Factors influencing the accessibility to maternal and child healthcare services: Case study of rural areas of northeast Bangladesh

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Hafiz Iqbal, Eshita Deb, Mitu Chowdhury, Modhumita Bhattachirjee Pia, and 1 more This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-6234069/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 Despite efforts at the government level, maternal and child healthcare (MCH) services continue to pose significant challenges in Bangladesh. However, limited empirical and country-specific research has been conducted to understand how health system elements impact the effectiveness of MCH services. This paper takes an initial step in identifying socio-demographic and infrastructural factors that influence MCH service accessibility in rural areas of northeast Bangladesh. Through in-depth interviews and logistic regression analysis, data from respondents were collected to explore the relative importance of various factors. Results showed that age, level of education, monthly income, and farm size are significant driving factors of MCH services. On the hand, availability of healthcare services, satisfaction with service providers, knowledge about maternal and child healthcare services, and availability of experienced and skill predictrics are significantly associated to MCH services. The model developed in the study provides a detailed understanding of the determinants of MCH and how they influence MCH outcomes. The findings of the study serves as a foundation for the ‘white box’ approach to theory-driven evaluation of MCH. Humanities/Health humanities Social science/Sociology Health seeking behavior Healthcare barriers Antenatal care Maternal public health Postnatal care Introduction Over the past three decades (from 1990 to 2020), neonatal mortality in Bangladesh has significantly decreased by half due to a progressive government initiative focused on pregnant women (Dutta et al., 2020 ). These women have benefited from an iron and folic acid (IFA) supplementation program supported by the government (Kurzawa et al., 2021). Additionally, advancements in medical science, improved healthcare facilities, widespread media coverage, pregnancy awareness campaigns, better availability of medicines, effective family planning practices, increased access to contraceptives, educational opportunities for girls, restrictions on early marriage, changing societal attitudes, and improved economic conditions have all contributed to positive outcomes in maternal mortality.Furthermore, the government and other development partners have actively promoted prenatal care, introduced health voucher schemes for disadvantaged women, established community-based skilled birth attendants, and implemented midwifery programs in service delivery (The Daily Star, 2014). Colloborative endeavors have been instrumental in attaining positive outcomes in MCH in Bangladesh. The Bangladesh government has initiated an awareness program for pregnant women, aiming to safeguard them from anemia, vitamin deficiencies, and inadequate nutrition. The program also emphasizes the importance of consuming recommended amounts of vitamin B, iron, and folic acid during pregnancy. Across various clinics, hospitals, and Upazila Health Complexes (the smallest administrative units in Bangladesh), the government has integrated nutrition, maternal, and newborn healthcare services into a comprehensive basic healthcare package (Hoque et al., 2018 ). As a result, the maternal mortality rate in Bangladesh has decreased by over 69 percent in the past two decades, surpassing India but remaining lower than Pakistan’s rate (WDI, 2017). Over the past decade, the infant mortality rate in Bangladesh has steadily decreased, declining from 35.5 deaths per 1,000 live births in 2011 to 22.9 in 2021 (Rahman et al., 2021). Despite this decline, rural (MCH) in Bangladesh still faces challenges due to inequitable geographic distribution of healthcare professionals, essential drugs, and medical equipment. Significant reductions Significant reduction in mortality have beem observed, particularly among children aged one month and older (Islam & Biswas, 2020). These improvements can be attributed to various factors, including enhanced postnatal care for newborns, better breastfeeding practices, prevention of neonatal tetanus, increased vaccination coverage for vitamin A supplementation, improved pneumonia management, and the use of oral rehydration solutions (ORS) to combat diarrhea. In 1975, the infant mortality rate was 145.2 per 1,000 live births, but by 2015, it had dropped to 30.7 (Iqbal, 2019). Bangladeshhas also witnessed a decline in child mortality among children under five years old. In 1975, the child mortality rate was 217.1 per 1,000 live births, whereas in 2015, it stood at 37.6 (WDI, 2017). Factors such as elimination of cultural obstacles, religious superstition, increased competent delivery attendance, awareness, and women’s literacy rates have played a crucial role in lowering infant mortality rates (Cheong & Baltazar, 2021 ; Mike & Muhammad, 2020). In rural Bangladesh, there are significant challenges related to equity, inclusion, human rights, access to healthcare services (Kemp et al., 2022). Informal and unregulated rural medical practioners (RMPs) play a crucial role in providing healthcare services to about two-thirds of the population. Despite being embedded in local communities and providing cost-effective services, RMPs are frequently perceived as offering substandard care by qualified healthcare providers (Iqbal, 2019). Issues observed in RMPs’ practice pattern include inappropriate diagnoses, irrational use of antibiotics and other drugs, and polypharmacy. Hospitals and physicians of metropolitan city face challenges such as less comprehensive healthcare facilities, greater distance from patients, and higher costs. The retention of competent medical practitioners disproportionately affects (MCH) services in rural areas. Even rural communities in northeast Bangladesh are not immune to these problems. Despite poverty and inequality, Bangladesh has achieved better-than-expected health outcomes. However, non-communicable diseases (NCDs) remain prevalent, especially among underprivileged communities like rural inhabitants. Rapid urbanization poses new challenges for healthcare workers, further dividing problems between urban and rural healthcare services are critical steps toward improving overall health outcomes in Bangladesh. According to Nachlis (2018), Sammut (2021), and Alkhamis ert al. (2021), the existing healthcare system is centralized, which can hinder efficient service delivery; inadequate governance structures and regulatory frameworks pose challenges to effective MCH services; within the relevant ministry, there is a lack of capacity for managing MCH service effectively; the delivery of MCH services is fragmented, leading to inefficiencies; socioeconomic challenges, particularly poverty, impact to MCH services; suboptimal allocation of resources affects MCH service provision; insufficient regulation of the private sector can improve MCH services; and a scarcity of qualified doctors and nurses further exacerbates the challenges in MCH service delivery. These findings imply that identifying influential factors can help to alleviate these problems and establish suitable healthcare policies for rural mothers and children. However, the following questions remain: what causes are to blame for the poor accessibility of MCH services in rural areas of northeast Bangladesh? What level of accessibility can be increased if appropriate changes to the variables are made? We still need to look at the elements that affect MCH service availability in Bangladesh's rural areas. As a result, there is a need to investigate the major factors affecting such patients' access to healthcare. The study explores these elements and provides required suggestions for mothers and their children in rural Bangladesh to sustain healthcare practices. Literature Review This section covers three categories, such as the challenges of equitable healthcare for all in rural areas, contributors to maternal and child healthcare, and policies. Below is a quick rundown of each of the strands. Challenges of equitable healthcare for all in rural Bangladesh Despite concerted efforts at global, regional, and national levels to enhance overall population health, significant hurdles persist in meeting the healthcare needs of those residing in rural and hard-to-reach areas (Doshmangir et al., 2019 ). In Bangladesh, healthcare facilities often lack essential services, medical equipment, and trained staff. Many rural areas struggle with inadequate transportation infrastructure, making it difficult for people to reach healthcare centers promptly. Even when services are available, utilization rates remain low due to various barriers, including cultural norms and lack of awareness. The coverage of healthcare services in rural regions falls short of meeting the population’s needs (Haque, Parr, & Muhidin, 2019 ; Alam et al., 2019 ; Haque, Parr, & Muhidin, 2020 ). Rural areas face a severe shortage of qualified doctors. The retention of medical professionals in remote health facilities is also problemetic. The scarcity of healthcare providers disproportionately affects mothers and children residing in rural areas. There is an overall shortage of experienced healthcare workers, leading to imbalances in skill mix (Liu et al. 2017). Healthcare providers tend to concentrate in urban areas, leaving rural regions underserved. Limited access to update medical knowledge and training hampers effective healthcare delivery. Challenging work conditions and lack of incentives discourage healthcare professionals from serving in rural settings. Adequate policies and sustained commitment are essential for improving rural healthcare. Without political will, progress remains elusive. The absence of reward or incentive systems in rural areas further exacerbates the situation. Intruducing compulsory rural service for newly recruited doctors could enhance healthcare access in remote regions. A multidimensional strategy is needed to address these issues, including regulatory changes, infrastructure investments in healthcare, and methods for luring and keeping skilled professionals in rural areas. Ensuring equitable healthcare for all, regardless of their geographic location, remains a critical goal for Bangladesh (Iqbal, 2019). Contributors to maternal and child healthcare Hwang and Park ( 2019 ) state that antenatal care (ANC) services, skilled birth attendance (SBA), postpartum care, child vaccination, and maternal healthcare satisfaction directly impact maternal and child healthcare. Distance from healthcare centers, travel time, travel cost, and mode of transportation (e.g., car, motorcycle, horse cart, ambulance, and boat) affect access to services in rural Cambodia. Kifle et al. (2018) assert that MCH in Eritrea is influenced by a number of factors, including the husband's educational background, the location of the delivery, household affluence, awareness of potential difficulties during childbirth, and the perceived quality of delivery care. Whereas, Sumankuuro, Crockett, and Wang (2018) argue that the skilled human resource base of healthcare facilities, healthcare infrastructure, medical equipment, logistic support, and effective referral management play crucial roles in maternal and childhealthcare in Ghana. Maternal age, parity, level of education for both parents, women’s current working status, urban/rural residence. Women’s decision-making capacity regarding their husband’s earnings and wealth index in Afghanistan and Ethiopia (Mumtaz, Bahk, & Khang, 2019; Tekelab et al., 2019). Addressing these multifaceted determinants requires tailored strategies, policy reforms, and community engagement. Empowering women and ensuring equitable access to quality care remain essential goals across these diverse contexts. Healthcare policies in Bangladesh In rural areas, both Supply-side (healthcare services) and demand-side (patient needs) interventions are critical for improving access to maternal and child healthcare (Yaya et al., 2018). Financial incentives, compensation packages, and motivation play a role in attracting and retaining doctors in rural regions. For instance, In order to solve retention concerns, the Bangladeshi government has made great strides in creating and putting into practice pertinent policies and procedures (Putri et al., 2020). The Human Resource Management (HRM) Operation Plan 2011–2016, the Health Nutrition and Population Sector Development Program 2011–2016, and the Bangladesh Health Workforce Strategy 2015 are noteworthy projects. The government has implemented a provision to provide a premium of 30% of the base salary for doctors in hard-to-reach areas, particularly in three districts in the Chattogram Hill Tracts in Bangladesh (Joarder et al., 2018). This study is unique among the few that have tried to pinpoint the factors that influence how easily accessible healthcare is for mothers and children in rural Bangladesh. It explores influential factors specifically in the rural areas of northeast Bangladesh, potentially adding new insights to the existing literature. This study may be the first to explore the influential factors of the accessibility to MCH in rural areas of northeastern Bangladesh. The careful review of the pertinent literature demonstrates that even though a few studies endeavor to detect the determinants of the accessibility to MCH in rural areas in Bangladesh, However, this study extends the literature and highlight its unique contribution. Methods Ethics statement This study, conducted from January to April 2020, rigorously adhered to the ethical principles outlined in the Declaration of Helsinki (1975, revised Hong Kong 1989). Prior to data collection, the study received approval from the Ethics Approval Committee of Sylhet Agricultural University, Bangladesh (Approval No: 327; Dated: December 19, 2019). Consequently, all research