaging in recommended health behaviours (such as those promoted during the counselling), the barriers these women perceive and the cues that prompt them to action. This will be assessed using scalar and categorical answer options to the questions.\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eAndersen\u0026rsquo;s Behavioral Model\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eThis model highlights the determinants of health care utilization such as predisposing, enabling and need factors. This model thus helps us to understand how mother\u0026rsquo;s individual characteristics, access to resources and perceived needs shape their healthcare utilization. This will be assessed using categorical and scalar options to the questions.\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003ctfoot\u003e \u003ctr\u003e\u003ctd colspan=\"3\"\u003e\u003cem\u003eNote: ANC\u0026thinsp;=\u0026thinsp;antenatal care, PNC\u0026thinsp;=\u0026thinsp;postnatal care, ttC\u0026thinsp;=\u0026thinsp;timed and targeted counselling, CHW\u0026thinsp;=\u0026thinsp;community health worker.\u003c/em\u003e\u003c/td\u003e\u003c/tr\u003e \u003c/tfoot\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec19\" class=\"Section2\"\u003e \u003ch2\u003e2.10 Data analysis\u003c/h2\u003e \u003cdiv id=\"Sec20\" class=\"Section3\"\u003e \u003ch2\u003e2.10.1 Univariate Analysis\u003c/h2\u003e \u003cp\u003eAfter the data have been cleaned, they will be exported to STATA for analysis and Excel for better visualization. At the univariate level, descriptive and summary statistics will be drawn using the frequencies, measures of central tendency and dispersion with proportions to provide a descriptive analysis of the context in relation to each of the factors, such as demographics and behavioral characteristics.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec21\" class=\"Section3\"\u003e \u003ch2\u003e2.10.2 Bivariate analysis\u003c/h2\u003e \u003cp\u003eAt the bivariate level, propensity score matching will then be used to determine the effect of ttC on ANC, PNC, and place of delivery. The data will be used to estimate the propensity scores by logistic regression using a logit model to remove the effects of confounding factors by comparing outcomes between treated and control participants who share a similar propensity score(\u003cspan citationid=\"CR26\" class=\"CitationRef\"\u003e26\u003c/span\u003e). The participants will then be matched using the estimated scores. The greedy nearest neighbor method will be used to implement pair matching(\u003cspan citationid=\"CR26\" class=\"CitationRef\"\u003e26\u003c/span\u003e). Furthermore, a calliper restriction with a width equal to 0.2(\u003cspan citationid=\"CR27\" class=\"CitationRef\"\u003e27\u003c/span\u003e) of the logit of the propensity score will be used. After matching, the standardized mean difference (SMD) of 0.1 will be used to measure the balance between the covariates after matching(\u003cspan citationid=\"CR28\" class=\"CitationRef\"\u003e28\u003c/span\u003e) Finally, after the selection of the covariates above 0.1 SMD, a logistic regression will then be rerun to study the impact of ttC on ANC, PNC and place of delivery while controlling for other variables. Furthermore, sensitivity analysis will be performed to test for the robustness of the results to the unmeasured confounders in PSM.\u003c/p\u003e \u003cp\u003eThe Pearson chi-square test will be carried out to compare maternal outcomes between the control and case groups. A test for multicollinearity will be performed to check the variance information factor between two or more predictor variables to ensure statistical significance in the regressions.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec22\" class=\"Section3\"\u003e \u003ch2\u003e2.10.3 Multivariate Analysis\u003c/h2\u003e \u003cp\u003eAt the multivariate level, logistic regression will be used to describe the data. It is appropriate to use logistic regression for predictive analysis whenever the dependent variable is binary. This will be used to explain the relationship between the variables (\u003cspan citationid=\"CR29\" class=\"CitationRef\"\u003e29\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eAfter model fitting, postestimation tests such as the Akaike information criterion (AIC) will be performed to test for goodness of fit (\u003cspan citationid=\"CR30\" class=\"CitationRef\"\u003e30\u003c/span\u003e). The link test will also be used to test for goodness of fit. The Wald test will be used to test the magnitude of the causal inference and test for the significance of the coefficients, whether to retain them or not.\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e"},{"header":"Discussion","content":"\u003cp\u003eThis study protocol describes how the current study will evaluate the impact of the ttC model on the maternal health continuum of care outcomes in Northern Uganda using a cross-sectional quasiexperimental design and employing propensity score matching to control for selection bias.