procedures were conducted in full compliance with both institutional and national standards. Furthermore, before participation, each respondent was fully informed about the study's purpose and procedures, and written informed consent, ensuring voluntary participation and the confidentiality of their responses, was obtained. Present study The southern and southwest portions of the Sylhet district (which contains the Surma River valley plain) and the northern portion of the Mymensingh district are included in the northeast region of Bangladesh, which is distinguished by a varied scenery of lakes, rivers, and hills. Unfortunately, healthcare facilities in this region lag behind those in other parts of Bangladesh. Key indicators such as treatment availability, healthcare services for infants, children, the elderly, and the economically disadvantaged, as well as eligibility for quality healthcare, all fall short. The region’s challenging terrain and remote locations hinder access to healthcare services. Insufficient attention and commitment from policymakers exacerbate the religion’s healthcare backwardness. Women in rural areas of this region face high maternal mortality rates (MMR). Additionally, a lower portion of mothers (25.7%) receive adequate antenatal care (ANC) and give birth with the assistance of experienced health staff compared to the national average (30.1%). (Khan et al., 2020). Focus group discussion (FGD) and variable selection We conducted a series of four Focus Group Discussions (FGDs), each involving 7–8 participants, between January 21 and January 27, 2020. These FGDs took place at various Health Complexes in Balaganj, Bishwanath, Golapganj, Kanaighat, and Osmaninagar Upazilas. The participants were mothers from different age groups. The primary objective of these FGDs was to identify essential variables f or the design of a questionnaire. These variables covered aspects related to farm size and knowledge about maternal and child healthcare. The findings from these FGDs, along with a review of existing literature, served as the basis for selecting the relevant variables. Age, distance of health complex (hos_dista), family composition (fam_com), number of experienced and skill pediatrics (exp_skill_ped), number of skill midwives (skill_midwi), number of obstetricians and female gynecologists (obs_gyne) , monthly income ( mon_inc ), and educational status (edu_sta) are the influential factors of accessibility to maternal and child healthcare services (Iqbal, 2019; Hardy et al., 2019 ; Broder et al., 2019 ; Okereke et al., 2019; Pant, Koirala, & Subedi, 2020; Mumtaz, Bahk, & Khang, 2019; Zegeye, Mbonigaba, & Dimbuene, 2018). Likewise, participants of FGDs suggest that accessibility to maternal and child healthcare services (acc_mat_chi_heal_ser), basic knowledge on maternal and child healthcare services (bknow_mat_chi_heal_care), level of satisfaction from service providers (lev_sat_ser_pro), service availability (ser_ava), and farm size (far_size) are also significant contributors to accessibility to maternal and child healthcare services in rural areas of Bangladesh. The first proposed variables are selected from existing literature and the rest proposed variables are selected from the findings of FGDs. Sampling strategy, questionnaire design, nature of collected data and model specification This research was conducted in various villages within the Sylhet district. It involved both quantitative and qualitative data obtained from married women who had at least one child and had experience accessing local clinics and Upazila Health Complexes during pregnancy and childbirth. The study utilized existing data from the Directorate General of Family Planning, which indicated that 48,414 rural women in this district had accessed various maternal and child healthcare services. 410 individuals were chosen at random, through purposive, and convenience sampling methods from this group of women. These women voluntarily participated in a questionnaire survey and were capable of providing informed consent. Structured questionnaires were used to collect essential data, and participants were provided with written information about the research. The data collection process occurred between February and April 2020 and involved individual in-depth interviews and observation. A concurrent mixed method evaluation was employed to carry out this research. The questionnaire used in this study was semi-structured and close-ended, comprising two segments. The first segment collected general information about the respondents, including details such as their names, residential locations, and communication information. The second segment focused on the respondents’ experiences and perceptions regarding maternal and child healthcare services in their localities, along with the proposed variables. To validate our proposed variables, we conducted a pre-test involving 13 respondents who were selected from the main survey. The results of the pre-test confirmed that the proposed variables were significant, reliable, and relevant in terms of respondents’ understanding and the validity of the variables. A few of the variables we've proposed—like farm size (far_size), level of satisfaction from service providers (lev_sat_ser_pro), accessibility to maternal and child healthcare services (acc_mat_chi_heal_ser), basic knowledge of these services (bknow_mat_chi_heal_care), and educational status (edu_sta)—are expressed by a dichotomous dummy, with Yes = 1 and Otherwise = 0. Continuous data is used to express the following: age, family composition (fam_com), distance to the health complex (hos_dista), number of skilled and experienced pediatricians (exp_skill_ped), number of skilled midwives (skill_midwi), number of obstetricians and female gynecologists (obs_fem_gyne), and monthly income (mon_inc). All these variables are considered as the explanatory variables and accessibility to maternal and child healthcare (acc_mat_chi_heal_ser) is treated as the outcome variable. To ensure accurate data entry and reliable estimates in both descriptive statistics and regression models, the data were manually entered and cross-verified after conducting the surveys. The econometric software STATA was then utilized to estimate the model parameters and calculate key statistical values, including the maximum, minimum, mean, and standard deviation. To determine the variables influencing the accessibility of maternity and pediatric healthcare services, an ordinal logit model was employed. This regression model is suitable for the observable dependent variable such as Yj; j = 0, 1, 2, ..., k ordered scales. The function of logistic regression is highly mathematically flexible, easy to use, and can be interpreted meaningfully in terms of the results (Alpar 2011 ). In the ordinal logistic regression model, let π( x ) = E ( y│x ) = P { y = 1│ x } where x is vector {x 1 , x 2 …x k } of independent variables and is expressed as the following $$\:{\pi\:}\left(\text{x}\right)=\frac{\text{exp}\left({\beta\:}_{0}+{\beta\:}_{1}{x}_{1}+{\beta\:}_{2}{x}_{2}+\dots\:+{\beta\:}_{k}{x}_{k}\right)}{1+\text{exp}\left({\beta\:}_{0}+{\beta\:}_{1}{x}_{1}+{\beta\:}_{2}{x}_{2}+\dots\:+{\beta\:}_{k}{x}_{k}\right)}\:=\frac{1}{1+\text{exp}\left[-\left({\beta\:}_{0}+{\beta\:}_{1}{x}_{1}+{\beta\:}_{2}{x}_{2}+\dots\:+{\beta\:}_{k}{x}_{k}\right)\right]}$$ 1 Results and Discussion Descriptive statistics The survey involving 410 mothers across seventeen villages. Over 93% of the surveyed mothers expressed concerns about the inadequate standard of maternal and child healthcare in rural areas. The respondents’ average age is approximately 27.83, falling within the reproductive age range (18–49) for woman. The average monthly household income for surveyed women is Bangladesi Taka (BDT: local currency of Bangladesh) is 33, 307.52. The mean distance from respondents’ residence to healthcare centers is 31.36 km. Most women have complited primary education (mean value of 5.74). The average family size is 11, with a significant number of dependents, including children. The smallest average value of farm size 0.25 suggests that a majority of respondents belong to poor households. Table 1 outlines the brief descriptive statistics of the variables. 4.2 Regression results The estimated ordinal logit model is shown in Table 2 . The estimated standard error of our regression model ensures that each of the variables we propose have a normal distribution. The measured values of Pseudo R2 (0.8150) and Log-likelihood (LL) (-19.196311) confirm that our regression model is suitable overall. A well-fitting model is indicated by a Pseudo R2 value more than 0.20 and an LL value closer to zero for the accessibility of maternity and child healthcare services (Iqbal & Rahaman, 2021). Table 2 outlines at important explanatory factors that affect MCH service accessibility. Every explanatory variable's last category acts as the benchmark for comparisons. There is a significant correlation for each category taken into consideration. Depending on the type of coefficients, the expected rate of the dependent variable varies when the value of a significant variable rises by one unit. There has been a great acknowledgement of the role that maternity and child healthcare services play in lowering the death and morbidity rates of mothers and newborns. With access to basic prenatal, natal, and postnatal care, moms can avoid the majority of maternal and child mortality. Even in environments where maternal healthcare services are widely accessible, adoption of these services is far from universal (Srivastava et al., 2014). It is well acknowledged that when it comes to the use of maternal medical services, a mother's age can occasionally be used as a proxy for her level of education about healthcare services (Fosu, 1994 ). Women may be more aware of the healthcare services that are accessible to them and value modern medicine more as a result of recent improvements in educational possibilities for women (Elo, 1992a ). According to a study's logistic regression results, mothers' educational attainment was correlated with an increase in medical checkups (Rahman, Islam, & Rahman 2010). According to a survey, just 10.8% of moms without formal education received "excellent" maternal care services, while 49.6% of mothers with at least a high school diploma did (Srivastava et al., 2014). It is well knowledge that using contemporary healthcare services is positively impacted by higher income (Elo, 1992b ). Husband's employment is portrayed as a component that makes it possible for mothers to receive healthcare services by acting as a stand-in for income (Fosu, 1994 ). According to an analysis, women from households below the poverty line were less likely than women from families above it to use maternal healthcare services. It was clear that, compared to women from the lowest quintile of society, women from the richest quintile had a 4.53 times higher chance of obtaining ANC during pregnancy (Jat, Ng, & San, 2011). This indicates that, in comparison to mothers from lower-income families, moms from higher-income families used maternal healthcare services at a higher percentage. According to Shahjahan et al. (2013), the study also revealed that moms with one living child had the highest percentage of appropriate maternal healthcare utilization when compared to women with two or more children. Due to time constraints that prevent them from seeking healthcare, women who have a large number of children underutilize the resources that are accessible to them (McKinlay, 1972). The size of the family, which is the fundamental social unit, has a significant impact on matters pertaining to health, including the utilization of healthcare services (Zhang et al., 2016). Large family mothers typically underuse healthcare services since they have too many responsibilities taking up their time. Resource limitations brought on by larger families also have a detrimental impact on healthcare use (Wong et al., 1987). Distance appears to work as a key deterrent to access. Because pregnancy and labor are physical conditions that cannot permit pregnant mothers to walk or travel for long, these can bring about adverse effects on the health of a mother and the child she is carrying (Leslie & Gupta 1989). Lack of transport, distance, difficult topography, the cost of ambulance fuel, use of an ambulance for unintended purpose and uncooperative behavior of ambulance drivers affected the accessibility also (Kea et al., 2018). Adequate experienced and skill pediatrics, skill midwives, and obstetricians and female gynecologist may improve the accessibility condition to maternal and child healthcare in rural areas (Barnea et al., 2021 ). Pregnant mothers' decisions to use formal maternal healthcare services are significantly influenced by the gender of the medical staff. It is typically seen that during the complication phase, female patients are reluctant to visit the facility in order to receive care from male specialists (Banik, 2003 ). This is caused by a number of causes, one of which being the country's primarily Muslim population. As a result, cultural and religious preferences have a significant role in whether or not mothers receive treatment from male doctors, which may be viewed as a religious sin (Sarker et al., 2016). There are situations when women might not want to give birth to a male doctor or divulge important and complicated medical information. According to certain studies, the lack of privacy and secrecy in receiving maternal healthcare from male doctors was a source of distaste for expectant mothers (Some, Sombie, & Meda, 2011; Titaley et al., 2010). The constituents of static healthcare services have served as a representation of their quality. High-quality health posts are those that have a complete staff of medical professionals, at least 50% of recommended medications on hand, distinct maternal health and child health clinics, and a respectable physical infrastructure. Health articles are categorized as low quality if they don't meet all of these requirements (Acharya & Cleland 2000 ). Access to healthcare services in low-resource countries like Bangladesh may be severely hampered by factors such as service location, unqualified healthcare workers, staff absenteeism, inadequate health services, costs and prices of services, including unofficial payments, and staff interpersonal skills, including trust (Jacobs et al., 2012 ; Ensor & Cooper, 2004 ; Peters et al., 2008). According to the results of a linked poll, the majority of respondents (89%) were willing to pay for healthcare services if pharmaceuticals were easily accessible, and 92.4% wanted to pay if overall quality improved. However, the use of maternity and child healthcare services was hindered by long waiting lines, the actions of providers, and a shortage of doctors (Uzochukwu, Onwujekwe, & Akpala, 2004). The decision regarding when and where a woman should seek maternal healthcare services is influenced by a collective consensus involving the husband, mother-in-law, and other family members. This decision-making process is further shaped by gender dynamics and economics constraints. Women often rely on men for financial support, and local cultural norms emphasize the importance of respecting the opinions of mothers-in-law. Similar findings have been documented in studies conducted in various countries. For instance, Pradhan and Mondal (2023) showed that, among nuclear families, women with with stronger marital relationships are more likely to utilize MCH services and give birth in healthcare facilities. In joint families, women who maintain positive relationships with their in-laws are also more inclined to seek antenatal care services (Allendorf, 2010 ; Warren, 2010). Rural women of northeastern Bangladesh face several adverse circumstances. This study targeted those barriers and results showed that several demographical factors were influencing rural women whether to make decisions about seeking institutional maternal healthcare services or not, such as age, education of the mothers, household financial status, cooperation of family members, number of living children and size of the family. The distance of the maternal healthcare center from home and the availability of healthcare facilities are also evident factors. Women are not interested to utilize maternal healthcare services in the facilities, because it was not staffed with encouraging, respectful, skilled healthcare providers, did not provide the required drugs and the environment was very unhealthy. Not only that, some women were also found not cooperating with the services provided to them. They were not interested to go there again for the scarcity of female doctors. Besides these factors, several cultural barriers, superstitious mindset and lack of awareness was also an issue. This study not only examined the significance level, but also identified the probable positive outcomes which could be made by bringing necessary changes in those identified factors held responsible for the healthcare-seeking behavior of northeastern rural women of Bangladesh. Conclusions In the villages of this region, there has been a notable improvement in the availability of healthcare services, particularly MCH facilities. These services play a crucial role in addressing factors related to maternal health, such as maternal mortality and morbidity rates, as well as maternal nutrition status. Additionally, process indicators related to service availability and utilization contribute to enhancing overall maternal well-being. Many healthcare centers now operate in rural areas, both government and private organizations, including non-government organizations (NGOs). These organizations complete to provide maternal healthcare services, resulting in better quality healthcare and more affordable options for patients. Dispite these efforts, a gap still between available facilities and the target population. Accessing maternal healthcare services is not a one-sided process. Both recipients and service providers play crucial roles. Ensuring adequate facilities in maternal and child healthcare centers is essential to attract women. Effective utilization of services is equally critical, Otherwise, efforts may not yield fruitful results. Women and their families must understand the importance of qualified professionals providing MCH services. Establishinh more healthcare centers in rural areas will enhance accessibility. Regular monitoring of health workers’ activities is vital. Raising awareness among rural residents, particularly those receiving maternal and child healthcare services, along with government and policymaker interventions, can significantly enhance the quality and accessibility of maternal and child healthcare services for rural women in northeast Bangladesh. Maternal and child health remains a top priority in the villages of this region. Therefore, it is crucial to place special emphasis on maternal health, examining existing policies, strategies, and interventions aimed at improving MCH outcomes. Declarations Data availability statements The data supporting this article cannot be made publicly accessible due to privacy concerns for the individuals who participated in the study. However, interested parties may request access to the data from the corresponding author through reasonable means. 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Studies in American Political Development , 32 (2), 257-291. https://doi.org/10.1017/S0898588X18000123 Okereke, E., Ishaku, S. M., Unumeri, G., Mohammed, B., & Ahonsi, B. (2019). Reducing maternal and newborn mortality in Nigeria—a qualitative study of stakeholders’ perceptions about the performance of community health workers and the introduction of community midwifery at primary healthcare level. Human Resources for Health , 17 (1), 1-9. https://doi.org/10.1186/s12960-019-0430-0 Pant, S., Koirala, S., & Subedi, M. (2020). Access to maternal health services during COVID-19. Europasian Journal of Medical Sciences , 2 (2), 46-50. https://doi.org/10.46405/ejms.v2i2.110 Peters, D. H., Garg, A., Bloom, G., Walker, D. G., Brieger, W. R., & Rahman, M. H. (2008). Poverty and access to health care in developing countries. Annals of the New York Academy of Sciences ,1136 , 161–171. https://doi.org/10.1196/annals.1425.011 Pradhan, M. R., & Mondal, S. (2023). Exploring the Relationship between Household Structure and Utilisation of Maternal Health Care Services in India. Journal of Biosocial Science , 55 (3), 438-448. https://doi.org/10.1017/S0021932022000219 Putri, L. P., O’Sullivan, B. G., Russell, D. J., & Kippen, R. (2020). Factors associated with increasing rural doctor supply in Asia-Pacific LMICs: a scoping review. Human Resources for Health , 18 (1), 1-21. https://doi.org/10.1186/s12960-020-00533-4 Rahman, A.E., Hossain, A.T., Siddique, A.B., Jabeen, S., Chisti, M.J., Dockrell, D.H., Nair, H., Jamil, K., Campbell, H. and El Arifeen, S., 2021. Child mortality in Bangladesh–why, when, where and how? A national survey-based analysis. Journal of Global Health , 11 . https://doi: 10.7189/jogh.11.04052 Rahman, M., Islam, R., & Rahman, M. (2010). Antenatal Care Seeking Behaviour among Slum Mothers: A Study of Rajshahi City Corporation, Bangladesh. Sultan Qaboos University Medical Journal ,10 (1), 50–56. Sammut, S. M. (2021). The Role of the Biotechnology Industry in Addressing Health Inequities in Africa: Strengthening the Entire Health Care Value Chain. Journal of Commercial Biotechnology , 26 (4). https://doi.org/10.5912/jcb1008 Sarker, B. K., Rahman, M., Rahman, T., Hossain, J., Reichenbach, L., & Mitra, D. K. (2016). Reasons for Preference of Home Delivery with Traditional Birth Attendants (TBAs) in Rural Bangladesh: A Qualitative Exploration. Plos One ,11 (1), e0146161. https://doi.org/10.1371/journal.pone.0146161 Shahjahan, M., Chowdhury, H., Akter, J., Afroz, A., Rahman, M.M., & Hafez, M. (2013). Factors associated with use of antenatal care services in a rural area of Bangladesh. South East Asia Journal of Public Health ,2 (2), 61-66. https://doi.org/10.3329/ seajph.v2i2.15956 Some, T. D., Sombie, I., & Meda, N. (2011). Women's perceptions of homebirths in two rural medical districts in Burkina Faso: a qualitative study. Reproductive Health ,8 , 3. https://doi.org/10.1186/1742-4755-8-3 Srivastava, A., Mahmood, S., Mishra, P., & Shrotriya, V. (2014). Correlates of maternal health care utilization in rohilkhand region, India. Annals of Medical and Health Sciences Research ,4 (3), 417–425. https://doi.org/10.4103/2141-9248.133471 Sumankuuro, J., Crockett, J., & Wang, S. (2018). Perceived barriers to maternal and newborn health services delivery: a qualitative study of health workers and community members in low and middle-income settings. BMJ open , 8 (11), e021223. https://doi.org/10.1136/bmjopen-2017-021223 Tekelab, T., Chojenta, C., Smith, R., & Loxton, D. (2019). Factors affecting utilization of antenatal care in Ethiopia: a systematic review and meta-analysis. Plos One , 14 (4), e0214848. https://doi.org/10.1371/journal.pone.0214848 Titaley, C. R., Hunter, C. L., Dibley, M. J., & Heywood, P. (2010). Why do some women still prefer traditional birth attendants and home delivery?: a qualitative study on delivery care services in West Java Province, Indonesia. BMC Pregnancy and Childbirth ,10 , 43. https://doi.org/10.1186/1471-2393-10-43. Uzochukwu, B. S., Onwujekwe, O. E., & Akpala, C. O. (2004). Community satisfaction with the quality of maternal and child health services in southeast Nigeria. East African Medical Journal ,81 (6), 293–299. https://doi.org/10.4314/eamj.v81i6.9178 Yaya, S., Uthman, O. A., Amouzou, A., Ekholuenetale, M., & Bishwajit, G. (2018). Inequalities in maternal health care utilization in Benin: a population based cross-sectional study. BMC Pregnancy and Childbirth , 18 (1), 1-9. https://doi.org/10.1186/s12884-018-1846-6 Warren C. (2010). Care seeking for maternal health: challenges remain for poor women. Ethiopia Journal of Health Development, 24 (1), 100-4. https://doi.org/10.4314/ejhd.v24i1.62950 WDI. (2017). International Comparison Program Database: GDP Per Capita, PPP (current international $), The World Bank: Washington DC. Retrieved from http://data.worldbank.org/indicator Wong, E. L., Popkin, B. M., Gullkey, D. K., & Akin, J. S. (1987). Accessibility, quality of care and prenatal care use in the Philippines. Social Science and Medicine ,24 , 927-944. https://doi.org/10.1016/0277-9536(87)90286-3 Zegeye, E. A., Mbonigaba, J., & Dimbuene, Z. T. (2018). Factors associated with the utilization of antenatal care and prevention of mother-to-child HIV transmission services in Ethiopia: applying a count regression model. BMC Women's Health , 18 (1), 1-11. https://doi.org/10.1186/s12905-018-0679-9 Zhang, L., Xue, C., Wang, Y., Zhang, L., & Liang, Y. (2016). Family characteristics and the use of maternal health services: a population-based survey in Eastern China. Asia Pacific Family Medicine,15 , 5. https://doi.org/10.1186/s12930-016-0030-2 Additional Declarations No competing interests reported. Cite Share Download PDF Status: Posted Version 1 posted You are reading this latest preprint version Research Square lets you share your work early, gain feedback from the community, and start making changes to your manuscript prior to peer review in a journal. As a division of Research Square Company, we’re committed to making research communication faster, fairer, and more useful. We do this by developing innovative software and high quality services for the global research community. Our growing team is made up of researchers and industry professionals working together to solve the most critical problems facing scientific publishing. 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-6234069","acceptedTermsAndConditions":true,"allowDirectSubmit":true,"archivedVersions":[],"articleType":"Article","associatedPublications":[],"authors":[{"id":482291627,"identity":"a48e312d-2586-48a1-9493-19c9975fae70","order_by":0,"name":"Md. Hafiz Iqbal","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAA50lEQVRIiWNgGAWjYHACZiCWYDBgYG58AGTx8JGghbHZAKSFjUgtDCAtbRIgBkEtBsePPzb4uMdCdjv7wbbKrzl2MmwMzA8f3cCn5UyOceKMZxLGO3sS227LbksGOozN2DgHjxbJhhzmwzwHJBI3HABqkdzGDNTCwyaNV0v/88cQLecfthVLbqsnrIVfIsE4GazlRmIb48dth4nR8sbYcMYBoF9mPGyWZtx2nIeNmYBf2PjTH0t8OFAnu50/+eDHn9uq7fnZmx8+xqcFBhgbgAQzD4jJTIRyuBbGH0SqHgWjYBSMgpEFAPbiR/Jpj9FNAAAAAElFTkSuQmCC","orcid":"","institution":"Government Edward College","correspondingAuthor":true,"prefix":"","firstName":"Md.","middleName":"Hafiz","lastName":"Iqbal","suffix":""},{"id":482291628,"identity":"2c317406-849b-4ca7-9319-2696c7475909","order_by":1,"name":"Eshita Deb","email":"","orcid":"","institution":"Sylhet Agricultural University","correspondingAuthor":false,"prefix":"","firstName":"Eshita","middleName":"","lastName":"Deb","suffix":""},{"id":482291629,"identity":"3f8d1267-3aeb-4588-b05e-3ec2dee762cd","order_by":2,"name":"Mitu Chowdhury","email":"","orcid":"","institution":"Sylhet Agricultural University","correspondingAuthor":false,"prefix":"","firstName":"Mitu","middleName":"","lastName":"Chowdhury","suffix":""},{"id":482291630,"identity":"5acdcb18-815c-4116-ac01-2fc3d520800f","order_by":3,"name":"Modhumita Bhattachirjee Pia","email":"","orcid":"","institution":"Sylhet Agricultural University","correspondingAuthor":false,"prefix":"","firstName":"Modhumita","middleName":"Bhattachirjee","lastName":"Pia","suffix":""},{"id":482291631,"identity":"fe94e1fc-86cb-4f0f-b458-5480ed4e1262","order_by":4,"name":"Md. Nur Mozahid","email":"","orcid":"","institution":"Sylhet Agricultural University","correspondingAuthor":false,"prefix":"","firstName":"Md.","middleName":"Nur","lastName":"Mozahid","suffix":""}],"badges":[],"createdAt":"2025-03-15 17:08:03","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-6234069/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-6234069/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":91620903,"identity":"b710b547-dd79-4dab-81fb-ae163350b2aa","added_by":"auto","created_at":"2025-09-18 11:24:36","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":664759,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-6234069/v1/8061a50c-6e39-4661-baa1-3ef89c138ea9.pdf"}],"financialInterests":"No competing interests reported.","formattedTitle":"Factors influencing the accessibility to maternal and child healthcare services: Case study of rural areas of northeast Bangladesh","fulltext":[{"header":"Introduction","content":"\u003cp\u003eOver the past three decades (from 1990 to 2020), neonatal mortality in Bangladesh has significantly decreased by half due to a progressive government initiative focused on pregnant women (Dutta et al., \u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e2020\u003c/span\u003e). These women have benefited from an iron and folic acid (IFA) supplementation program supported by the government (Kurzawa et al., 2021). Additionally, advancements in medical science, improved healthcare facilities, widespread media coverage, pregnancy awareness campaigns, better availability of medicines, effective family planning practices, increased access to contraceptives, educational opportunities for girls, restrictions on early marriage, changing societal attitudes, and improved economic conditions have all contributed to positive outcomes in maternal mortality.Furthermore, the government and other development partners have actively promoted prenatal care, introduced health voucher schemes for disadvantaged women, established community-based skilled birth attendants, and implemented midwifery programs in service delivery (The Daily Star, 2014). Colloborative endeavors have been instrumental in attaining positive outcomes in MCH in Bangladesh.