\u003c/p\u003e \u003cp\u003eThis study will integrate the HBM, which emphasizes individuals' decisions on health behaviors based on perceived threat, benefits, barriers, and self-efficacy(\u003cspan citationid=\"CR31\" class=\"CitationRef\"\u003e31\u003c/span\u003e), with Andersen's behavioral model, which identifies predisposing, enabling, need, reinforcing, and health-care utilization factors influencing health behavior(\u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e18\u003c/span\u003e), to obtain a comprehensive understanding of whether emphasizing these factors within the ttC model will result in a more effective maternal health continuum of care outcomes.\u003c/p\u003e \u003cp\u003eNorthern Uganda is a critical region for this study due to its high poverty rates(\u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e13\u003c/span\u003e), cultural diversity, limited healthcare access, poor health outcomes, and presence of conflict-affected individuals(\u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e14\u003c/span\u003e). This research will generate new and original evidence on the impact of male involvement, demographic factors, and CHWs on the overall maternal health continuum of care outcomes. The results will inform resource allocation and target the most effective factors to promote healthy behaviors.\u003c/p\u003e \u003cdiv id=\"Sec24\" class=\"Section2\"\u003e \u003ch2\u003e3.1 Strengths and limitations\u003c/h2\u003e \u003cp\u003eThis research addresses a global challenge of maternal health and how a behavioural change communication model, ttC, could impact maternal mortality. Propensity score matching to remove confounding factors allows the exploration of relationships between various variables and maternal health. Having northern Uganda as the area of study gives more focus to conflict-affected regions whose results could be adapted to other similar settings.\u003c/p\u003e \u003cp\u003eHowever, there may be response bias due to self-reporting, which may affect the information gathered. This limitation will be addressed by providing confidentiality assurance to the study participants to allow them to answer more truthfully.\u003c/p\u003e \u003cp\u003eThis is not a randomized controlled trial, and data are being collected retrospectively, postnatal. This means that there is no control over the quality of the training to the VHTs, the counselling given to the mothers or the quality of information stored in the birth cards, the ttC registers or the VHT registers. To mitigate this challenge, the researcher will emphasize in the training to research assistants that a birth must be ascertained through tracing the records at the health facilities and triangulating with the CHW registers and birth cards in possession of the mothers to have more reliable and accurate study data collected.\u003c/p\u003e \u003c/div\u003e"},{"header":"Abbreviations","content":"\u003cp\u003eAIC: Akaike Information Criterion; AIDs: Acquired Immunodeficiency Syndrome; ANC: Antenatal Care; CHW: Community Health Worker; HBM: Health Belief Model; HIV: Human Immunodeficiency Virus; IRB: Institutional Review Board; NNM: Greedy Nearest Neighbor; PNC: Postnatal Care; PSM: Propensity Score Matching; SMD: Standardized Mean Difference; TB: Tuberculosis; ttC: Timed and Targeted Counselling; UNCST: Uganda National Council for Science and Technology; VIF: Variance Inflation Factor\u003c/p\u003e"},{"header":"Declarations","content":"\u003cp\u003e\u003cstrong\u003eAvailability of data and materials\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eNot applicable\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eEthics approval and consent to participate\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThis\u0026nbsp;study is being conducted in Northern Uganda, considering cultural and social aspects. Ethical\u0026nbsp;approval\u0026nbsp;was obtained from the Institutional Review Board (IRB) at Makerere University School of Social Sciences (MAKSSREC 10.2023.710),\u0026nbsp;and\u0026nbsp;the study was\u0026nbsp;registered under\u0026nbsp;the\u0026nbsp;Uganda National Council for Science and Technology (UNCST HS3826ES) to safeguard participants.\u0026nbsp;The consent\u0026nbsp;procedures will be carried out in the local language by trained field team members using translated consent forms for those with limited education. The field team will undergo extensive training on research ethics, consent procedures, interviewer techniques, and instrument usage. The principal investigator will lead the field team, composed of locally recruited individuals fluent in the local language with prior experience in data collection among similar populations. The study will not involve special populations, and data analysis will be conducted in-country.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eConsent for publication\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eNot Applicable\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAvailability of data and materials\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eNot Applicable\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eCompeting interests\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe authors declare that they have no competing interests.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eFunding\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThis study is being supported by the Department of Health Promotion, School of Nutrition and Translational Research in Metabolism (NUTRIM), Maastricht University. It is crucial to note that the content presented in this study is the sole responsibility of the authors and not the\u0026nbsp;university.