\u003c/p\u003e\u003cp\u003eThe Bangladesh government has initiated an awareness program for pregnant women, aiming to safeguard them from anemia, vitamin deficiencies, and inadequate nutrition. The program also emphasizes the importance of consuming recommended amounts of vitamin B, iron, and folic acid during pregnancy. Across various clinics, hospitals, and Upazila Health Complexes (the smallest administrative units in Bangladesh), the government has integrated nutrition, maternal, and newborn healthcare services into a comprehensive basic healthcare package (Hoque et al., \u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e2018\u003c/span\u003e). As a result, the maternal mortality rate in Bangladesh has decreased by over 69 percent in the past two decades, surpassing India but remaining lower than Pakistan\u0026rsquo;s rate (WDI, 2017). Over the past decade, the infant mortality rate in Bangladesh has steadily decreased, declining from 35.5 deaths per 1,000 live births in 2011 to 22.9 in 2021 (Rahman et al., 2021). Despite this decline, rural (MCH) in Bangladesh still faces challenges due to inequitable geographic distribution of healthcare professionals, essential drugs, and medical equipment. Significant reductions Significant reduction in mortality have beem observed, particularly among children aged one month and older (Islam \u0026amp; Biswas, 2020). These improvements can be attributed to various factors, including enhanced postnatal care for newborns, better breastfeeding practices, prevention of neonatal tetanus, increased vaccination coverage for vitamin A supplementation, improved pneumonia management, and the use of oral rehydration solutions (ORS) to combat diarrhea. In 1975, the infant mortality rate was 145.2 per 1,000 live births, but by 2015, it had dropped to 30.7 (Iqbal, 2019). Bangladeshhas also witnessed a decline in child mortality among children under five years old. In 1975, the child mortality rate was 217.1 per 1,000 live births, whereas in 2015, it stood at 37.6 (WDI, 2017). Factors such as elimination of cultural obstacles, religious superstition, increased competent delivery attendance, awareness, and women\u0026rsquo;s literacy rates have played a crucial role in lowering infant mortality rates (Cheong \u0026amp; Baltazar, \u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e2021\u003c/span\u003e; Mike \u0026amp; Muhammad, 2020).\u003c/p\u003e\u003cp\u003eIn rural Bangladesh, there are significant challenges related to equity, inclusion, human rights, access to healthcare services (Kemp et al., 2022). Informal and unregulated rural medical practioners (RMPs) play a crucial role in providing healthcare services to about two-thirds of the population. Despite being embedded in local communities and providing cost-effective services, RMPs are frequently perceived as offering substandard care by qualified healthcare providers (Iqbal, 2019). Issues observed in RMPs\u0026rsquo; practice pattern include inappropriate diagnoses, irrational use of antibiotics and other drugs, and polypharmacy.\u003c/p\u003e\u003cp\u003eHospitals and physicians of metropolitan city face challenges such as less comprehensive healthcare facilities, greater distance from patients, and higher costs. The retention of competent medical practitioners disproportionately affects (MCH) services in rural areas. Even rural communities in northeast Bangladesh are not immune to these problems. Despite poverty and inequality, Bangladesh has achieved better-than-expected health outcomes. However, non-communicable diseases (NCDs) remain prevalent, especially among underprivileged communities like rural inhabitants. Rapid urbanization poses new challenges for healthcare workers, further dividing problems between urban and rural healthcare services are critical steps toward improving overall health outcomes in Bangladesh.\u003c/p\u003e\u003cp\u003eAccording to Nachlis (2018), Sammut (2021), and Alkhamis ert al. (2021), the existing healthcare system is centralized, which can hinder efficient service delivery; inadequate governance structures and regulatory frameworks pose challenges to effective MCH services; within the relevant ministry, there is a lack of capacity for managing MCH service effectively; the delivery of MCH services is fragmented, leading to inefficiencies; socioeconomic challenges, particularly poverty, impact to MCH services; suboptimal allocation of resources affects MCH service provision; insufficient regulation of the private sector can improve MCH services; and a scarcity of qualified doctors and nurses further exacerbates the challenges in MCH service delivery. These findings imply that identifying influential factors can help to alleviate these problems and establish suitable healthcare policies for rural mothers and children. However, the following questions remain: what causes are to blame for the poor accessibility of MCH services in rural areas of northeast Bangladesh? What level of accessibility can be increased if appropriate changes to the variables are made?\u003c/p\u003e\u003cp\u003eWe still need to look at the elements that affect MCH service availability in Bangladesh's rural areas. As a result, there is a need to investigate the major factors affecting such patients' access to healthcare. The study explores these elements and provides required suggestions for mothers and their children in rural Bangladesh to sustain healthcare practices.\u003c/p\u003e"},{"header":"Literature Review","content":"\u003cp\u003eThis section covers three categories, such as the challenges of equitable healthcare for all in rural areas, contributors to maternal and child healthcare, and policies. Below is a quick rundown of each of the strands.\u003c/p\u003e\u003cdiv id=\"Sec3\" class=\"Section2\"\u003e\u003ch2\u003eChallenges of equitable healthcare for all in rural Bangladesh\u003c/h2\u003e\u003cp\u003eDespite concerted efforts at global, regional, and national levels to enhance overall population health, significant hurdles persist in meeting the healthcare needs of those residing in rural and hard-to-reach areas (Doshmangir et al., \u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e2019\u003c/span\u003e). In Bangladesh, healthcare facilities often lack essential services, medical equipment, and trained staff. Many rural areas struggle with inadequate transportation infrastructure, making it difficult for people to reach healthcare centers promptly. Even when services are available, utilization rates remain low due to various barriers, including cultural norms and lack of awareness. The coverage of healthcare services in rural regions falls short of meeting the population\u0026rsquo;s needs (Haque, Parr, \u0026amp; Muhidin, \u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e2019\u003c/span\u003e; Alam et al., \u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2019\u003c/span\u003e; Haque, Parr, \u0026amp; Muhidin, \u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e2020\u003c/span\u003e). Rural areas face a severe shortage of qualified doctors. The retention of medical professionals in remote health facilities is also problemetic. The scarcity of healthcare providers disproportionately affects mothers and children residing in rural areas. There is an overall shortage of experienced healthcare workers, leading to imbalances in skill mix (Liu et al. 2017). Healthcare providers tend to concentrate in urban areas, leaving rural regions underserved. Limited access to update medical knowledge and training hampers effective healthcare delivery. Challenging work conditions and lack of incentives discourage healthcare professionals from serving in rural settings. Adequate policies and sustained commitment are essential for improving rural healthcare. Without political will, progress remains elusive. The absence of reward or incentive systems in rural areas further exacerbates the situation. Intruducing compulsory rural service for newly recruited doctors could enhance healthcare access in remote regions. A multidimensional strategy is needed to address these issues, including regulatory changes, infrastructure investments in healthcare, and methods for luring and keeping skilled professionals in rural areas. Ensuring equitable healthcare for all, regardless of their geographic location, remains a critical goal for Bangladesh (Iqbal, 2019).\u003c/p\u003e\u003c/div\u003e\n\u003ch3\u003eContributors to maternal and child healthcare\u003c/h3\u003e\n\u003cp\u003eHwang and Park (\u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e2019\u003c/span\u003e) state that antenatal care (ANC) services, skilled birth attendance (SBA), postpartum care, child vaccination, and maternal healthcare satisfaction directly impact maternal and child healthcare. Distance from healthcare centers, travel time, travel cost, and mode of transportation (e.g., car, motorcycle, horse cart, ambulance, and boat) affect access to services in rural Cambodia. Kifle et al. (2018) assert that MCH in Eritrea is influenced by a number of factors, including the husband's educational background, the location of the delivery, household affluence, awareness of potential difficulties during childbirth, and the perceived quality of delivery care. Whereas, Sumankuuro, Crockett, and Wang (2018) argue that the skilled human resource base of healthcare facilities, healthcare infrastructure, medical equipment, logistic support, and effective referral management play crucial roles in maternal and childhealthcare in Ghana. Maternal age, parity, level of education for both parents, women\u0026rsquo;s current working status, urban/rural residence. Women\u0026rsquo;s decision-making capacity regarding their husband\u0026rsquo;s earnings and wealth index in Afghanistan and Ethiopia (Mumtaz, Bahk, \u0026amp; Khang, 2019; Tekelab et al., 2019).\u003c/p\u003e\u003cp\u003eAddressing these multifaceted determinants requires tailored strategies, policy reforms, and community engagement. Empowering women and ensuring equitable access to quality care remain essential goals across these diverse contexts.\u003c/p\u003e\n\u003ch3\u003eHealthcare policies in Bangladesh\u003c/h3\u003e\n\u003cp\u003eIn rural areas, both Supply-side (healthcare services) and demand-side (patient needs) interventions are critical for improving access to maternal and child healthcare (Yaya et al., 2018). Financial incentives, compensation packages, and motivation play a role in attracting and retaining doctors in rural regions. For instance, In order to solve retention concerns, the Bangladeshi government has made great strides in creating and putting into practice pertinent policies and procedures (Putri et al., 2020). The Human Resource Management (HRM) Operation Plan 2011\u0026ndash;2016, the Health Nutrition and Population Sector Development Program 2011\u0026ndash;2016, and the Bangladesh Health Workforce Strategy 2015 are noteworthy projects. The government has implemented a provision to provide a premium of 30% of the base salary for doctors in hard-to-reach areas, particularly in three districts in the Chattogram Hill Tracts in Bangladesh (Joarder et al., 2018). This study is unique among the few that have tried to pinpoint the factors that influence how easily accessible healthcare is for mothers and children in rural Bangladesh. It explores influential factors specifically in the rural areas of northeast Bangladesh, potentially adding new insights to the existing literature.\u003c/p\u003e\u003cp\u003eThis study may be the first to explore the influential factors of the accessibility to MCH in rural areas of northeastern Bangladesh. The careful review of the pertinent literature demonstrates that even though a few studies endeavor to detect the determinants of the accessibility to MCH in rural areas in Bangladesh, However, this study extends the literature and highlight its unique contribution.\u003c/p\u003e"},{"header":"Methods","content":"\u003cdiv id=\"Sec7\" class=\"Section2\"\u003e\u003ch2\u003eEthics statement\u003c/h2\u003e\u003cp\u003e This study, conducted from January to April 2020, rigorously adhered to the ethical principles outlined in the Declaration of Helsinki (1975, revised Hong Kong 1989). Prior to data collection, the study received approval from the Ethics Approval Committee of Sylhet Agricultural University, Bangladesh (Approval No: 327; Dated: December 19, 2019). Consequently, all research procedures were conducted in full compliance with both institutional and national standards. Furthermore, before participation, each respondent was fully informed about the study's purpose and procedures, and written informed consent, ensuring voluntary participation and the confidentiality of their responses, was obtained.\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec8\" class=\"Section2\"\u003e\u003ch2\u003ePresent study\u003c/h2\u003e\u003cp\u003eThe southern and southwest portions of the Sylhet district (which contains the Surma River valley plain) and the northern portion of the Mymensingh district are included in the northeast region of Bangladesh, which is distinguished by a varied scenery of lakes, rivers, and hills. Unfortunately, healthcare facilities in this region lag behind those in other parts of Bangladesh. Key indicators such as treatment availability, healthcare services for infants, children, the elderly, and the economically disadvantaged, as well as eligibility for quality healthcare, all fall short.\u003c/p\u003e\u003cp\u003eThe region\u0026rsquo;s challenging terrain and remote locations hinder access to healthcare services. Insufficient attention and commitment from policymakers exacerbate the religion\u0026rsquo;s healthcare backwardness. Women in rural areas of this region face high maternal mortality rates (MMR). Additionally, a lower portion of mothers (25.7%) receive adequate antenatal care (ANC) and give birth with the assistance of experienced health staff compared to the national average (30.1%). (Khan et al., 2020).\u003c/p\u003e\u003c/div\u003e\n\u003ch3\u003eFocus group discussion (FGD) and variable selection\u003c/h3\u003e\n\u003cp\u003eWe conducted a series of four Focus Group Discussions (FGDs), each involving 7\u0026ndash;8 participants, between January 21 and January 27, 2020. These FGDs took place at various Health Complexes in Balaganj, Bishwanath, Golapganj, Kanaighat, and Osmaninagar Upazilas. The participants were mothers from different age groups. The primary objective of these FGDs was to identify essential variables \u003cb\u003ef\u003c/b\u003eor the design of a questionnaire. These variables covered aspects related to farm size and knowledge about maternal and child healthcare. The findings from these FGDs, along with a review of existing literature, served as the basis for selecting the relevant variables.\u003c/p\u003e\u003cp\u003e\u003cem\u003eAge, distance of health complex (hos_dista), family composition (fam_com), number of experienced and skill pediatrics (exp_skill_ped), number of skill midwives (skill_midwi), number of obstetricians and female gynecologists (obs_gyne)\u003c/em\u003e, \u003cem\u003emonthly income\u003c/em\u003e (\u003cem\u003emon_inc\u003c/em\u003e), \u003cem\u003eand educational status (edu_sta)\u003c/em\u003e are the influential factors of accessibility to maternal and child healthcare services (Iqbal, 2019; Hardy et al., \u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e2019\u003c/span\u003e; Broder et al., \u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e2019\u003c/span\u003e; Okereke et al., 2019; Pant, Koirala, \u0026amp; Subedi, 2020; Mumtaz, Bahk, \u0026amp; Khang, 2019; Zegeye, Mbonigaba, \u0026amp; Dimbuene, 2018). Likewise, participants of FGDs suggest that accessibility to maternal and child healthcare services (acc_mat_chi_heal_ser), basic \u003cem\u003eknowledge on maternal and child healthcare services\u003c/em\u003e (bknow_mat_chi_heal_care), \u003cem\u003elevel of satisfaction from service providers (lev_sat_ser_pro), service availability (ser_ava), and farm size (far_size)\u003c/em\u003e are also significant contributors to accessibility to maternal and child healthcare services in rural areas of Bangladesh. The first proposed variables are selected from existing literature and the rest proposed variables are selected from the findings of FGDs.\u003c/p\u003e\n\u003ch3\u003eSampling strategy, questionnaire design, nature of collected data and model specification\u003c/h3\u003e\n\u003cp\u003eThis research was conducted in various villages within the Sylhet district. It involved both quantitative and qualitative data obtained from married women who had at least one child and had experience accessing local clinics and Upazila Health Complexes during pregnancy and childbirth. The study utilized existing data from the Directorate General of Family Planning, which indicated that 48,414 rural women in this district had accessed various maternal and child healthcare services.\u003c/p\u003e\u003cp\u003e410 individuals were chosen at random, through purposive, and convenience sampling methods from this group of women. These women voluntarily participated in a questionnaire survey and were capable of providing informed consent. Structured questionnaires were used to collect essential data, and participants were provided with written information about the research. The data collection process occurred between February and April 2020 and involved individual in-depth interviews and observation. A concurrent mixed method evaluation was employed to carry out this research.\u003c/p\u003e\u003cp\u003eThe questionnaire used in this study was semi-structured and close-ended, comprising two segments. The first segment collected general information about the respondents, including details such as their names, residential locations, and communication information. The second segment focused on the respondents\u0026rsquo; experiences and perceptions regarding maternal and child healthcare services in their localities, along with the proposed variables.\u003c/p\u003e\u003cp\u003eTo validate our proposed variables, we conducted a pre-test involving 13 respondents who were selected from the main survey. The results of the pre-test confirmed that the proposed variables were significant, reliable, and relevant in terms of respondents\u0026rsquo; understanding and the validity of the variables.\u003c/p\u003e\u003cp\u003eA few of the variables we've proposed\u0026mdash;like farm size (far_size), level of satisfaction from service providers (lev_sat_ser_pro), accessibility to maternal and child healthcare services (acc_mat_chi_heal_ser), basic knowledge of these services (bknow_mat_chi_heal_care), and educational status (edu_sta)\u0026mdash;are expressed by a dichotomous dummy, with Yes\u0026thinsp;=\u0026thinsp;1 and Otherwise\u0026thinsp;=\u0026thinsp;0. Continuous data is used to express the following: age, family composition (fam_com), distance to the health complex (hos_dista), number of skilled and experienced pediatricians (exp_skill_ped), number of skilled midwives (skill_midwi), number of obstetricians and female gynecologists (obs_fem_gyne), and monthly income (mon_inc). All these variables are considered as the explanatory variables and accessibility to maternal and child healthcare (acc_mat_chi_heal_ser) is treated as the outcome variable. To ensure accurate data entry and reliable estimates in both descriptive statistics and regression models, the data were manually entered and cross-verified after conducting the surveys. The econometric software STATA was then utilized to estimate the model parameters and calculate key statistical values, including the maximum, minimum, mean, and standard deviation.\u003c/p\u003e\u003cp\u003eTo determine the variables influencing the accessibility of maternity and pediatric healthcare services, an ordinal logit model was employed. This regression model is suitable for the observable dependent variable such as \u003cem\u003eYj; j\u003c/em\u003e\u0026thinsp;=\u0026thinsp;\u003cem\u003e0, 1, 2, ..., k\u003c/em\u003e ordered scales. The function of logistic regression is highly mathematically flexible, easy to use, and can be interpreted meaningfully in terms of the results (Alpar \u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e2011\u003c/span\u003e). In the ordinal logistic regression model, let π(\u003cem\u003ex\u003c/em\u003e)\u0026thinsp;=\u0026thinsp;\u003cem\u003eE\u003c/em\u003e (\u003cem\u003ey│x\u003c/em\u003e)\u0026thinsp;=\u0026thinsp;\u003cem\u003eP\u003c/em\u003e {\u003cem\u003ey\u003c/em\u003e\u0026thinsp;=\u0026thinsp;1│\u003cem\u003ex\u003c/em\u003e}\u003c/p\u003e\u003cp\u003ewhere x is vector {x\u003csub\u003e1\u003c/sub\u003e, x\u003csub\u003e2\u003c/sub\u003e\u0026hellip;x\u003csub\u003ek\u003c/sub\u003e} of independent variables and is expressed as the following\u003cdiv id=\"Equ1\" class=\"Equation\"\u003e\u003cdiv format=\"TEX\" class=\"mathdisplay\" id=\"FileID_Equ1\" name=\"EquationSource\"\u003e\n$$\\:{\\pi\\:}\\left(\\text{x}\\right)=\\frac{\\text{exp}\\left({\\beta\\:}_{0}+{\\beta\\:}_{1}{x}_{1}+{\\beta\\:}_{2}{x}_{2}+\\dots\\:+{\\beta\\:}_{k}{x}_{k}\\right)}{1+\\text{exp}\\left({\\beta\\:}_{0}+{\\beta\\:}_{1}{x}_{1}+{\\beta\\:}_{2}{x}_{2}+\\dots\\:+{\\beta\\:}_{k}{x}_{k}\\right)}\\:=\\frac{1}{1+\\text{exp}\\left[-\\left({\\beta\\:}_{0}+{\\beta\\:}_{1}{x}_{1}+{\\beta\\:}_{2}{x}_{2}+\\dots\\:+{\\beta\\:}_{k}{x}_{k}\\right)\\right]}$$\u003c/div\u003e\u003cdiv class=\"EquationNumber\"\u003e1\u003c/div\u003e\u003c/div\u003e\u003c/p\u003e"},{"header":"Results and Discussion","content":"\u003cdiv id=\"Sec12\" class=\"Section2\"\u003e\n \u003ch2\u003eDescriptive statistics\u003c/h2\u003e\n \u003cp\u003eThe survey involving 410 mothers across seventeen villages. Over 93% of the surveyed mothers expressed concerns about the inadequate standard of maternal and child healthcare in rural areas. The respondents\u0026rsquo; average age is approximately 27.83, falling within the reproductive age range (18\u0026ndash;49) for woman. The average monthly household income for surveyed women is Bangladesi Taka (BDT: local currency of Bangladesh) is 33, 307.52. The mean distance from respondents\u0026rsquo; residence to healthcare centers is 31.36 km. Most women have complited primary education (mean value of 5.74). The average family size is 11, with a significant number of dependents, including children. The smallest average value of farm size 0.25 suggests that a majority of respondents belong to poor households. Table \u003cspan class=\"InternalRef\"\u003e1\u003c/span\u003e outlines the brief descriptive statistics of the variables.\u003c/p\u003e\n \u003cp\u003e\u003cimg 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5UYYpBs7yRw7nsldSbZ5Was+y6/KhToS88W9wsXkdaEE7vP2wj32kZjnuYS0EKdkH1F6ZFQOEfKvtoTcVC4leJ7LwhhjRskS/nQ6KGMWBDpCxRlhY8z4w7Ex3n/h3Y3FDEfDeE+G9zWGdZTLGGN6MfIz+sYYYxYvetl9Nh8OmATuvffe9AKzlXxjzFxiRd8sGHjhk6/4cG3z0rExZjx4+OGH08vXo/rE67jDrgYv7vICszHGzCU+umMWBHzJgs/fRbZs2eLvYRuzgOALTXxVqZ/Pgi502Mngs6ruq4wx84EVfWOMMcYYYyYQH90xxhhjjDFmArGib4wxxhhjzARiRd8YY4wxxpgJxIq+McYYY4wxE4gVfWOMMcYYYyYQK/rGGGOMMcZMIFb0jTHGGGOMmUCs6BtjjDHGGDOBWNE3xhhjjDFmArGib4wxxhhjzASyZGeH+ndX/vjHP9a/jDHGGGOMMeNGrq+3VvSNMcYYY4wxCwcf3THGGGOMMWYCsaJvjDHGGGPMBGJF3xhjjDHGmAnEir4xxhhjjDETiBV9MxQ+++yz6tZbb62WLVtW2ywe7rvvvpTvH374obYZjPfff79asmRJuhqzGKDN0HZoQ2Y4zKY/OvXUU5MxZpKJ+spiGG+t6JuuMFgwcBx11FFJCcXw+/nnn59+JrZt21bt2LGjvhtvSP98Mw5pMIuLN954ozr33HNTO16xYkVt2wztW+0e40noaPj666+rxx57LCkeyLlX36AJksrFyvn44bY2XJCn9BDq/pVXXpnaAQp7Tq/2s8cee6Rrv/oK7SyWUTSkiee040Em2SOFz2saU2Lr1q07ly5dunPlypU7N23aVNvu3NlR6HeuX7+ez7Kmq5DduLNx48YZ6Z4PxiENZnGyZcuW1E4x1MMmtm/fnto/7lavXl3bmlGyatWqJO/ly5fXNmXU12Lop8144rY2HNA/kM2GDRtqm6kxFJnRZiJtx1a1IcqoH9B/VKYRwlmzZk2yJ13j1C69om+KsLV14oknVp0Bp3rzzTer0047rX5SVQceeGB14403Vp0GVX3zzTe17cKAfLESMJ+MQxrM4qYzeU/X2267LV1LPPnkk2lFEo444oh0NaOHsmF3tGlVktXCl156qeooOOn+8MMPT1cznritzZ6HHnoo1ffLL7+8tqmSvN599936boq5GFvRf1SmkeOOO6565JFHkl7ETgH607is7FvRN0VuuummVFnvuOOOap999qltZ0JDO+CAA+q7meicKIYttwjP2MrUdlfcemMLW2fnaCRshfFb25jd/Ip8iw8/QNg0PvJF/niuZ6A0Y49/Og0gHWzHES/pIE7cYIcbpTeCvdLJlfCgKQ3aulcckab8CKUHo61DY7pBm16zZk2jQill8vzzz69tZhLrJIa+QIMa9VP2GO6p07rXMRPcEzf3qtNqa4SNHfGoHamtE5bs8Kt4FT5G8Dy3wz/KAG0Jv/zmOWHSbrFTm8JNr2MAw+b6669P1ybFEKVQbnJK/ZHyK1nl+RXIWu4EeadsscOt5Kmw6LdkF8OLdUBlG+uA7NTf45fn2JP2KHfqA/f4Ux1YSMy2rUFsB1zzcJCbniOrKCfJWHWiJOOFwFdffZVkFWGSe9JJJ6XfTWOroH6pz0JWyHxQmnQioL2sXr06pYO2Gmkqx9g2MLENluz6pl7ZN2aapq2pXmgrjO01wmA7km21uA3NthputF0mPzoaxHYXW5fY8Qx7HR3q5Rdww7aZ3Kxduza5wV4o7AhbbhjSTNqJk3B0r7hwQ3zkCzviUXoj+FW6cMN9JE8D+VYcSjv0yg+yJi2kE0P68rwZE6EuUWfUzqnrOdQh6hZu87oqf7KTG+qmkF2s97STuPVOOLSRGBZ2uMGONKrey476rTAURwyz1BZzO/yRZ/lV26GfwpAP0oEdfvO2O0qUZ/JJ+mLfBkon4DbPK35L+cUPeVb/JbnjFghX7gg32uEOe/U59FXY4Y5ywx3ywo3CA7mLdSe341555Uo4oDxQFqQB8IMdfhYKpB05kS/S3m9bA+SOP9VJyUty4Xm8l5xUd5BXlCfucUt5zWXdng2SDelVPWwCd7kMVe9i30FY2Elu/VBqexG1L2Qs2pZjXkdwSzhcB6U5pWbRokalDr8t6mBihZSdoKHFzgW3PI8NU37U6Ys2fvOOIG/gkPvBTWyQoIYaOwbuS51MnkfgPqY/95enARRH7Hh65Ycw6DAipTQaI6hfatv5YANxYFGdzNsLdlwFg1PeX1APcSflIq/vIg+/FCc02cV4eY5dpGRXGqi7+Y3yGSWki7ikGOYyJT0YKOUBmvKbKxDY5eGX7ErpaPKbx8u90ityO6U3ylh1oGSXhzfOkGbJaZC2BowBcSzRuKdJVT9jKs+E7BYK9DfUYdKMzJrGuTzvgPt8nKRfwm0sj7aU6npEZRnd9CpHINxcFyGfsx3TfXTHDJ3Stpa23Dhj9+OPP6Z7tmSPOeaYZF+Cs3CRXn7Z/mK7bL/99qttprb2OvV8xtm+nLfeeittq8ats9NPPz09i1vbEMPuRqfxVqeccsr01hzbef3SJj/HHntsioMtd7YuYZC4zOLkhhtuSNe77rorXeGdd96pLrvsssbtadVBrtQ9tpQ//vjj+ukuqIedwbW65557qtdeey2912PaQd+H7Oib6AcExw0uueSS+q4/SuVJ+GZuGKStaQzoKH/TY9N//Md/pGdqc/2MqaV4Yv0aZ+hvPvroo3QGHs4777x0/CUfo3N4zvh+wgkn1DZT7LXXXvWv0dOmHIHjeqQ1Hqmi75ztmG5F3+yGPj314Ycfpusw+OKLL+pfU2cuDzrooOrbb7+t3n777dq2HbPx243OTDopL7nh5ZpBoKHSodNw6Yw4mzcKeAFIHQWdCAp/fo7RmCZKCuWf//znnsokbqnXDELUcdpPiTvvvDNdaauul/2RK4b0Keecc06jUmjGm0HbGpTGJs6si1GNi+MISi95R+FngsO5fC1ylfjll1/Ste0i3TD4+eef03Vl9tJur3JkPGcc1/s51JNhvJxtRd/sBjNnKhszUHVIw4KXY2655Zb0tvzdd9+926p9N9r6/fzzz+tfu4gz5BJ0viVFZDYKujokVmzYIRg0rF75QQ5MSLZu3ZpWPGb10o5ZdESFso0ySZ9w/PHHT7tncGriqquuml6Bu+CCC9LVtCNXDNsqhWZ86betidLYIbvZjKkLhdJL2IyvzzzzTNJTXn755dq2mdI4OiqefvrpdKV8I93KUVA3WNXH/rnnnqvOPvvs+sngWNE3RficFVx77bWNK3HY9/sVhM2bN1dHH310mkz0Sy+/hxxySLV06dLU6cU0s3X3008/1Xe7o9XI22+/PV1FqVG2Jb7tz5EF4pBM29ImP6zk6BlyefDBB9MK/7AnaGZyiQolK0m9lMkPPvggXXttJ7O7RN3VgEz4c/XVlNheurX9cUeK4RlnnLFgV/Oj/GO5LEb6bWtMohkD/vCHP8yQHSvY//znP9Pv2YypC4VPPvmkeETn4IMPTtdux3D23XffdH311VfTdRh0q8dM4F544YW0WKrybVOOgv5SbmEYEzcr+qYI381nJQ6lkTN/VF5VUK7cs1p33XXXJTv49NNP0zUqmerktZW19957pyNBNFrCQVEFvscv5Vjh6Jno5ZdB8Oabb04zfNKMHYoFn9qKs2IaEZ0joMzTQXKm/tFHH03HEfCHkoJirv8foNWAv/zlL+kaUXpjR7Ru3bokI6Axs7Kvz4BBngZQHLq2yQ/ypRy0dYkSRthMEowpQf2iPsYBR+985OeF//a3v6Wr6jhoUFX95qrwaJPURdoPSPmgHbGNzXn92K7VZmL43333XbpGBbHkTunnqt/7779/ujJppx+ivbA6BqSTtEX3ukKpHSsNStMoIS3IMa48MsjTN0FUCsmHjlbG9EKeD4WL+5jfXAZyF+uG+pXcX+4Ocj+AskO/Sh+H/PW5Qfo+jRP6XywaIyDvC0HPF9L/bpltW4N77703jcMczaE+Y3j/S/WhnzG129g87nBEhzoU6xltg/Gu2/hOG1q7dm2aXOloK0ZuKCP1C3xytNeOOG7juXqB/Amfdwfo61555ZUZ5durHCOM+7jt9snVvthpTBd4e3zNmjXpjXGqC4Y333mrPb7Bz9vieo7hecmON+c7nX+65613wiA87HhW8iN6+RX4UXp5oz2mE3iDnWfEpWdcCVP+yLOeER52MvgTeXr1Bj/pUlhcCTuSpyGPI+a7W3744gJxyR+/oyyMiaieyMR6Rl3sKMX13e5uMdRv6p/qvfyo7VCvY11WeyjV7/hlCgxh5e5KdpjcL0bQRrhXv4B/wiFtkPsrhSU/ud2oKOVRkD76I1FyS1ohT3Mpb3NlB1zVd6kPVH9IvSnJOLfjPs+z8jvOxPRiYv0h/W3amqCf19iH39jH9xoXc3mWZBzTNo4oX6RTeaUe0S6iHKE0vgN+VRfxR5jICfe4wxA2/UcTudyiIWz8qp8p0a0cI6SF58NiCX86kRpjjDHGGGMmCB/dMcYYY4wxZgKxom+MMcYYY8wEYkXfGGOMMcaYCcSKvjHGGGOMMROIFX1jjDHGGGMmECv6xhhjjDHGTCCtP6/5xz/+sf5ljDHGGGOMGTdyfd3f0TfGGGOMMWYC8dEdY4wxxhhjJhAr+sYYY4wxxkwgVvSNMcYYY4yZQKzoG2OMMcYYM4EsOkX/jTfeSGYhcOutt1Zff/11fWeMMcYYY0x75kTRv/LKK6v333+/vps/SMeee+5ZnXbaabXNeHPddddVa9eurT777LPaxhhjxhP6+CVLlhT7+vvuu69asWJFes617WLLDz/8UJ177rnJ37Jly6rHHnusfjIT+sijjjpqOvxxGG/mE/LPeIfcm+AZMpVsu7mNuEzag6xYsJOckcPzzz9fP90FbnheMiX3EdqS2haGcifeSJ6OU089ddHpFbORMfRqU7MNf6Twec1Rsm3bNj7fuXPNmjW1zfywfv36ZBYa27dv37l8+fIkR2OMGUfUT9HXb9mypbadYu3atTtXrlw53Ydxj7uNGzem+yYUJmMHv7du3bpz6dKlu/Xj2BPehg0b0j3hltKxWCDfmzZtSjJoGvPyMuGKrLHvhsukP1avXr1z1apVST7IFllFuQjJHncyct8Nyhk3kitlQpyUbYSwiBN3hI0f4lxMDCpjaNOmZhP+qBm5ok+HQEYR0Hwpq8RL/DSChQgVhs7CGGPGEfp5FIxcmVPfyyAZQRFhXOjWJxNm3m+jrORjCQNsrtiQlsWmyOQgpyalBNnnz9qMMy6T9iCPXKHXBCjKgbaBfQ5+kXc3NJGI0P6IQ5CO2CYBBTS6mXRmI+MIMiu1qWGFPypGenSH7aK33367ev3119P9/fffn65NxK3EaNiWEoTJ9omesYWYb1PlEG+nMVT77LNPbVPNCB8jSIPs4hZNTBvbkXHbS2nS83x7Tls+2i7jOQZ/upc/ws4h7W+99dai22ozxow/9HWHH354dcQRR9Q2u3jnnXfSlSOTkXPOOafasWNH9cUXX9Q2u0O4eb/9m9/8Jl1ffvnldKVP7CgyKbzIiSeemOwX+xGeJjqKZvXSSy/NGDu/+eab6sgjj6zvyrhM2nPggQdWl19+eX03Be0E+f3444+1TZWOEmOf88QTT1T//d//Xd+VoRw//PDDGbrBd999V3UmWfXdVDqOO+64+m4K2l5nklDfTT6zkXEbRh3+bBmpov/kk09Wd911V6pkVO5HH3208eVSzjetW7euevfdd5lmVp1ZULLvzIiqr776Kv2mU0JZRqC46cxS00TiggsuSM9L4Id4TzrppNpmCjocGgls3749XeHGG29M5+Ix/AaUdDrBL7/8MvkDOi11krfffntKx8cff5zCopGdd955MzpRnnOPwv773/++Wrp0afXTTz+lcB566KGUn6eeemo6/IgqUNNZSGOMmQ/oz997773dFBpBH1fi2GOPTdcPPvggXXNQBlFGcsVTfeHmzZvTlf4UDjvssHQVhx56aLo2hb/YefDBB9N4pcUnjS133313upZwmQwPFii7QbtC1r3eJ+Q9PkCP4Kw+ZYQe8eabbyb7HMoafYbwH3744dp2cdJWxoMy6vD7YWSKPkotKwaq0Lfddlu6Nq3q33PPPcmtOo1HHnkkXV999dV0BVYMWPHWoMIEgglBt9VurRjttdde6SqY5dIgQKtO4oUXXphuQISLkk56WMXA3x133JEKUCsYzM5R7nmGm4suuijZK27SyWo9fpg8kP6PPvooucfu4IMPnnanfOcwKcGPMcaMCygNd955Z323O/vvv3+65sodq46Q98s5vZ6LfMfAdIexhoUyFpZ+/etfp3GuaezJcZkMDnoRC5c33HBDbVMG3aLNijv6BhM29IPTTz+9uvbaa6tnn312xo6LYBJAWbPwyS6A9JfFSlsZD8qow++HkSn6rOZff/319d1Ux6JV/bjSLVjhZgaUgzIsUPrxr6M1GCYI0LQFrAFGqwkRpUmTEKAxnHLKKdMNhUkEnWGMkwYFmlywnYkh/exMXHjhhck+B2U/h4pAfPiHppk+fmnQxhgzDtDX0XeWlApx8sknJyWE3Vr6VqDf1CKLjn2YuYfJFgtlGpeZtJXGZjM8dMoh6jUlHn/88ep3v/tdfdedX375JbUxTiFol6akS6HvcHJg48aN6ZjxFVdcsWA+NT4K+pHxIIw6/H4YiaJPZ0HHzvGVqCBrS4/KnsNqAs81GEjxPf/889NVrF+/PlXW3PTaCmuCgQpFXvHRCPNtaDrCUpxaASG/DHqs6rOC9cwzzyT7NhAvcZIOlPnF3PCMMQsD9dMoD91gEvDKK69URx99dHX88cenPo5+nlVNlBPt4Jq5hXHntddeS0d1OObBuIqyf9VVV9UuzLBhgot+0EtX0QJim7aB20svvTQdw6EsN23alPQZFg+bJm3EzykFePrpp9N1sdGPjAdh1OH3y0gUfRT5m2++eTfFmPPrrNwzCcgrIZWP1QWUXiYFKL5U2nwg4ThQDmFp4OmXuKrPLJiw8sJhYCo1GinlvCNAw+EMP5OEfrcsyTsD32WXXZZ2C6zsG2PGGfppdlPjQs5NN92UnqHQs6oo6E9RJhkD6Oc4n48ywrtJTdAvM1bo3LdQP693rui7IT8apHu9C2Bmwuq9jpgCR0p5H45jq00KostkcFD8vv/++1YLki+++GJ6j68N995774wTCJwH5z1H2tc//vGPZFeCHQXKiZX9xUg/Mh6EUYffL0NX9Okk2LK45JJLaptdUBmZAHAuPV/V56s2BxxwwIwBIX+J4ayzzkpbU1p9Fw888EB1yCGH1Hcz6fXSF2hVnwYTjxuBOi1euI1EZZyJAF8Y6LaF3UT8sg+dLfGVBkDkygqYMcbMN6wAc8Y7mvgBBZ6XQOE544wz0jGDXi+poRRxljgqnurHzz777HRlEkG/mCuf3GPfa8dhscIY/PPPP9d3U+h4a9ML1OAy6R8p+W1fymRnhSNvbfj3v/+dyjKihcq8fHMowxNOOKG+W1z0I+NBGHX4fdNRqocG39btdPbp+7nxO7sRvjdKtEuX7vonD9BRcJMdV8zq1avT90rjP1UhzE5Hkfzjhue6dgP3fDe2G4q/BGkhDOImLvKIe4E/5bkzYUjPcc83VDEx3fm3VvGrPOJXceTgl3CNMWYcod+in4r9usAu9os5POdZ7PvoN+kf1e/hhvu8f5Rf9aOE35SOxQJjCTKI41SEMQ1ZSka4xy66d5nMHvIrHSUa5FdqB+hHTWVWKg/JVWFRPjynTPgNCpOywA5D/CrDxUa/Mha92pToFv58MVRFn8whCJkchBefYyRQKiFKLhW0yQ2oQ8K+1MGUoEL3EjwNpWkyQMPgmdJGeGpEQNp5hiEc0shv4sRdzAsmdnZMEBQu11Ia9E82FlsnaYxZOKh/j/2UBk76dvpN+sYSuFP/GaHv07hCGPlzweCqxRT61MXcV5bG2Vwe+ZimsSeOay6T2UH+Jf+SibIWtBFNjnKayoN7yRmDfhTbGeUVn1N2TXEsBgaRcZs2JbqFP18s4U8n0fMOL7PyScv8+AtHZDjK0vRd2DZw9r5T0dM7Ak3Ha9iS5Mxpr7fh5wO+ccwXh2YjA2OMMcYYs7gY2ec1+4Fz6p988klRCT/mmGNmrXzjvzNDS2f5S+hTVOOo5HOOjv+u1vYbx8YYY4wxxsBYKPq8/MMLrazq8wY/yi0vsLCaz0uw3f4hS1v4Gg4vrehFXuLhyxB8fWD16tXVLbfckuzHDSYnKPrjOAkxxhhjjDHjy9gc3eF4Cgqt/inUypUr05ds+HrPIF+zaYLJgz5/ydcf+LwU/7xlHL8EgEx4c9tKvjHGGGOM6ZexUfSNMcYYY4wxw2Msju4YY4wxxhhjhosVfWOMMcYYYyYQK/rGGGOMMcZMIFb0jTHGGGOMmUCs6BtjjDHGGDOBWNE3xhhjjDFmArGib4wxxhhjzARiRd8YY4wxxpgJZNEp+j/88EP1/PPPV6eeemp133331ba7I3dHHXVUV3f98P7771dLlixJ18XIZ599lv7bb798/fXX1a233pr+i3G/4A//xpjdUds699xza5uZ8J/E6bNK5sorr6xdTUGfSRuNbtr0nfgjftzjv6mPoD9esWJFckf/XWrXbcMq0SZ8Y4xZaAxV0UeRY9Cgo8T06iil+MoMS6HOIR1Rud5zzz2rt956q75rBncff/xxfWdmA4MoMr/88svTPQNpLPsmQ5345Zdfqh07diTTL9ddd121du3aVDeNMbugX/x//+//VS+88EL173//u7adydNPP12tWbOmWr9+/QyzdOnS6bYsHnjggd3cXnLJJfXTMijmxxxzTFLKt2/fXr377rvVLbfcsttYgMLOxOKpp56q+GfuBx54YLVy5crkX7QNq0Sb8OcKyqXb5EuQr9hXku826dWiCX74XfKD3bAmXyXoj1lEwx/+mxa/5nvyRbqUTuTQVJeQj9LJlQlyG9qG36Y8FE7JtJFbPiZzH4lpxdBe5ro8+qVNGxm0jrUpE9wgJ54r/HnRRTqd2tAhWEyn069tyqxatSq5W758+c5Ox1zbDh/SsWXLlvpuCuLtDET1XTNt3ZlmkD1lHVm9evWMMkHGyDrabdiwYVr2ej4I1C3q2LZt22obY4ygbebtE2gvGzdurO92sXXr1tSeIrilTfcLfTPtOvb/tHvs1F65ct+ZsKd70ZlszBhj2oRVom34cwFp2bRpU5JvqUwE7nhOvyiDv15QRp0JTPKPnPidx6P+krzzm/JGFuqLBbLFXn027rmP8i9BeMgb/0Ad4z4fowcNf1iQTuJErhjkRDrzNsE9dYd08hvZ9apz0Db8NuWBHW5ifcBQ3m3aJfHjLvolTKE4ySfPlFbinKvy6Jc2bWTQOtamTID2JTcY0oO7KNu5YGSKPhnsVtkRLALADZkfFU2dCHZ5oZRo6840Q4PIG1guU+5L5aROT88HBf+jrGfGLFRoF/20DQb7XCmOfTmDZ6+BUjDo5XEzCMZ+l/BKfQOKSewT2oRVom34c0mvMkHe/SoLGgtjXyz5ROVSZRnLUDLSeM6V+0EmR4wH6AcRZI29mE34w6KkcJOmvC7l7pAv7vL6lNM2/Dbl0dTmkGseTwnqWsm/IE35c7WPNuHPB73ayGzqWJsyQS65G35j161tj4KRKfrKZJPAyKgEXco0jUSTBQk+Cozn2KmCSvB0FipcNbho1Pj4TeXFLWFwX0qH3AFXhRPtIdqXIK9UBNKnNJDuaEd6+U0YeeUD4tNzrrnyTBxqfBh+x4oumSHPksywI17scFNqwKSBZ7ihfLo1JFA96IVkK9nk6LnQvUyTP0E6cdcrvcYsNuj3Sn1fE/QXGswE7ZH+Rn2D+pRu0GZxW+rrsFea1D/H/h9in9E2rBJtwp9rSFNTmjVuImP68Lbp09iQQ5nFuPJ7UP+JTEBKTR53UxwiD0fk4Q0a/iiR3HvVa8Y8yiavT71oCr9NeZQgftz0SofGaMZz2k/etpugbPBHWY0bbdrIbOpYmzJpCgd/vcIfNiN7GZezSx0hV48++uhuZ6I467X33nunc5AlON92xhlnVA8++CDSqF5//fV0jorzTTGst99+O93ffvvt1X//939XHcW36hRwde+996bnp512WtUpxPSbK2Edd9xx6R4+/fTT6osvvqjefPPN5Jcz5MTTxI033pjOnUKnQaR70WlM6dwq8ZfgnPlPP/004zn3snvuueeqf/7zn+mMaKeCVPfcc8+Ms1yc83rppZdSnomLM2Wnn3769Hkyrp2GWp144okpn4SJ4T6G89FHH6Wz7i+//HJ15513prDgt7/9bTpjy5la7E455ZQUZ4T7b775pvryyy+n80H4eflG/vznP1edil3fDQ/SSf3qdFIpvbFcSxx++OHp2nTW1BjTG/pu+rm876YvpO/88ccfq84Amq70Kd36BrHXXnvVv7qzzz771L+aaRtWiTbhjwP/+te/0vhDOTC+Hn/88emsfS94F4MxIufoo4+efmeN8mV8OPLII9O9UP+5efPmdH311VfT9ZBDDklXccQRR6Qr4ZRQPIcddli6ikMPPTRdP/jgg3QdNPxRQB2mbjMuM95IFjmMwZwJZ8x75ZVXWtenbuG3LY8S77zzTnpnpk06cEc86B3UkW56UM7JJ59c/xof2rSRQetY2zJpeu/ppJNOSte5rMMj/erOXXfdla5PPvlkugrsr7766vpud6655ppU8aS8cUV558VYFFTZoezCI488kpR6DEplk4BzUBT1whN+4dtvv03XJhjQiINOM6JG1TR5oRIce+yx9d0UuJUdExXCJl+SDZMDQFGnshInfmi4Z511VnomN/fff3/Kj16Qw90TTzyRKqQmPoStRo87fmMuu+yypLjffffd0+FT2fGrykgamGQga57j7o477khuVCYlKDMmdcOEtDC5Iz2Un/LUC+TDRMcYMxgsSFx66aX1XRn6lmeeeSb1KfSLZrjQj9NX05chY8YjFLQ2ixht+8phTr5K8KGLNgwa/jC54IILqvPOOy+NZSzENb2syfhy0003JXcvvvhiq0kutAl/kAksYaFX9IIxlHH9q6++SguivDhKeuICYQkmZSjT6ALjRj9tZNA61qtMJJdcoWexFPbYY490nQtGquhrVX/dunXTlV6VmIIogVAomF/96le1zRRnn312umoWJkqF1OaLOlAqKFbYe3HbbbelNMZZL42q1xcmulHq+LS6ofzEBsVgysq9ZpFMBHgjPsIzZuf5pGQQSAN51tvrGHYUoKlDUAXXDHkYsNuDokHH1G8Hw8SQztQYMxj0eW1W8Fg4oe/ptXBiZgd9IDvSyPpPf/pTbWuGCfJl7GMhj3GQlfcSjMcoylIq2SFvQ9vw+wE968MPP5xewGwLehkLaKyEd5s4os+xcs1X7cad+WojWnS99tprp/Ve9BfJV7rbXDDy7+izes+qr1b1WXlGUe5FrnDP1cz+k08+qX81Q2OgMSsfKLTdjiLNFWyX5wxTbuSZziw3KN1zBcebUNZ9BMeYuYVBiiN9bfs5+p5uq170owx4+fEDLRBoi1u7l/nKmPwRTtuwSrQJf9xhdxVl0YwG6jzjHMo440/T4hZ1BaWSBU4W39rSNvy2sJOm0wr9QlpIR9POBaDPPfvss3Omlw2DuW4jKPIcCUf/pT6wEPu///u/KQ2Dls2gjFzRj6v6VF4qT5uOs2klaNjHQAYlruqznX3RRRfVT0ZHqfHHXYWmSswAOAxYbShtR7b9ZvAw4HgTndAVV1zR1zlCY8zs4Jv6Z555Zn3XHfoJjHZim2B8YOUx9ivayZRf7SDIHnCPP/oC0SasEm3DH2fYEe7Vz7NSrN3hCHYs4kDbCdOgkyPFE2UNutdR1nGdfJ1//vnp2uvYBcdhOQLTL3n4bcsjh2O7WlEeBCboTboWi2ycXlhISj7kbWTQOtZPmbCjwpEoFkQ5RsQCNn5vuOGG2sXcMHJFH7Sqz4ubvVbzJURmw7HD1u+2A82oIZ1a1Wcrpt8tsn5Q58f5vwgNTi+S0ImzEpBXWuQ2jNmjOmjOxke6KflqKHmDmC2sfJAeXg7uZ+UDWTDpNMZMQZvA5ApyDs/o50rHduiH4j8JYjHnqquuSspGVAb4mALKT+wz+CAAqF+h/2JRiI8eaOeAK/daLCItci//0CYsUDqU37bhjzOff/55dfPNN9d3ZTR2xjFCvy+++OJ0hWFOvnJY5aQPzscE7rHXmDGuk6+ff/45pTPWpxIodOx+9Usp/H4nsNRh9K3ZHA3hQyWl9yhp68S50JR8yNvIbOrYIIsKLEyiw/H+Uq/6M3Q6M42hok8MtflEFJ+HxG2nYs/4BJQ+99QRdrLH8Htl+PYudvgj3OgXN/iVXfzkEXZ8Skl2MT08wy7GIXcdJbq2mQlh8bzt56X0OafoXnakT0gufOpKkFbsuOIWeWAEaUUWpH9b/XkswsZO9+Qxlw+QP+ximRE3dpSFkDvkrjTkZZrD8yjTEjFdTbIkLp4jG1B5kT/KQRBfXicE7qPMjFnMqP+KJvZDEfqBprZDv0GfgH/aHu7U50Ro47jL+wz8Y4d/njf1AaSN8HFHX1SKo01Y+OVZ3ke0CX8uUH/Y1I9hT/8c+/k4VgD9JO5yWXNP2ISLf37nbniGX5U39YT7vG5IXsgcP7jnPqYZN8gz9tGqdxpbSH/uBtqEP0qoA8RJ/ED6qDe6B2SI/EhrLA/JWJTKo034QDj47VUegrrQ9CwvD+IiLNJMPBj8x3EfsCd+hR0NeYp5HQfIU682AqQft8hBeeQ+5gc3ef3kOe56lQnuKHtkxPMYxlwyVEWfzCAQmVipEUCsPLlbTBQSwqHSY4+AKKQo/NwvAizZAYXBPWE0uWtjVyKvFE2osshwn9shk5IdEIfyIXnkUFmjXPPBSvYypTwSf1422AFpIF7ix5709Mo7ZY7bJkppyN2X0pPLSWlsGsSRDe6Izxgzf9BGTZmmPjlCH6xnKJS5UgaMnzynL4x9XmkcKfXhcSwhDBSlEqRN40E+3gD+eJ73u3F8Jw9N/XKv8EdJlDNpRW55/Mgujk/khTTnMi2VR5vwRdvyAJ43hZOXR55+ZFwqC/IlN7khH+NGmzYiBq3DvcqEcBV/qU7MJUv400mMGQC2yNjKmsuXURcibOs/9NBDIz3e1AvKiS828aKUMWZ+oB3yzXQd0TCjhe+6d5SROf3Ch2nG5WHmgzk5oz+pMGjN5oWXxQL/POQPf/hDfTf3cI6O88KekBkzf1jJn1vo9/hmt5XK8cDlYeYLr+j3CS+A8rIZ/xVtW/3VHdMb5PTee+/Ni7LNf8T73e9+5w7WGLMoYLf573//+4J9cXLScHmY+cSKfp/wtjX/gGrt2rXpn0W40baHzu6LL74YyleA2sIqIm/Xz/lb7sYYY4wx84wVfWOMMcYYYyYQn9E3xhhjjDFmArGib4wxxhhjzARiRd8YY4wxxpgJxIq+McYYY4wxE4gVfWOMMcYYYyYQK/rGGGOMMcZMIFb0jTHGGGOMmUDGRtF///33qyVLlqTrYuGNN95Ihn+NzT92WrFixcjyT/iLSbbGGGOMMYudoSr6p556alLWS2bZsmXpOQoniu2weP755+tfC4srr7yy2nPPPavTTjut+umnn5Ldtm3b0nUUXH755dXf/va3BSsvY8xkwnjAf8vWOMEYYYYPCz2MO/fdd19t045bb701lQvlw+/S+M1/PT/qqKOSm1EuWC1EtIgn2bC41wTjM3pSP/LrJ3z4+uuvUz2Qn0ktq2H0K4SB31KbQc6SIQaZ5m0D2apdyA3yn2uGqui/+eabM5RV/umuzOuvv14deOCB1RVXXFEddNBBqWOIHHfccckd17YgsKeeeqq+WzhQaQ444IDpvCKXQw89NP0eJXfffXeSV6+OwBhj5gIGxmOOOSYNptu3b6/efffd6pZbbulbGTXdQeH4+eefq0cffbS2aQeK0ttvv119/PHHqXz4fcEFF9RPp2As//Wvf11deumlaQy/6667quOPP97KfgcU92+//TaNuxs3bkx2p59+elHZY1zG7VtvvVXb9Kaf8AH3y5cvT7/x06/OtVAYVr9y1VVXVTt27KjvdkFZXXjhhdMyJI4ff/wxTdIE7eKMM86oTjnllGr9+vXVqlWrUvvjPp8QjJxOIofOypUrdzYF3amM6dnSpUt3doRT2/YPfomnI7zaZmHQmQil/Od537JlS7LnOkoIf7ayN8aYYbBmzZrd+sMNGzYkO/pKM1yQa0fpqO+6o7F606ZNtc3OnVu3bk12PBMdxTGNxZHVq1cn+8VOlBMgS+TXNM73qwf0Ez7lztjfNuyFzDD6FdwrnLzNUL9z3VNlJ/CT61n4w01ebqNmJGf099lnn/rX7rBC0MlsmiU9+eSTyY7Zp7af4ioAsx4dB2JmFre0+M0qA7NfnmsmxSxK2zUYtk3i7oG2xpjZYa/wueazLGZt2nYh/nw2iHu2YhQX8faaqd1///1pZtdNRkA+FS6GuLEjPtIS40Zu5AU7tlaxww15zdHs/eWXX05XY4yZL+ij8v7wN7/5Tbq6j5pfXnvttXTleKk4/PDDq46ymFYygXGHXfxzzjkn3YsTTzwx2S/2VX10ggi7KqyoH3LIIbXN7GgbPuVw0003pZMVk7iCnzPbfoV6jTn//PNrm5kg4w8//DC5Ed99913VmfDWd1V144037qbnXX311emq49pzxby8jHvRRRel6+OPP56uv/zyS8o4HUOEbZMjjzwybY2g1Ecl+qOPPkoFieE5x4bgt7/9bbp2ZlIpPCYUbCkC/jkXz+Tg008/TZOL2267LW2rYBcrABWFrZkHH3wwhd+Z2aWGIuWZsJgc0PHxvDObK25rRvDD1s1JJ51U2zRDYyRvpI18UGmAfJMn0nrnnXemfAL5fuCBB6pLLrkk2bE9xESgxNFHH1396U9/qu+MMWbuQfmgL6OPj9CnwubNm9PVzA8vvPDCDMVFMH7oeImuhx12WLoKHUX94IMP0nWxw2Imi3V//vOfq1deeaXnQl+/9Ar/4osvTsop7+mxCMhiIPpLVFQnhWH0K/fee2/Sr5q47rrr0pUJLQvCxMnkV3poL04++eT619wwL4o+yjZIsacAjj322PQ78u9//7v61a9+lX5zjp2Opxeck0L4VHT8sHvAJAGw0+oElf6RRx5JCrWU6DjLQknWc/jd736XrnKDos1qPy+5Au6YDNDxNTWeL774Il332muvdG2CCQHxMwkhbeQDiEMNmHj5jbnsssuSLDmDj1vsjjjiiFTZSysquMF9nDgZY8x80Ks/NPOHxpteaEw3ZdA3WChEF3nxxReHPvZ2Cx99hPGenX/0GHQkFiZZkWaBcFL1gEH7FU5FsPLere7zDFkjd96JuPbaa6tnn322Z3th4rt27dppnW6umBdFvy1nnXVWUniZqVIZEY4U7yaoxCjBKLj4veeee+onMylVAs30NCPcb7/90j1o5V6K/auvvppW53W0BqO4pNDnaHWj24u3n3/+eZpp33DDDT3zOii8CAxN6TTGGGPMcNCuP7v06Ansvg+TbuH//e9/T1f0Ka1qo1vcfPPNaQLwzjvvJDszdVwb3bCN7sVJFBR9FHeUfvS2ppegAR0WHVO7AXPJvCj6nCOD0rZgBKWaM2UIhy/15GfkSyBoBM4M64QTTkhHX0YFYdPAcpOfm+sHdgyoNFQeY4wxxix8UB452oFy2O/Xj9rQFL5OIeQLjDpFwVd7zJQi/tBDD02f8OgGuyQcCX/44YfTSYpNmzalSVO3L+rwTmqbVf9RMC+K/tNPP52u+Qs8JVR5n3nmmWrdunVpW6UJBMzkgZV/zrLPRuEGVtdz4guuL730Uv1rF6ShdFymLTS+jRs3pmNKTWfsjTFmoUPfzoud+ZlZ9Z9t3mUyo4NjrzqDH8GOlWPQNT+Lr/vSkdzFDkdtOSc/KvLw9f5E0/sSk3Z0btB+BUWcuh1PafCZWOBYFPeCM/wo9VLaORLOJzxR9v/xj38kuwjvg/L+5Hwo+TASRb9pRgMoyiixzDrJeDfiCj6CZKup6SgOcBSFIzdNb0q3hTfWqSh8dzXmhVmcZsdsg7HyHhV/YMus6Y16dXpNDU4wQWG3gFl5Hv4wUB723XffdDXGmPmAvo6zwrGfVf949tlnp6uZH84888x0jQtX+s3LncBREMbyXKniHnuULjMTxl+UxFGRh8/35NFn8oVJnayY6xdD54JB+hXsOf4UzYYNG9Iz3r/kXvD+KLpmRMeiJFeBkk/Y86XkJ3YOmc6MJn0nNA+a7+/qm6R8c5f7iL5xylWsWrVq+lukmPzbpdx3KnB6xvdj9Y1f4oFOwST32PE8uonh4F/pEsSLXaezSr/Xrl2b/OAWuPJMYeFG127gnrBy+K5q/oz0YCeZEKfslA5ADthFmRIOdqXvtZJO0m6MMfMJ/Rh9eOyzue/Vj5r+0dgcxz6hcsifcc+Yw3P88zt3Q5nFsUZjOfaLGeSFrKjL/AZkI3mWkB5Qqv+Ss571E77CVTtTWU5qO2vTr6APlup8JJe5UB2PuhluCE+y50r86GI8i4Y48zIaJUNV9Ek8mS8ZBIBCWlI8yXh0yz0gJCnTGPxH4aDYEi5upORKwcWOQqIwucdehRZNyU6QDsLHLo8baCxSsnGndHeDPOUVK88/z/N0lWRbSjth5W7zdLVNqzHGjBr6bvVZ9NsaPM3wyMcYDOOHYGxD9oxnESkruGfcYBwtKSiMsxqrUSBj2IsV5BTHYinWJflBaYyPIFPKICqX/YQfy2gxtLNe/Yp0Q5411VfpWCV9ifAkTwxtRxMuoDz0LDe0o7lkCX86EZs5gpeFO5Ujfet+PrZyOH7E50e//PLL+d1KMsYYY4yZRzgi3pkQTB+9mUTG+vOakwgvCndmgkP/vFZbeImEF5ut5BtjjDFmscIZ/m+++WailXzwiv48wRd1+PznbL8M1A96sXcu4zTGGGOMGSc43cD/GJj3F2XnACv68wj/nIH/KDgXXyaYy7iMMcYYY8z8Y0XfGGOMMcaYCcRn9I0xxhhjjJlArOgbY4wxxhgzgVjRN8YYY4wxZgKxom+MMcYYY8wEYkXfGGOMMcaYCcSKvjHGGGOMMROIFX1jjDHGGGMmkLFT9PnHTvzX2CVLltQ248Gpp56azHyBXPiPtvOZhvmAfGMWC/y3PsqZ+r9s2bLq1ltvrZ8MB8InTMKeK77++uvqvvvuq1asWFG9//77te0Uo2xXjz322G7xGWOMMYuJoSr6DOYoKL1MNw4++ODqo48+qu/mh+eff77+NT7wX20//vjj+m52DJq/uZYLEz7yfdppp9U2kw0K8Yknnljdcsst1fbt26s1a9ZU99xzT1LOh8m2bduqHTt21HdzB/HOJZdffnn1t7/9bSzb86Txww8/pP7/qKOOSn08v5vQONEGwo0TXyZvJWgjirs0oVzstJVjCS0M4JffhJVj+fdPri8h45JsBc+i+2iQeRP9tLfFRL9yaVteLFxFd2OxOMt/xh0mHQVl59KlS/lvu7XNLni2evXqnVu2bKltyqxatarof65Yvnx5/Wu8QC6Y2dBRtgYKY+PGjTvXr19f340e4prL+MYB8jsX9X6u4onQ5omzV9sfBdT3TZs21Xdm2GzdujX1+ZIz/XwTuKUetKl/hENf3Jnwpt+KJ+8XFOaGDRvSPX3VfNW1caStHEswXq9cuTKNG/jldz5+WP79o3GYMpDp1Uch39wPhjKR7HP6aW+LiX7l0ra8sKPNRHfENd+MpPQRSJMAafy9OoBu/kcNneG4NgrkghmUpo66F/0MDMOARkUZkN7FBPKdi7o3V/FEaPPEOR+DP3FSfxdbfZoLVK5NikYE+UtxbFP/1BfHciMe7OgjBEosYUaIZ1wXbOaatnLMkcIeFRopSDwTln//UCb9KoBN4y99W6kc+21vi4VB5NK2vNCtYjsbF0ZS+v0q6nQ6dAr4Qfj6DQoLIyVBikq0EzyTf675rCs+p4GsXbu2frIz/Va4MkAYFHSuIFOg+CEc3JL2GB+NT8+BuPmNiR0l4E8VD0NFzCsM8edpyMGPZEY8/JaMYvgYhUUFJj7Z406VmjwofzJR/hiheKMdRLnippdSUJJ1DBujPEnRUNhCssY+5geQEXHoOfUhlgdhKg344zkGf93k2wR+utWTmIdomuiVhjx/yodQ+ek37jB5W4HYNrniPoc6EusPv2N8oDzGdJbaFeXAPfEQhvLJlXxF8K+05SYf+Mhfr3pn+oPyQK6lOlGCso5l2gvCjnUD8I9fxZnfCymyvdrmYqCNHEuoTefE8Cz//qFvQjb0XbSJ2chIekOJftvbYqFfubQtL02MKQ/G+3wMmk/m9GXc0ouFnBXkTPJTTz2FxKtLL710xlneN998s+p0GvXdFDfeeONudsCZ7pdeeql6++230xlnzq2dfvrp6ewzcFb3pptumo7r5ptvTmeg9bLn3XffXXU6rPSb5xjOYHFOnDBzOHvFWecvv/wyxccZReLTmeBffvll+jw0ed9///2r119/PZ3tIq2C9OHvnHPOSXF2KlL1wgsvVA888EDtoj1XXXVVdeSRR6ZwONMfz5Dx7kOncifDc2QLv/3tb9OVPCi9lAMceOCB1Y8//ph+Ixv8IX+unYEg2QvCy+0o308++SSFjSG8n376qX66O6T30UcfrU466aTaZopnn312OuxO51Ydd9xx6TdX7skTbgDZfvPNN6lcVJc4+y5Z3H777ak8kQ9p6jTM6rzzzpshK55z/9Zbb1W///3vq87gltLdTb5N9Kon5IHw8rrXRLc08FtnApX/Dz/8cLqMI5TN2WefndwdffTR1TXXXFM/mYI6+8QTTyRZEBdyoP3kdRf5IV/cEB+G+27vFpDOvF3JDpl/+umnKX233XZbkgt2L7/8cu1y6iVtZHjXXXeleDudbLKnjnBPPYuQvz/96U/1nRkGtCO960Ffy3lUrqWX57E7/PDDk2kDZ7wJm3oekf/NmzenK/UCDjvssHQVhx56aLp+8MEH6bpYaSvHEoxBtO0c2pLkbvn3z7/+9a+qowimMYWx7vjjjy/qRm3461//Wl1//fX13S76bW+LhUHk0k95dSYCqb2hV9J2xub9sM6gOHQ0UyqZSEchSHb56jYzouiWGRT3cSaV22llgTCFVhV4pntWI0RH6UrP42oEv7HLIU8YodkbYQh+M+uLcSi86C6PQ2lXOgEZxPggT0MJnkd5Io8ot1IYpBfZCK3AR7iPcoKSrHI77pkFR/LyjqhcY3oEMiSteXjcs7IByJAyiPAshslKFUboeS6nPBzoJd+ctvUESvIs0S0N5DFPN3ntt06qbUqugrCwV11F9tTViOpzlLHKNZdVqT7ijjoYwY40ilL5YIe7EqSTZzHPZnZQpygD1RHqjPru2JdhH+tCt3ISqi+xzAX2qjOqt3m96uZ/MdFWjiWansfys/xnB21D8iyNed3QeJj3aYO0t8XAMOTStryo//SNuIt94Xwx0hX9TvjTplMZd1vtfeedd9KVFYLIPvvsU/9qj1YW4koeX90gbs3euGd1mpVDVguPOeaYZD8I7Ap0CnJGWvlNHpnR5V8dKOVJbkif0skMkBXZQb+wc9ZZZ6UVV94QJ5/IQ6vfTSATZEN68MtsdFgce+yxKU+Eq50Vvv7QhFaAtCIUQYbswjCrVljkkZ0KfZmHetBpjDPeemflF7TCTHowhMHM/MILL0z2OaUvGfQr337rSRu6peHVV1/dLd3kVbsykVKdJDzQ6jlfwYpcdNFF6ar2RlmwQxGhHrOawYrgoOy11171r13E1ce99957ercmgqxLHHDAAen6xRdfpKuZHbQl6i/lrLZHPXzwwQfTb/pXQV19+OGH6ztjjKDNsBNOO+p3xxH96ZRTTtmtH3d7KzMMubQtL8Zjdqs7E7EZfeF8MWdHd6iMZ555Zn03hY5wILy5AqEfdNBB1bfffjvj2MAglJSnkoLSBhQ+FLTXXnstHVfozBrrJ/2Bws7xIJQi8oky2AsUXiYX1157bXXCCSdMHyEZBlR4TVpQwmhsUiYH4ZJLLkmN5/7770/3Tz755PQxI4Hs4iRT5pFHHknPiR8Fn8bKcapnnnkm2bdhEPkOs57AIGloS64Ic/wswtGanLaTiGHCcT/qgfKO4skRpcsuuyzdm9GienHEEUekq9CEUxNxyocjYaOuD8YsZO64447iwkU30BVyncrtrcyw5dKmvNBr16xZM90Xzidzeka/aSW321nefimFxYomUNgoCO+++246jz/bCQYrWk1K6x577FH/6g1KPue+OG9MWrutELcB/8w6UWDXrVvX9fwf6UfhRRasjHdbbR8UwkbJ3rp1a4pDZ8gHgYYaV/Uff/zxdM48wmpzqVx0dviCCy5IkzzOpqM0l5TXbvQjXxhWPYl0SwMKbw6yGmT34Pvvv69/zSROVJo6PBTxUcGuwb333pvO8rNrwzsB1AveHzGj55BDDknXpjPe7LgA73SwoxZ32LQbxG9N1HKo39SfPHzVYb3DowWR/Cy47tlRXMy0lWMJdh1VVhHsJHfLfzgwBvXTXzKeMIbl4/Wg7W3SGbZc2pYX46T6wvlkThV9gVCpqKymQqkzKfHzzz/Xv2b+BnUoFGiEFfw4KHFMSEd5ZgPHJ4AV5Qi7FKxc9xOHOsVhKNmxwrKlrheOm2AFF0X0/PPPr236Jyqx+Yu2yF/PkQlb+6zwNymdKsd84IhoVZ9ty3zrUgMPLwpG4guC1DdefB5kdt+vfPupJ7nsmuiWBgZuyjPvuNgBYdBvi+T40EMPpatQuzv55JPTFWWgVJ6U+SgmjYL4OKbExJjdGnYVuin5ku2+++6brmZ20HaoI7Sl2P71WyuNW7Zs2c2wsKBn+SQ9Qv1h0hrDV78gf7Qf2lGuyHKPfT91flJpI8cSKsPYtvX74osvTlfLfzh8/vnnqR9vC0crS/1r3tYwbdvbJJPLBDMbubQtLxairr766vpuHukMkkNl27Zdn2PMXxLhnpdz4suUHWHPeLGBFxc6HUSy6xRA8kOY3OMWO8LQC6Nc9bJDZ+BJdlwVT4yro5SktOGecIkT97jRS0OyIx7cETduSRNGeeJKeggPt8CV+/gCI3EqPKG0y53i1AuWXImL8HnWlIYSyjtuMMSPnZAMeEb85FEyANIpOfJcacSPwpGdXjTFL/7Il/LLM9JNWrDjN3Cv+JvAf/4yZg7h4E5lH1EakBXuSF+UAfEjW9JAuniOe2SNwR6/pfB7yTcHN23qSbc4c7qlgXvCJhzVa57Fl3cln251EqJcAFmRl1g2pFXyVBnjHjvdA3YxLFCeY51WfYwy5Rl2xCFU/riTwU7ll8Nz4jHDI5Y9Msfkba0Ezym7CHURO8pQEB7hEyao3UQ3IL+q46prsX4vZtrIUW7ysuNe5av2n7ux/PsDOdOHxv4yH++aykNQDm3lW2pvpn0/1Ka81Bdq/MHgJo6788lQSx/hIKReJlZQBCKFAkERBgWAMhKVDnUeuEF4hMHALcFCHlapMKRM8Qz3NBjseAayUzyA+2hEjA+TNz5VJBnlLbcjHNlzpUKRvm5paGrkpEd5xCBHwheqkDHPxIVb7AgXuXMf5Uc6sCN9MTwpjQpPeVS6KR/kghsMvxVvE+SBMLpBGIRVgvRJfsRJeDHNpI1nGNKHvPmtvCmtMlHWveRbgufd6gm/9UymW/7blDH+eYY7lQXIXqapTgp+S46EhbxyYnwY0qNOEQhDzzDKW7TDlORQsgPiJD1RDjIKP0IeYr7McIhlj4zVr3ZD7iOUs9pjJK/LpfoH9FmqC3n7Mr3lSJlhT9uNxL6rW/la/u3ReCtZxf5ZNJUH0LfyrC2l9mba90Nty0vhYSi3cWoDS/jTSZgxYwPnyTsdWfpSU9PxGo4Ecf5tlMdDzPjCkZ399ttvt6MBvKPD/wz46quvapspO87w807GIMe1jDHGmIWKFX0zlqDI82UkXpouwScdebHXLD44J8zL601dF5M/vYAP3PNZUH0G0hhjjFkszMvLuMb0gq/h8FKpFDZeJONrPShtmPyTmmbxoBeCqQe8ZM0OkL4qxOdb+bKWoP7wUqGVfGOMMYsRr+ibsQZFjk9Z8eUk/sEZX1fhs4pMBMzihXrx9NNPT/9TLo568QWmG264Yfqzuao7/vKHMcaYxYoVfWOMMcYYYyYQH90xxhhjjDFmArGib4wxxhhjzARiRd8YY4wxxpgJxIq+McYYY4wxE4gVfWOMMcYYYyYQK/rGGGOMMcZMIFb0jTHGGGOMmUCs6BtjjDHGGDOBTKyi/8MPP1TLli2r7rvvvtqmqj777LP0L/KxN/3z/vvvV0uWLEnXXpx66qnJzBf8V1TMOEKdXLFiRZLlUUcdleplE+QBN7jFz/PPP18/GQ2kjfZB+wGuxEkaYluaNOaib7j11lurr7/+ur4zxhhjRs9QFX0UOxSSbmY+lT/48ccfqx07dtR3ZhiMWvnsFxS2PffcszrttNNqm/EBZe/TTz+tvvrqq2rLli2pLt57773105mg5F9zzTXVm2++WW3bti0p+uRtrkGWH3/8cX03mfzyyy/VRx99NNK+4brrrqvWrl3bdWK3EGHiT73sNhGMk1uubSfhTDTPPffc5I9J2GOPPVY/mQkyjRPiNosRi5V+FmyAPgvZ44ffWgSIWP7dadNGgLEUHakf+bmNtAM5xbqMnPvpi9uWocAd8YwFO4dMRyHZSbCloDds2LBz1apV9d38sH79+mLazOAsX768/jX/UL6YcYW61zZ9tJX5bi+in3QvVDpK+Mj7hu3bt6f2Qj85CXQmqzs3bdrUtX4g15UrV07nWXLeuHFjum9CslqzZk36vXXr1p1Lly7dLR7sCY/xBQiXe9JmZiKZtpXP6tWrp8sOv/zO+yTLvztt2gjghuf9yM5tpD30O+SfPCMf5NRWd2lbhkLyxowDI0lFtwyqos0XakhmONDBjIs8NcmkwxtXSF+bjgKs6M8tc9U3EM+4lOuwaKofapMMkhEURgbabm1VfUt0w/iBXZwoMVgTXgQFte0gvphApsgGGfZS8qQMxrKTAhMnaZZ/O5Bbrz6UMmlTNsJtpB3IIpfpIAs7bcqQstAEud/wR8Wcn9G//PLL618zzx9j2H7StiBXtqB0Nhi32vpl+wXYSpEd2zDRr7bAum2z4F9xKwzBb9l32+KKeWBLKI8v3y7Cbdy2nk0+2XZS2Nhp+w63MQ5BPAqHaymtyjdh8lt552yx/MuOuB999NH0Gz8YIG62uKI8Rbc0SBaKoySLbtx///1VR4Gq9tlnn9pmKt2SERAfvzH5kSPil1vizMuqG6Q31gPyr3ICyRVuuumm9DuXv8Ce52+99VYy/JYsCZOwlUbkE/NBOvRcbuWO+qI8YpfLINanbigvMqoPXGVXKnsgPuopz4lPYXElbZSX7JTmSMwTJvYZKmvVn1JZU7+4xy9uSxBeTIPyJ9qkgTgUDr9jGNRRyjXP2yTyzjvvpCvHvyLnnHNOOib1xRdf1Da7Q5nl7fk3v/lNur788svpigw7g3gKL3LiiScm+7zsFjPI8/DDD6+OOOKI2qY7r732WrrGI5D470zQqqeeeirdW/7zi9tIOw488MDquOOOq++moP/pKOT13fC4/fbbq1tuuWVGmcw3c6boM8DGCsWAePrpp6fK15lwpPPKL7zwQvXAAw+k5z/99FO6cjZ48+bNaaDgXHNntlrdc889aaD9/PPPp886M3CqYuMX99h1g4LHL3RmX+kstOA3dps2bdqtggga2YUXXlg9+OCDKQ+d2XVS4qRUAAM9FerLL7+sOjO9pAySb7mZTT45V/zJJ5+k8JHbRRddVG3cuDG9h0AcyFigfDzxxBPV22+/ndL6+9//PqWV8MVVV11VHXnkkek56ZHyAsRFWukYxN133111ZrfpN34w+CEPxJPTKw2Erziee+656p///GcaUGiMyKKbYkS8TDpOOumk2mYK0k14yIj4999//+r1119PylfMO/QqqyaYDJxxxhnT9YDw8UN4kiH1iWeAzPh94403pvsc7HlOB47ht+omnQjyo3xII3X0vPPOm1FWOmtOPbnzzjuTO/jtb3+b6skll1yS7E455ZQZMoiy6sazzz473UHG9sGVe9KMmxzSuN9++6V2Tn1GySNfW7duTfX6ggsuSGnGr+pZfH8B/8gUZQOZ0B6QBf6A9JN21Z+zzz47tQXyiYwo/0MPPTTZUQZNdQoZ8W6E2hJlq7bUJg2S4ZNPPpnCWb58efXzzz+n54BfoE+cdNS/5Rx77LHp+sEHH6RrDmMFMqQ/ikh29JWgPv6www5LV0E5Q1P4iw3q73vvvTdjoa0XtFP6l5yjjz56Wu6W//zhNjIY9PmMe7SJhx9+uLYdDugCyF9lMDZ0BquhQ7Al0xkUaxe7tgC5CrY6OkpCfbdrGyvfKmmyi36h5I577COy6wzQtc3UVg/p6QZbz3ELU3nS8SRtfXaUqnQP/Ga7DL9iNvlU2mMcCo9tPSAv3Ofb52wvYa8yINyYH/zFMlO40a4kTyCsmM62aVAc0V0p3hy5kewjJRnl6W5bViVwwzZgRNuneXqwy8u0iVyGgLwwAjkRZpQNfmLeoFROsot+S+4Au5huZINcVMcE93kZ5xBWnq9SXvN8IMs8Pm2/qv6U8qS6UbKLeSrlXe4Ubz9piP1JDnWmV/+ykMhlKdSu8meyL7VXKJWPwF51RbKOZQvd/C9GkJf6tiaZ5UQ5R2K7tPzb00YekluvsoFuMsbebWR3lGcM41dT/9NEN3nR38exObaT+WakK/qd8KdNZ3CsbadgxoM9V61+zteXPVjh7BR6OvohWFm8/vrr67vd0WyaFUqhPGnVhNVoVvPiFg6/O5Uh+R3mllmMg5VV8qNVSO0AHHzwwekq2AEAzfjPOuusNNPlyAMrl6XtrkFpmwaRb/VDt5UHPdMqRYnSVprKYNCywr7TwKtf/epXtc0UrCbDq6++mq7DgraCoWxZoWZHaT5ANjfffHPaRYmr3ewmjOprR8iS+HRkBsOqPHQ7AjIbqP/UC+WxnzTQfprgSNB89Xdzycknn5zkt27duuk2xIqajn7omIEZHfQTt912W7H/M2YxQX+OjrZx48a0q3/FFVe0Pp7bC3SnYe8QDIs5O7pz/vnn1792QcfPgMdZQDqizgyofjK35EoLCsvjjz+ezt7OFrb+c/baa6/61+hgezWHYwWRXJlmgsKxE7b8DjrooMYz5LOhVxrmk9mUVX5EYVSDKnWTgZstdY4hPfPMM/WTuSefIHNU5dJLL02/R8X6+thTbobRVpugj4rMRxoWKrSDV155JfVHxx9/fJIlk3qObjEBGLst7glDk6thLdgYMwnQV+t48dNPP52uswFdiWOa4zqZnjNFn44mdjZ0QHT8d911V1qhnO+OKCotvEDGSm4bOD+fQ34Eq8EoZyX22GOP+tfwKcX5/fff179mEpVZyoFz0yiQrMKhVA6TNmmYL2ZTVt9++239ayZ77713/Ws4cBacDor3CJiYzedEKZ8gMznWTsaoeOmll+pfu6DMpNCMAsKP5TgfaVjIoMzrHRUUfM7nswv20EMP1S52h36I/ljnjIVkrHdxtDiU7/bpXu8CLFYYX9lxijtQvBcFjL/spDfBGJjvtAJ2krvlP3+4jcwOdlyRDSv7s4U2xft8sZ2p7fB7FIum/TDnX90RqmTjsgoWlRZ2F1D8u3HIIYekRsbb1VE5ZFtaq7schQFWOiM8H+VqFkoXxwIuvvjidK+Gng+sekGQ7XWIlZHjF8hDxxJmS9s0DEqvl/t6MWhZqbOl3sR6oN9nnnlmukJ83oaSezoPXmAfl5UDTZB54RUzynRRRtTrOJEGXp6lPY4CtSWV47DSQNlSrxYb9I+83Lx27dqeR7wYGz788MMZ7UDtWxNK2iVyzJUd7rGf7wWk+Ybdpy1btswwOka7YcOG9LwJ1fk4gdVvjS2W//ziNjI7kNsJJ5xQ3w1O3sYwepGd36NeAOvJziGjly4x3V5G4yUI3OjlT66dSpdeUOMZfvXCVnzRUS+8xpceeMkIO/zqhaOSO+Aee704F9ELhrmfJngpg7BIN79JZ0ehnU4DV9JEmJ3CTnZcuY8vLM4mn0oDLwhih8EPbiI8x51ePkG+uIlxknbCi+FgJ1RmCgNkR75IM+HiF5lg+C3apEHhkQ6hF06juxJNblTmKgPAHXYqB9LZpqxKqPxiGfA7LwPlrU39UvnnMiQ9hIsd8osyxSgf2EV/kkGs95KB2iCU3JXqYkR1MPppgjTjNsqGdJbqi/KBH5A77FRXdRWSRywzyZ2rUJ3CvVA+JEfVz+imTRokwxhfTh73QkZlGvuKHNpSrKs5POdZlCOypr5LTmqP0Q3Ir+qxyht7szuq51E+knVehtzH/obfuRvLvzdt2ghoLMnrOEjObiP9Q3+P7Mk/MsMgs7wPLslYtC1DgTvcjwNDTYUyFk2TUBC03HNFiCgeVFIKQ51RDCe3w6hgutkpDYpPplSYDNK9FLsIYZBmwsMv+YqoQilOOsrYuGaTz+g/xsHvPB0Q04qykg+4+JMSg4n5ydPEPfCcPKncILrDRLqlIY+jJAuVZQnSnz8vlXnJDnqVVTeoM5Id+aMuxzLI48Q0hZ3nGaM0ImPCxyA72g2/CZ/4cn+letMkg9wOv031LoKCj6x6UQqrHzsgv1KkybfkAm3yxD32uR0gP8pN5VhqI9BvGnI0cVKeFjK5LPN8qSyRJW0L2ZXAnep0BFlJpk3lAbH99dNuFyMqsygj6j7yo15HYp9I+eT9mrD8m+nVRkTed2AibiODg4yUdwzykr4SaZJx2zKMqEzGgSX86STG1PDtdL4cslDguA3nw1yMU8csOo05fSN+XF+KmUT4HjzvWPhl1HYgL77eo/+NYIwxxoyKeTujP45w7nbUXw0xo4OXazoz8XRe2swd/BM0K/nt4Ewo8nrkkUdqG2OMMWZ0LPoVfa2u8d/l+JoJq2wLaTWYrybwgua2bdu6frd7McH3bHnBxsrnaEBZ5es/+hIN/0q9n/+4uZjhK1a/+93vRvYivjHGGBPxin4HFOVPPvkk/cvvhaTkx084cWRlvj/hNC6wWspnJ/25w9HBZxKZGFvJbw+LCnylyEq+McaYucJn9I0xxhhjjJlAvKJvjDHGGGPMBGJF3xhjjDHGmAnEir4xxhhjjDETiBV9Y4wxxhhjJhAr+sYYY4wxxkwgVvSNMcYYY4yZQKzoG2OMMcYYM4FY0W8B/3iJf0411/+A6Y033khmvvn666/TP+PiP6IaY4wxxpiFwVAV/VNPPTUpxN0MbkxvrrzyyvTfXU877bTaZv448MADq7PPPru64IILrOwbY2bAAgj9Va//zP3888+n/r+fBRP6m3PPPTeNHcuWLUv/XTgHN7feemt6rjHms88+q58aaCPHJqJs+d1rDKAe4NY0M2h54G7FihXT/rotwOX6mHWvKvULRx11VJIHcmzTF7HYGuUYDf1ehPJQW1H5jAX8Z9xhsm3bNv7TbjI5GzZs2Llq1ar6zjSxfv36ZMaNLVu27Fy5cmV9Z4xZ7NAnbNq0KfX33fos3PAcd/hpw/bt23cuX75855o1a9LvrVu37ly6dOlu8axduzaNLYTLM9zgz0zRVo4lVq9enfp8xnX88rvbGE7YTeO/mWLQ8qCO4w93GH4jZ8ooh/aGvdxiiGcxo7qJHGHjxo2t+iPkSFlFWWIosyhT+iG1FeBKGWE/34ykNXZr6BKyKaOJEh3AOEInTyU3xhhBn9WrX2BAbTOwCgbXvC9k/MAuDqZ5eAysuDFTtJFjCSlCKI1CyhLPcghfEwPLv5lByyNXGPEvZT/3xzgdwzc7k6zyhUrqK/ZNINdSXacd5P5KkzXux2Fxe87P6F9++eX1r6ktEW2jYNjK0jYUV21Tsb3CliFusGMLGLfaotUWFVf8caZcdvjvtY2LH7lnu4Xf2tIhrJgOgduSwX/MA1s7ehbz18T9999fdSpGtc8++9Q2UygsbQuRHuSQw1YRz+RGZ/zx3yRPPW+T1rPOOqtat25dfWeMMaOB/i3vC3/zm9+k68svv5yuHCs87rjj0m+xY8eOqjOA13emjRxLvPbaa+kaj48efvjhVUehqZ566qnaZhe33357dcstt+w2dpmZDFoed999d/1rCvxfdtll6fcvv/ySrkD4b731VtJjGOfRYRY76IAdpb0655xzapspTjzxxGTfdISH/gVdKOfFF1/crY/pKP7VSy+9NENv+uabb6ojjzyyvps/5kzRR6GMwqTynX766UnwnQlHtWXLluqFF16oHnjggfT8p59+SoZCeO6556r/+q//So3j22+/rfbbb7/k9quvvqq++OKL6s0336w6M6xUuTlHTmN59tlnk1+4995707WJq666KhUG6fj4449nFBQNSOmIdGbXyb1MZ9aX7F9//fXUAAmDhkbHqPy9/fbbKX1N4OfRRx+tTjrppNpmF3Si+Cd9nZl61ZmZVuedd96MtKKoU9FwhxuUemSMrJvkiV0/aT300EPTQFqaZBhjzDBgrKCfyQdJ+ijYvHlzukYYzOkD6e8efvjh2nZxM4gcBWMs40zO0UcfncbaCAtKhKlwTZnZlEcTKJi53NesWZPiueeee1IZLvbxWvX1sMMOS1eBPgMffPBBuraFtnHJJZfUd1M8+OCDST9Dl6Iv0iJqPkGbD0aq6Gt1GHPFFVfUtlNoBoqyCazKUCE/+eSTdM9M6thjj02/TzjhhLSqgEKP0LSCgyKr2RYVXWHdeOONSdkmDNxQKN3497//Xf3qV79Kv/ET3ROu0hGJhcfAwgCzfv366bQx2WC3QjsY2NP4qHBNOwxMWmCvvfZK18iPP/6Y5EP6yNtFF12U7OWHMJkkkHa5YfUdkHWTPJFVP2ndd9990/V//ud/0tUYY0ZFqS8sgQL161//OvWBH374YdeV0cVIWznmMI70gvHv6aefnrFbb7ozaHnkMDF46KGH6rsp0IkeeeSRtBDKoh2nAFgUbNI7FhN84GS20Newq4VOFUFvQt4sptIXIW/KYRwYqaIfV7xRHCMo0NhzZbbJLIjZUAlW8EcJCjGKut5gpwClsLeBLRxWOVCaxauvvpoGnTjZYXYNUs5zNKvULDOCjDB0qmzHXXjhhfWTKTRjjZWPjlcyjuTy7CetCl8TMmOMmW/or+nr2FlFsWFhaRw+TbwYYOz0Dsrcg8LJeNzty3y0C3bnUUy1wmxmByciLr300vpuJt99913SdVl0RqeibcRTF/PFnB3dOf/88+tfu6CisuLOWcDbbrttekV+rkEh5sgNs+ODDjqor08ioXQzgyvN3Fjh10QnmtKZr15QWYiLVf3999+/euaZZ+onw2GYaTXGmPmA/grFBlhlNqOFsfKaa65ptfJvhgf6AAuDbVaMmQygfLJIaGYPC64nn3xyfbcL7NFlOe3BaQl0KpR9jobPN3Om6DOzjKvkKPnHH398dddddyUB9bOCPgqIn8JBgeZlU5TqXrBixMo3fuJKuuC8fA4NlLz3C+flGcC+/PLLNDFp2oIqbc8h314MM63GGDMb6I9ZhczPLKs/Kr3HJOiLWTRiZX+xMxs5slOdn8UH7LQod9NNN6X3wOJusPzwe2y+Iz4mzKY8BOMyR9PiCYJecFRo7733ru8WH6qv+Vl83ZeOZ5dA5zvllFOK+h6r9zpSDZTPhg0b0nFqymw+mTNFP0cCHocV49gZsQ128803Tx9daYLZMcdneCk3bp1pe4zjQBxFypVsXjY+5JBD6ruZqLLllRHoPHlxuWnlRH7peCOkpyk+0U9aVWFLFd0YY4YFYwPn7eMgqb6Rf+DXDfzwLpIZXI5nnnlmusbFHv2++OKL05UzybnRC7z87lVOi5HZ1muU/H7fh/j000+rq6++ur5bfHB8mZeW8wkW99i3XWhml1DtIoeXn3/++ef6bgodw+ajJ/PKziGzbduuf5jF7yb03Vh9o5Qr3yXtdBLpGX7lhm+dRhQHboW+KYuJ34/FTa+0dGZ76Xun+MMQH3ZC6eAqeB7jB/zKn9KDP4WvazdwX/oHC0uXLk3xES55WVN/i5c0KV2EH+PDDUY0ybOftHY67+Su9G1ZY8ziQ/0xfUY36DNw161fic/ol+j31Ifhhvvohm+8Ey9h4x6T93uLnTZylJu8DLmP4w6/e5UzzylLU2bQ8kD+3OMuGvQFhcX33fHHWE8YGJ57vN5dd5E+hL1Ank19lMqEawn0Kp4rPMor1yXni6G2RjXwaJoyibDknitCoUIiKApCAs/DUWFF049dCRqJFF0MhaPCzNPBfW6XG6GCxo58lSpPDmkpyQyZEAaGCkrY/Mat0soV/4ovThia5CnappW4ed5U2Y0xi4dSX1jqZ+lvcncR/Khvi6C4yC99dOl57Ltxa6Vmd3rJkf4ce8aASGlM6dX3Kx7TTL/lofEe9yWjf2qGP4WLwX+T3rMYQU7qL5i05rKRDorJoV/RhKoEspd/wm/bXuaCJfzpJMqMCRwJ6lTE9B38cXzBiW3HI444oq/zgcYYY4wxCwH0nDbvNi4U5u2MvinD2ffO7H76H4eNE7zou23bNiv5xhhjjJk40HNYzJwkrOiPIbxow4sd4zSj5CUgXuzly0TGGGOMMZMEX9XhfwdN2mKmj+6MMVQ6PqPZ9o3wUcFxIr4SwLdj/b1kY4wxxpiFgRV9Y4wxxhhjJhAf3THGGGOMMWYCsaJvjDHGGGPMBGJF3xhjjDHGmAnEir4xxhhjjDETiBV9Y4wxxhhjJhAr+sYYY4wxxkwgVvSNMcYYY4yZQKzoG2OMMcYYM4FY0TfGGGOMMWYCaf2fcf/4xz/Wv4wxxhhjjDHjRq6vt1b0jTHGGGOMMQsHH90xxhhjjDFmArGib4wxxhhjzARiRd8YY4wxxpgJxIq+McYYY4wxE4gV/Ywffviheuyxx6oVK1ZU77//fm07GbzxxhvVlVdeWS1ZsqS2GRzCOuqoo1JYyOr555+vn4wnpO/UU0+t7rvvvtpmNCAXzELgs88+S/Vh2bJltc1gfP3119NtZljyJW233nprq7QRJ+5ou6Y79GmU1UKCcp2r9mvGB+qqy9ssVvoZA3vRl6LPgI5iJ5MrdzTM+BzTLygew4bOIk9XyTCQ/PTTT8nPtm3b0nXUINO5mlAcfPDB1UcffVTfDQ6K7DXXXFO9+eabSU4oeMMut1L5lEwb2aEo7LffftVbb71V24wGZLDnnntWp512WqpLpfRiaLjnnnvunJV7N3788cdqx44d9d3g/Od//ufQ2wzhDSNtZhfHHXdcKqtB22u3ei2Dm2FDuxp1+10IjHq8iAtdOaWyzhXxbs/j4hB9IHWQ+FBmIig49I/HH398ddNNN02PL9j3C+HjV+khrF4LMfghfW0mGcpPyVBWQHyl55imdlhyKyN5KHwo6V7RkE78lOpOmzaNma9JF/mkPigd5IMyKkH+eN4mrZSLwi3V9xzCVBowczF+D2sM7EvRP/DAAyu+xrlx48Z0f9ttt6WrYBDR1zpx0++XO2PFHTYbNmxI6cFs2bIl2a1fv36G3d57753yeOihh6bnc8H9999f/xo95G2fffap7wbnoYceSg2DsAgThR+FcZisXLkyVXKVz6pVq5K97rdv316tWbMm2fWCdFI3RwmdwAEHHDAdjyZBIqb7kUceqd5+++00kPUadEbJ4YcfXh1xxBH13eBQB1DEhkk/abvxxhtT/RtG3V4MMBGlruYKVhuo1+o/aZOq1zLU+a+++io9HxaUK2k2ox0vUJ7+8Y9/VO++++6MvktQvhr7ly9fnvoy2l5EbnhOGHpOP3f66adXl156aXJDez3hhBOqgw46qPrkk0+SG0AHOPHEE5OhnjFGv/LKK8n9IBM9TTpJK/E+9dRTKR3d+t2rrrqqlXLFxAN3pDGa1atXJ0O/CE8//XQaq3J3S5curS6//PLkJifKOrYzZHrZZZdVjz76aBojpTNJ99KYSPjRz/XXXz895uSTC/QeZC33+IVoh/40H1AnqfNnnnlmSg/5I++333577WIXKN4///xzet4G+pSrr746/UZG3U4lkI5169al35QtMhm1TjGs8TnRSXDfdIS9s1MJ0eJ3doRf206BfaeR13f90anQyf+w6VTc+tcUpJm05/aKW8/zvA2bJhmOEmQ8YLFPQxiYUYE8cpmU0t3pvHdu2rSpvutNqcyHQaeTSGGTnpxOZ1yU99atW5P9oG1lWCCPUvr6RW1mmPIdVtrM7lBXO4rGwH0P5dLUB4yijcGw69