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAuthors\u0026apos; contributions\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eZD\u0026nbsp;conceptualized, drafted, and revised the manuscript. SG, JBA,\u0026nbsp;and\u0026nbsp;JG helped draft\u0026nbsp;the manuscript\u0026nbsp;and provided valuable feedback for revision,\u0026nbsp;including editing the manuscript. All the authors have read and approved the final manuscript.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAcknowledgements\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eSpecial\u0026nbsp;acknowledgement\u0026nbsp;is given\u0026nbsp;to the\u0026nbsp;biostatistician\u0026nbsp;of Oyam District,\u0026nbsp;Sara Awor,\u0026nbsp;for\u0026nbsp;providing\u0026nbsp;the district demographic statistics on births.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAuthor Details\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003e\u0026nbsp;Department of Health Promotion, School of Nutrition and Translational Research in Metabolism (NUTRIM), Maastricht University, 6211 MD Maastricht, The Netherlands. School of Statistics \u0026amp; Planning, Makerere University, Kampala, Uganda.\u003c/p\u003e\n\u003cp\u003e\u0026nbsp;\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\n\u003cli\u003eMaternal health [Internet]. [cited 2024 Mar 27]. 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Factors associated with health facility deliveries among mothers living in hospital catchment areas in Rukungiri and Kanungu districts, Uganda. BMC Pregnancy Childbirth. 2021 Apr 26;21(1):329. \u003c/li\u003e\n\u003cli\u003eP N, Nk N, D S. Determinants of early postnatal care attendance: analysis of the 2016 Uganda demographic and health survey. BMC Pregnancy Childbirth [Internet]. 2020 Mar 16 [cited 2023 Feb 4];20(1). Available from: https://pubmed.ncbi.nlm.nih.gov/32178635/\u003c/li\u003e\n\u003cli\u003eWudineh KG, Nigusie AA, Gesese SS, Tesu AA, Beyene FY. Postnatal care service utilization and associated factors among women who gave birth in Debretabour town, North West Ethiopia: a community- based cross-sectional study. BMC Pregnancy Childbirth. 2018 Dec 27;18(1):508. \u003c/li\u003e\n\u003cli\u003eTimed and Targeted Counselling (TTC) [Internet]. [cited 2024 Mar 22]. Available from: https://www.wvi.org/health/timed-and-targeted-counseling-ttc\u003c/li\u003e\n\u003cli\u003eubosadmin. Release of the Multi Poverty Dimensional Index Report 2022 [Internet]. Uganda Bureau of Statistics. 2022 [cited 2024 Mar 22]. Available from: https://www.ubos.org/release-of-the-multi-poverty-dimensional-index-report-2022/\u003c/li\u003e\n\u003cli\u003eRoberts B, Odong VN, Browne J, Ocaka KF, Geissler W, Sondorp E. An exploration of social determinants of health amongst internally displaced persons in northern Uganda. Confl Health. 2009 Dec 15;3:10. \u003c/li\u003e\n\u003cli\u003eKirscht JP, Haefner DP, Kegeles SS, Rosenstock IM. A National Study of Health Beliefs. J Health Hum Behav. 1966;7(4):248\u0026ndash;54. \u003c/li\u003e\n\u003cli\u003eTravers JL, Hirschman KB, Naylor MD. Adapting Andersen\u0026rsquo;s expanded behavioral model of health services use to include older adults receiving long-term services and supports. BMC Geriatr. 2020 Feb 14;20(1):58. \u003c/li\u003e\n\u003cli\u003eAndersen RM. Revisiting the Behavioral Model and Access to Medical Care: Does it Matter? J Health Soc Behav. 1995;36(1):1\u0026ndash;10. \u003c/li\u003e\n\u003cli\u003eBandura A. Health promotion from the perspective of social cognitive theory. Psychol Health. 1998 Jul 1;13(4):623\u0026ndash;49. \u003c/li\u003e\n\u003cli\u003eDavis R, Campbell R, Hildon Z, Hobbs L, Michie S. Theories of behaviour and behaviour change across the social and behavioural sciences: a scoping review. Health Psychol Rev. 2015 Aug 7;9(3):323\u0026ndash;44. \u003c/li\u003e\n\u003cli\u003eJones CL, Jensen JD, Scherr CL, Brown NR, Christy K, Weaver J. The Health Belief Model as an Explanatory Framework in Communication Research: Exploring Parallel, Serial, and Moderated Mediation. Health Commun. 2015;30(6):566\u0026ndash;76. \u003c/li\u003e\n\u003cli\u003eStephanie. Statistics How To. 2017 [cited 2023 Mar 18]. Propensity Score Matching: Definition \u0026amp; Overview. Available from: https://www.statisticshowto.com/propensity-score-matching/\u003c/li\u003e\n\u003cli\u003eBabughirana G, Gerards S, Mokori A, Charles Baigereza I, Baba Magala A, Kwikiriza R, et al. Effects of timed and targeted counselling by community health workers on maternal and household practices, and pregnancy and newborn outcomes in rural Uganda. Sex Reprod Healthc. 2023 Jun 1;36:100845. \u003c/li\u003e\n\u003cli\u003eCharan J, Biswas T. How to Calculate Sample Size for Different Study Designs in Medical Research? Indian J Psychol Med. 2013;35(2):121\u0026ndash;6. \u003c/li\u003e\n\u003cli\u003eEkirapa-Kiracho E, Muhumuza Kananura R, Tetui M, Namazzi G, Mutebi A, George A, et al. Effect of a participatory multisectoral maternal and newborn intervention on maternal health service utilization and newborn care practices: a quasi-experimental study in three rural Ugandan districts. Glob Health Action. 2017 Sep 5;10(sup4):1363506. \u003c/li\u003e\n\u003cli\u003eAustin PC, Xin Yu AY, Vyas MV, Kapral MK. Applying Propensity Score Methods in Clinical Research in Neurology. Neurology. 2021 Nov 2;97(18):856\u0026ndash;63. \u003c/li\u003e\n\u003cli\u003eAustin PC. Optimal caliper widths for propensity-score matching when estimating differences in means and differences in proportions in observational studies. Pharm Stat. 2011 Mar;10(2):150\u0026ndash;61. \u003c/li\u003e\n\u003cli\u003eZhang Z, Kim HJ, Lonjon G, Zhu Y. Balance diagnostics after propensity score matching. Ann Transl Med. 2019 Jan;7(1):16. \u003c/li\u003e\n\u003cli\u003eStatistics Solutions [Internet]. [cited 2024 Mar 24]. What is Logistic Regression? Available from: https://www.statisticssolutions.com/free-resources/directory-of-statistical-analyses/what-is-logistic-regression/\u003c/li\u003e\n\u003cli\u003eBusemeyer JR, Diederich A. Chapter 4 - Estimation and Testing of Computational Psychological Models. In: Glimcher PW, Fehr E, editors. Neuroeconomics (Second Edition) [Internet]. San Diego: Academic Press; 2014 [cited 2024 Mar 22]. p. 49\u0026ndash;61. Available from: https://www.sciencedirect.com/science/article/pii/B9780124160088000048\u003c/li\u003e\n\u003cli\u003eAbraham C, Sheeran P. The Health Belief Model. In 2015. \u003c/li\u003e\n\u003c/ol\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":true,"highlight":"","institution":"","isAcceptedByJournal":false,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true},"keywords":"Timed and Targeted Counselling, Community Health Workers, Antenatal Care, Postnatal Care, Maternal Health","lastPublishedDoi":"10.21203/rs.3.rs-4177199/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-4177199/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003e\u003cstrong\u003eBackground\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eAbout 287,000 women died during and following pregnancy and childbirth in 2020 worldwide. Almost 95% of all these maternal deaths occurred in low and lower middle-income countries, and most could have been prevented. The timed and targeted counselling behavioural change approach, implemented by community health workers targeting mothers and their male caregivers (e.g., their husband, brother or father), is expected to positively impact overall maternal health. This study aims to assess the impact of timed and targeted counselling on the continuum of care outcomes in Northern Uganda.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eMethods\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThis study will employ a cross-sectional quasiexperimental design, with retrospective data collection comparing an intervention group to a control group. The main outcome measures are antenatal care, place of delivery and postnatal care. The study employs a two-stage sampling procedure purposively selecting the Oyam District, including two strata of subcounties: Aber (Treatment) and Otwal (Control). The required sample size consisted of 456 mothers per treatment group (i.e., 912 in total). The study participant selection criterion will be mothers who have given birth between the 2nd the 12th month of the study area. Propensity score matching will be used to control for confounders and improve causal inference. Sensitivity analysis will be carried out to test the robustness of the results to unmeasured confounders in the propensity score match. After regression, postmodel estimation tests such as the Akaike information criterion, the link test and the Wald test will be carried out.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eDiscussion\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThis study is the first to evaluate the impact of timed and targeted counselling on maternal health in Northern Uganda. These findings will be used to modify the implementation of the timed and targeted counselling approach, thereby enhancing its impact, efficiency, and effectiveness.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eProtocol Registration\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThis study protocol was registered under the Makerere University School of Social Sciences Research Ethics Committee (MAKS REC) under MAKSSREC 10.2023.710 (registration date 30th of November 2023) and the Uganda National Council for Science and Technology (UNCST HS3826ES).\u003c/p\u003e","manuscriptTitle":"Impact of the Timed and Targeted Counselling Model on Maternal Health Continuum of Care Outcomes in Northern Uganda: Protocol of a Quasi-Experimental Study","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2024-04-05 13:05:33","doi":"10.21203/rs.3.rs-4177199/v1","editorialEvents":[{"type":"communityComments","content":0}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"1c597886-7528-4917-9f36-ad7e92dd20b6","owner":[],"postedDate":"April 5th, 2024","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"posted","subjectAreas":[],"tags":[],"updatedAt":"2024-07-23T15:15:57+00:00","versionOfRecord":[],"versionCreatedAt":"2024-04-05 13:05:33","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-4177199","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-4177199","identity":"rs-4177199","version":["v1"]},"buildId":"qtupq5eGEP_6zYnWcrvyt","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}