dCY67Gi17trlvZA+nLn3Nf8kM/GO1j3ISj8qa+9lv26mNzeRFfU1gdhTbpN/jrFR9uS31+RxGc1iUYG0o6DWnr1fcTdpOsiZtnpDXCeNiUdsLTeBSf5265x00ut1I+Rg35yWVMHkoyEU35L0EeJZNu5UF4bevFMFFZzJaBz+iz5QF33XVXuor33nuvOuWUU+q7XeRbS3H2xAwTO2bs5513XvodV/f5Hf3m2zL477b1kq86NKE85eRp72cLi3yyDYg/bZ8BKwrkFZhl81xbQYRPfrDDb1x5Qxbx3BZu+Y0pzUi1Dav489U2ZqrauiMMfjdtSREX7ignDL/jNj1pi1ts/I7brYRL/vGDPenC5NtwzJTbzJbjSp+2exU3ee211Su3GOVjkHBYceh0PMUV5aZVZmbrwEpChHpBnIqftEg+XOP7I7hV2ZZWZ+Pz3MT2ldMtDSKWNfUm7wdETAPXvI6SbsVDGTSdH4/1nDAFZRPbQzdiu+KqcFSvMfyG2OZlF9ue2o3SE8tFeYp5Ufqxj3Wq3zJtygNpwU6GsCDmQ3UcqJf003/5y19qm6m0kEbKdTaov41xx/ijXYS8qN6RDsk9B9kpvzFP0Kv9Uv/wQ9h5OHkdj89zE+txU9nm5GWd15OYf0xsdzxrGi9wozE09wfUW9JXqk9zCWNPLmP6wZNOOqm+q6r9998/XfN+gvqaj+PICtMExzXhueeeS1dBOg477LD6bheUG+b888+vbbrDanzev5O/F154oTr55JPTPav6pfb04osvppXhbjSNHaBTB3k/3m1nlfCeeOKJ9JvVaZXFbPUjiOOB6mBsB9RVjf2xvtIWmtoLML7ncmAX5ayzzqrvZg/hs1PQtKpPel966aXGetGt3XLt1uYlC/UfyKdJHupnMOr329K3ok+Bsu0LndlGUvhiZWN7iC25CBmhc0KQnclF2pKi05I/jjJgx3YfzzHa9iJz2HdmXsmeOB9//PH0bK5A6USRI27y0LZhUGDk/eOPP05pZ9tSeaYCkydQ3oiHisa5RLYYsbv55pure+65Z7pgf/nll5QGKjuVho7x9ddfT4VPXBEq0y233DIdFvHniiXblEceeWR6TjpVQUuQb9whCwy/2coH8sVWIluu2BMPhvtYcakfxEG9+f3vf5+2L/VexGz47W9/m66d2f+0fMhvNyhPOtutW7dO56PfcMgLW4VxsGqD6gH5F9ixrXzOOeckGVIvGDQeeOCB9Bw5YUgXg9c///nPVLbkgToS5Ux9ISwUcMKifYG2HdW+cnqlAcgzSiJ1Djk11Rvq8h/+8IdU5rjDD+1eCgr1k217nmFIU6ku4O7ss8+uvvzyy+roo49O74eI2B66Qdugs1Za6HjJJ/mlXlMHIrTF3C7G9eSTT6Z00Df97//+74xy+a//+q/UPpQX4v7mm29S+nEDtAtk1k+ZdsvDs88+O604bNq0aXqizJV70oObCNvClK3qovj3v/9d/+oP8hOVc+ImX8gIaG/AMQAGVvIgqCsXXnhh9eCDD6Z6x3P6wXzg/fTTT6svvvgitVfyRT8S3XRrv6RPZ/0Jh3rF8VONYy+//HJyB8iEMqJOkh7qAm0Vw72Unm5lm5OXdawnvdpd03hBPCgHKMzyR/244IILklug3vZqH6MGOZNvjurkZRrHUxRkxhH6iabFAyF5N4ECR9nSP1NeyJjyIlwtEEXuvffe6s4776zvBuOdd95Jdbebkg6U7SWXXFLf9c93332Xrhy76QfqCfKlPpDWYYBc+xn7OXbz3//936n94g65t4ExjbpOP9d05GlQbrjhhnT985//nK4R+vomHaBXu+3W5tV2Qf3Hhx9+ON2HRbqNga3oJK4v2L7pZCb97iQsbSuwpRHvI52Glux4FsGOZ4IwFI4grtwvbjqCqu8Gg/Tn8Uf0nGtnwEhxKs/9gJ98O4g8iRiPYEuOLXVB/HlaJVOeCdkJlUWMD7RNJZBldIO/XnnFT14GyIiwI2xPElenYdY2U35zmbQBfzHdOcgM2Ym1a9fu5p57yZFrdC/ahBNRGZbCglK6kYvKIcpe8uIqcBdlrfjY0hSlelSScykteb1pkwbKOg+b9OBP8gVkGduu6rLqA25LbV4obd3qOZTsIspTTAvlhV3MJ/cx/ZDbKa4YFqgM8vZG+E2yUp1pU6Zt8oCckHkuU+5j2IK05vG2BX8lE+uJIH2ki3rE79gfCJ6X2kJsV9zTHiPYxfJp037bhFMqZ9nFMulVtjnd6gn2XEVT21f8QDx5eSvPMax+UD6b4FmpnAXpa6oH6veQWy4DQT2mjuCO8hykfkbUTjBNcSIzyVVyjvWhLeS7V3oJHzm0gXTksiR85ILJy7hN2gmvm5u8nveC+pfnR/U5H/tzd9jl+SuhNGGoO93S1i1vOYSj+MlHnm/qIvFxLclW+Yzl0NRu87pHvcz7D+RFuQrlu9cY2Iu+V/Q5mrPvvvum36x2dIQz/fIDs5FOBtNvYLbDqkwnYTNWELHPYbaX7wQwqyd8+WUlkLjyl4BHxeeff55mXMz2tELWD4cccki6EoZWMbUS1AQzVV48YrbHLO6YY46pn+xOadVA8Wi2zuwvkvthC4xVDlbiiBNZD5JXyiXfRtXqATPcCCuRwwaZITvyT35YDS3BTJoyYCektCrQNhzxwQcfpGuvF7i1rYf59a9/nVbBaRexPmhVjiurXtQb3JUobdEqLcBKT0dJqe920elY6l9l2qQB+/x4Xp4e5MeqEfEp3//xH/+Rnim8Y489NoWFnNUnlNpHqZ5TV9vCai3EPogyVj4HIYYV0XEBQdyUQyx/VoAgrnZBtzJtkwfkxA4gbVHyRE58aau0gqm0soswCPT1xC9DPktyIX3s2lLurFY9/PDD9ZMpVFei7FQP8za611571b92QZsWbdtvKZzNmzfXv3Y9/9e//pWuEY1//ZRtTl5PlF+uvdp+5NVXX03lHdOgPLPzMU6QN+piR+FJ94zvpaMb1GNkgDvqBTKlLPtp85Fvv/029bX0RcTJuBphpZjyHmTci9Dm0IFKbS3Cym6v3eYI9SyWr+rYM888k2Q63/Qz9pf6cvVt3WDXp6PspnKkjZ9xxhlFPXI2aFU/7iShR/Hycynd0E+7zds8bTfXhQiD/OXMdgwc6OhO7Mx1bglFkUlAPMKgrVC2HCI0BmCgB8Kkw4xKKRkGCR+BcPynMzuadYNsCwMIhdaZ7dc2/UHh8BUDZELFpBL0GgCAjogtTjooJkCDoMGvSSERDIoc/WGQI8649d4vbSvoKKAOId9rr702TRjpEEowcFCmTBZLDaVtOP0iZQjTmf1PH02gg4zpQEGhrr/22mspjXHi3A8c2Vq6dOl0eVLvaHd0Wr3olQZkqON7vYj5ltF7IrRjdYoMwv0M5uOmxHQjV4hlUH6HDccBKHd9naXbtvOwoa/JF2sEEzgGfvr5YZdd/tWWYbRfxizSyzsM1EkMx6Yoy9inDrNsB2375LGUhl6LSvMF6aIPQJFnzOCIB+WWg2JE3jAok6UvrfSCPoVFHRRFxuLVq1dXV1xxxbSyT7nyFbl4fGhQUArbyBzdRmf425DXsU2bNiVdSROgflEfW3pPYVDmYuwnPMqJtsUYNKyjR4J2zbjMxIO2CBzl6XXEalhjdr/004/2pejTGHPFkcGajHFuHqU0KvWcXWQAz/1wBhWksKOA5O6YNIBWBHXOfK6UfGAiQmfErHSQBgWqnJytYpW1qVMTKGYoaXzi7O67795Ndv3SZmKBTDnzygoBL+lw9n8QGMRLoHiMEjouBmVkxYpRt84WBVWfa0MhUKcH/YQzG5j901nRqaDo6jwfHQaTWVYUGAxmU9eJg7OPnEWm/VDvWO3tNaC1TQPhtqH00lC0Q9bIgskPMqdMRkWpLWhBYZQwcMR6Jkqy6UWvPNDfxFV9+uV8oWWUNLUZ+jUmHLQvFj1K8mAHNaef8hlm+0WOvLjI+wosgLAbxaQ8f89hWGU7m7bPBCSHNElZGQWlPEfycas0plA+jDkobfH9iBz6rHhyoC20Ffyo/lOmyBZlXy+lMhHOV8wpB2Ahhvu2EGa+A5VDvWA3NJdPP7BjwHiNbkT++qlryIQxhzG522mBfpnLsV/turQrN1viqj51hbP31JsmZjtma9E7Qr897Lbbl6JPokorNhdffPF0QfeqwAiDhqXtO0CpZ0aUQ0XWLFYCxH83RXnYUKm0okDc/UBhqcDUyfTq1FhZZ7aOojYb9PUCZN2NuIJPB6KXf7tR6uTpPOlA8gqK29kMuG1gZotc234tAdmyi0F64ypRv+GAdqXisZlItwEx7wQVxjDkRTmwNUido/0wsWmzatUmDbRLvVjVBO2V/PEybnRH29VREVbV9Iwy4UXMUh2aLSojBu4I8et4nYjHQLrlry1a3clXI/tVBPvJg1b1USgwTQMV35wG9RXDJCp21EEGZZQgbeXHCR3pJ70scESZo5TE8ujFIO23CdLB5BiFirZDG2JCGmU5rLKFQds+Ry9pM/nYxOJBXi+GBZMp4mxqHxxNyccvdl1Kk1T+iSNIaWM8KoVL/cj7y17wEjLkdYiX0KknwCSAUwLRbKi/Gc/kgvs2kDfC7DVu8019vgnfhl79T0keatMlCE+7eywCdVNg+2Gux37JJT+WPAzQX7Wqz+p8r9X82YzZnPSgzkQdDNiN7XfC0JNOB9aazgC/20togmcdAdV3U3SU+fTSQKexpHuu3Ofu8NtRppN7DMiv7gE3nc61vhvspQQgfvzx4kMJxR1f2Op0bskuvmSl+JW/HOzxl+df93qRg3D0sgdp6jTg9Aw7vUhEmnEHuMnj1QtY8UWgPM2Eiazll/CRp+LHEHaUcY7STDi4F9iTbuJUHSFe7HSPe8WP+7bgHz9N/pQm1SvyRh6wQx4YhUH6hGSGP9LWJpwSPI91RcR0R7BX3YnyUVnHNoC8SDPPcCc3qgtAurCLaVD4pF8GO/zHclNdklzbpEHxxbKOsszDIo88x8R6Q3qIX2Fwj1s9b1vP8zyUUDlKDqRT5SxIG/ETNvmOMlQaFBd5iyivPM+RH8JX3IQp5JdnolSmbfIglPZuMpEbyT+Wa6wjOdFdDmGR37z+RGJdiXUBO8kIN+RTz9U2o9x4FtMhN5JJqf22CQfwKzvcYkgTaZO8oFfZ5jTVE9l3a3dKO/GQZtLIFXfYEy/PdBXyF+tSN8gD7mO7i8Tyj26Ih3xhr3ITpIm2Rb70jDzhNrZ50o0dYRE29/jBTcwTYIfbJiQb3KjMCJP7PKwIbiTniPKQ5w1UN7qBvyb/Jci3yjUiOasMIiq7mBbiU30q5SuCW8LEHfWuDaRHZSE54xc73RMu8ef5V1xNMiGt5JU6B6oz5KcEzwkvl1kTpJN0xfjVXnI5qd+KbbdNu5WbvM0TJ/LgGeVGfKQ75k3lHNuZ0hFlQpwYyTunlZasyiNTEjKJbLKPfksJ0TPiieTx5oJX2P0Qw5OJQszTi+DV8KMdqDJH/xHscSt/FEQuI+VRnTCVDHeyozJQabDjWQwPQ3pLdoBfhU865TY2HJ4rPgzPujU6uZNRXJCnj7BieUd/mCa5RUpxEkeOKj95IVwNRtjn5YeBkl23cJpAhnma8jLJDeHjL8oHucsfV54RL2VHvcllgZuSHagexbLN3SguGcLqlQaBXBQ27tRhEkbMk+zljnQJnqmjx/Bbz0tpa2PXVKfytlAqT/zyDDd6LrfkqRQ/cI32uIsQt2TIc9KhNlby2xRemzwI5Ig8u0H7jGlVXae8muSYy6BkSBtpje1Osop2mBg/biSj2A/lfjAlO0Am/FYeevUDTeEQN/KL9VNG+ZO7prLN6VZP8CPZcm1qdyr/WPa4RV7Y416yFtSFkn0JpU0mpjGC3IhT+cYgK+Io5Z/0Ys9z9Qf4JT+xv1C4cqNwS7oFbprSJ6JsMPih3+mG6kQuL6WrlD/sYz5KkAfy2walt8nksi7V42hwTxl0S2NTGG2gjqn+YpBVjCuGhynFhV0OMlMd40q4JXeAPNqEKXK3sbzVBkXuFkPYvdptnqa8vka5UYdiPZe9DGGV7IiT+PAf8xBZwp+OB2PMgHAcpdPI0lcBhrUdOlvYyudltnwLkC1mvnyS/+M0M1lwpIfjEN22lJctW5aOsOV1hK1kjgoNfft4AUE7+fvf/77bmWvaemcgT/86fzHLZy7gKAhHI9ocOTRmsUN74T2n0nsifX91xxgzE871bdiwYcY/lZpPaPB8Rq6kiHCGdOXKlfWdmVR4MbCbks9EYM2aNbvVEc6/8qL1YlZikQHn80ufzKWtM6nXJzbN6OAdAyv5xrSDd2OaPrxgRd+YIcAsmhdr8pfi5gO9kIWix4uBrEJimADw9SheejSTBcopL7hS5phun9RktZpP9/JVrwj1g6+Q5N+5X2zo5U0+0Ul7Rl7IF/mw2/GrX/1qVl9NMe0Yl91RY8YZxnYWbvhiUFOb8dEdY4YIijX/+Gi+V0RJB1940FdOWIXkCyx0BlZSJg8UUT6Xxxdi+KJG02f+UFr5Ok231X4zJScGTxR9JvBL668Y+ciOMWahYUXfGGOMMcaYCcRHd4wxxhhjjJlArOgbY4wxxhgzgVjRN8YYY4wxZgKxom+MMcYYY8wEYkXfGGOMMcaYCaQvRZ9PuN16663pPyouWbKkWrFiReN3w7HnOe74vjPf+szh82VyQ5h8o5g4ShAe4fAtY2OMMcYYY0x3+lL0r7rqquqTTz6pbr755vRvwPlmM/+BE4U9wj3/mOepp56q+Hon3+3mv3FGJR43f/rTn6rLLrusWr9+fVL0b7rpphRHDt8E5x+8vPXWW7WNMcYYY4wxphutv6PPivw777wz4x+x8E9Ffv3rX6d/xvPVV18lO9xxz0Qg/udFFHn+ScsjjzyS7tkZiM/1D1+2bduWTP5PfVjJP/7446stW7b4H5YYY4wxxhjTg9Yr+ije+X9bPPzww6tVq1allX3BZAD+z//5P+kq+K+Cjz76aH1X7fbv1/nXvazuwy+//JKuxhhjjDHGmMEYysu48d+pv/rqq+l6yCGHpKs44ogj0rXXGXt2A5hAGGOMMcYYYwZnVoo+x204snPDDTfUNrtghb5fNm/eXD300EP1nTHGGGOMMWZQZqXoP/nkk9Vdd92123n6QWCln3BOO+202sYYY4wxxhgzKAMr+ryIu//++884tjMo7Ax88MEH0y/qGmOMMcYYY2bHQIo+Sv73339fVPLPOuusdM3P4nMsB/Iv5qDkv/zyy9WNN95Y2xhjjDHGGGNmS9+KvpT8piM2J598crqyQi9Q5j/88MNqzZo1tc0UUvLzr/kYY4wxxhhjZkfr7+gDq/ScyT/ppJNqmym++eab9KUcKez8h9t169ZV7777brXffvtVt99+e/rPtl9++eX0S7p8b59/qpWH9dNPP1U7duzY7RgP/vnnXPxzrXz1X9/YLz0zxhhjjDFmMdJa0ee/055++un13e5s3759xpd2pOyjtK9evbq65557pl/aRcnnP+XyrMSmTZtm7Biceuqpu/1X3JhsFP0zzjijuvfee707YIwxxhhjTIe+VvSNMcYYY4wxC4NZfV7TGGOMMcYYM55Y0TfGGGOMMWYCsaJvjDHGGGPMBGJF3xhjjDHGmAnEir4xxhhjjDETiBV9Y4wxxhhjJhAr+sYYY4wxxkwgVvSNMcYYY4yZQAZW9D/77LPqscceq+/MfMJ/Lb7yyivTfxBeLPzwww/V888/Xx111FHpvzAPG2R67rnnDk2mpHHZsmUp3XPBsNM/TFRflyxZUtuYuYA++9Zbb031kP8mbppBPh7fjDGTwECKPgrWW2+9VV1++eW1zRQrVqxIg7fM119/ncwoFLGFBorNMAdXykDsueee1dtvv13fzR/kDyVirpRL8v3xxx/Xd8NlNmFT5+dTkWIyQfpfeOGF2ma8OPjgg6uPPvqovjNzxR577JGuO3bsSNd+GGWdJlwm7Bo36CuJr4QmKrjjdz5xxh8TXIVFX9SU7m5hHXfccdV//ud/prQYY8xCpm9Fn07zqaeeqm688cbaZgo6S9i5c+e0OeWUU6rly5cn+8UMg8+jjz5aPffcc7XN7EDJ//bbb+u7qUGJSdZ88/PPPyclgkngqBXdffbZpzrttNPqu+EzG5nef//99a9d0F5+/PHHlO5RQxykf+XKlbXNeHHggQfOiRzMTJD7XnvtVd/1R6lODwN2Gc4444w0Vqxfv75atWpV6iu5z5V4FHgWNJiAb9++Pf2+4IIL6qdTE1z8obwT1po1a1JfdPzxx6d4Ir3CAvqXAw44IE0CjDFmodK3on/xxRdX11xzTX03hVbs77rrrnQVX331Veq4FzsMkkuXLk0DWNNKVVsYsMZ1lYmBkQkeg+xihUkY5TwOWJk2w2CUdRpF/Msvv6zuvvvuNBl+8803q9WrV1fbtm2r3nnnndrVVBrYobrjjjumJ4pPPPFE8q/dTdy/8sor1SOPPJLC4rphw4b0LB7DaROWuOSSS1LeR71wYYwxo6IvRZ9OkA44X0n95ptv0vW9995L18htt91W/9oFnaa2VrW9GuFez7SqGt1HRTceF1I4soudNr/lDlM6ThTDypXpmOZ+YJWJ1aLXX3893ZdWxmLYURa5HZOEE088Ma2a33TTTelZng/iI+08Iz/5xCLf2uZ3XO3iuba0CYu4+U0adY8/7PidD4C4+fTTT9OKci84q60te8LL88K9yoTnbVfWyE/MI/IgXW3l3I2YZgzxELaenXfeeek3q4g8J07SI5nmoIAoj1xzGVBvSRf2hCP5c1W8vcCf4sBfrBOEgXxIm9KQKzvc6zl5z9tGTFdTOcV8EgaLAG3oVV+RL+mRPGLdj+5K4D6mm9+E17ae4F/5wg/55nlMb3QPJbsm+cV05H7kHoO7Jnim+ko6X3rppfrJLgap0zBo+4ygkOcT0quvvjpdf/rpp3SF1157LV3j2HP44YenBRR2mIF0YxfR8VJ200SbsARpY5fgL3/5S21jjDELjJ19sHLlyp2rVq2q73axcePGnQQls379+vrJ7sitwtmyZUu6X758eboX3Ef7zgRjOvw1a9YkOyF7wsTwnHulg2v0J/ekRcgOlKYYj8LEdMtfDm4VD2nDP3nJ2b59e8prlG/JDkppwA3ls2HDhuSPODoD14w8yA43uscPdlu3bk12XFevXj0dx6ZNm5IbrtivXbs2uZNfZBXheSl/OciEeOUff8QpWame6LnKkHREclmQ/phH3ONGcuhHztxHO/IV41M9kUxAdlEu/JZMI/hDhpIXac7TqvTjH3vCkiyUxyZIO/kinuiPOAVh4oY0EJ/SyW+QPGMao0x4jn/lV3mIZYJdLGu5wXSDOGNZcp/XV8IkfuzJC/KKMutGt/rcpp7gRzJV3DzDTvU3yhoURpRvN/lhXwoHsCPOJggbv5IfYSE77BQfeSjF16tOt22fg6D4SJvgviQD5M2zbvBc/Qr0G5byFtNjjDELhe49ZAadXdPgyUDH89zkMKhhr4EF5DbayR1X0ICEIa6I7DEMEnJL5x79aVBSWtX5617hRj9Cg0+06wWDeRxQFEaefsFAIyVClOwII8oKcJMPXrlf4s3dSBmI5do0sBFWHDB5Lpn2CwpHDCtXSrjiRiDLUr5zO/KRy5dwYv5yuUAbO6WRq0Ce0Y3KOJeLZCpUx3LFiHTmcXAfFS/ALpdFTilPSodkT3xRNlKSlX6uaoMilht+4z0gE5Wd8llyg3032tZX8pi7K+U9h+cxXXl9LoWR26m88/wB7kqyi257yQ9UZrE98jvPcw5x522BekRYyuegdbpt+xwEwijV97wsADueNUGaczn1Gxblg/0wJjHGGDPXtD66o+3aI444Il1zOA/ZGXzqu12wraujAIQhN/vvv3+6QmdAStfNmzen66AQDkdGOHfZyVvayv3www/rp7sgrXoOHK0BzmKSXqUHlG/CxQ+mLU8++WR1/fXX13dTYXQGkxSPtsaHSelMdoyHeNmij7Bl3RkIi19oQY6Rs846Kx2NYMuecHne5ohODjLl+NF+++1X20ylA9lqq50r2+06HnHMMcck+16QD16gixBOfhxlEJRGrjpSM+iXeV5++eV05Qs0kYsuuihdOS8cKb1EOUh7Ofvss9NVL3OTD4yObF144YXJXhxyyCHpSl7VFtRuAHlztIN2I4NMKF/C1Dnro48+Ol1Fqa7m9FNfS+HlMswZVn2GWJcFxxbp72Ld49hIP/IDzol3lOoZx/6oP7FvyeE4EHGfcMIJtc0UeT0atE4P2j57QXjU6+uuu662mR0cueH8/WxQ2f7zn/9MV2OMWUgM9HnNHJ3ZlYKNicqyznj2ou253SY4L5oTv07ThCYfKOFKv8ygAz8D1rp163YbxKV8MAmYC/JBO55VFW2ULmBw510DBuKDDjpot/PkwwYFgngoQ03G5huUXeoZChuKHHVmNvzyyy/1ryn4LOYoySdv1FMUfJRnJt/PPPNM/WQK6sY//vGP6qSTTkpfR0ERzM++b9myZbd2gyEunbPO423LbOprL0Zdn+k76Af1nhJ1p7RQ0k1+QH5vvvnmNPFB+afMHn/88RkThhzVq9IEJGfQOj2K9km/+Oyzzw6ljJm43HLLLWkSY4wxi5WhKPoMPlrtEyjtDGAif16ipKjPlrhz8N1339W/ysx2ohFhwGJwzgfv7du3p9U5JgEM2HNNadcFSFMbUF74MgYKIXkY5AU88fnnn9e/dqHVT5QuBul33303fZGjH0WRl4Fz4qrqoFCHeSGRr0sR3qCTwMj3339f/5rJoJ9B7IXqnMLnk4IoaXz5BMW3NNFA6eKlSdzsvffe6YVwrTbD3/72t/rXLniJM9LrxdgmZltfezHM+lyCukIekAef19WOSqSN/OKqPrskq1evrp90p9TGIoPW6dm0zyaYOJDPkpJPfks7NNiVJibIj92okpLfb1jGGLOQaa3oawBoOi7AZzdz4qDB73gfV9o1mLNqOGzikYH8iwpCnTvpaJqQYK9V+V5oxY1BK0erc2zNl1b1c+V/mJMBBjhW+PM8Eke31UERVzz5YgX5uOeee2qb9jAAo7SgKMT8oQxqBZh6Rtn1uxpHWXIcIuaROP7nf/6nvptiEDl/8MEH6dpGVr1QnXvooYfSVfC/CODkk09O12GjozQKH+XmnHPOaVxBRY6SJW5QBqm7OnpEnWKlOVfk//rXv6arJtolxaoXs62vvWhTnwepJxHSSV3/wx/+kO5zhbiX/IT6Ddyy6l7qWyL77rtvur766qvp2sSgdXrQ9tkESj6ToKZ6eOaZZ6ZrrAv6nY89KPn/3//3/zWmrZ+wQG0yLhoZY8yCYWcfdJST4gtg2BMUpqMs17a7XiKLL2jpxSb8AC9Lcb88e2mNl8iive4x+QtmsleYOdGvXnzjqnCUBkxMB+EpPzGMbi+c8VIabpETv0vohceOAjDjBTfFwYtuuOGFNMKRO4XHvfKKO+xJN/YxTvwSnux44Q432CtfeqkulltH+ZhOR4Q4yTvhYXDXJPNeqG6Qbn6TV8JSWgmbdJFm7EgL7pGR5F96MVNliV/CxC35jS8btpEzhrRhlCalIdYhnuOXZ8hQaZKcVL6SaVM6AP+EFV9EVHhRzoSLHW67gR/ypJcISQv3Sj9wTziESfwxTRj88Fz54Mpz3St9GJUNMtFzwL/CBPzgRuFIvjm4U/qa6it+CQu7GI7ibAobetXnNvWEZ7iJdTCHOJTXnDbyE8RH3N3iipBehas8kj/sSDcyVPr7rdOkgbTwLMpBeYC8/pXAL37UVqPBP88F96QLO9LF71heQDpIWx4WdlGmbcIS+CdvqnPGGLOQ6EvRZxCgw8uJHSTPoyl1jnS40U1TBxvdqLON9yCFQYb7Ehq0ZbiP5GnCxIEhPu8GeYlh5OT5wCgvGnCwIxwGIa6kNaZF5SA3MSxMt7wwMMc0MgDGMsrTr7QB6Yjyxm8ciPuFsFEESmGRTsWFEsAzZIMdz0p5FCgW8osf3EfayDmGi8Fe7uSPcEgbeYjKs+oazyCXqeKAKAPSjKIiSnnslu8c8k1aFD55jnED6eY5RoodvyUX3Mf0k8aYV8CN5MnzXLEjHMmEsMkzYVLm3ZRA6FVfZS/TJLMSvepzr3pCPuRXbkrIXxO95BdpI7NIrF+kmzpJXJQh6VLalP62dbpX+wTdd8u78l0yiksQR6xHildwn4chQzyRXmFFkHm3PBhjzDizhD+dzq41nKPnuEH+T7OMMcaMFr5C9NFHH9V3CwOOBQ3jHZn5YtmyZeml7bbvLxhjzDjRt6LPWdJLL710wQ02xhizkEFZ5h0WXppeKHD2/tBDD12wSjLp530yXjg2xpiFSN+KPjDgvPfee+l79MYYY0YDiiYv1B555JHp60h8IajphdVxY6Er+Sxqvfjii1byjTELmoEUfaAT/OKLL4by9QtjjDG7g7J8xRVXpK80sbAyjM9Ymt54fDPGTAoDK/rGGGOMMcaY8WUo/zDLGGOMMcYYM15Y0TfGGGOMMWbiqKr/P0NgkL+J0+KrAAAAAElFTkSuQmCC\"\u003e\u003c/p\u003e\n\u003c/div\u003e\n\u003cdiv id=\"Sec13\" class=\"Section2\"\u003e\n \u003ch2\u003e4.2 Regression results\u003c/h2\u003e\n \u003cp\u003eThe estimated ordinal logit model is shown in Table \u003cspan class=\"InternalRef\"\u003e2\u003c/span\u003e. The estimated standard error of our regression model ensures that each of the variables we propose have a normal distribution. The measured values of Pseudo R2 (0.8150) and Log-likelihood (LL) (-19.196311) confirm that our regression model is suitable overall. A well-fitting model is indicated by a Pseudo R2 value more than 0.20 and an LL value closer to zero for the accessibility of maternity and child healthcare services (Iqbal \u0026amp; Rahaman, 2021).\u003c/p\u003e\n \u003cp\u003e\u003cimg 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\"\u003e\u003c/p\u003e\n \u003cp\u003eTable \u003cspan class=\"InternalRef\"\u003e2\u003c/span\u003e outlines at important explanatory factors that affect MCH service accessibility. Every explanatory variable\u0026apos;s last category acts as the benchmark for comparisons. There is a significant correlation for each category taken into consideration. Depending on the type of coefficients, the expected rate of the dependent variable varies when the value of a significant variable rises by one unit.\u003c/p\u003e\n \u003cp\u003eThere has been a great acknowledgement of the role that maternity and child healthcare services play in lowering the death and morbidity rates of mothers and newborns. With access to basic prenatal, natal, and postnatal care, moms can avoid the majority of maternal and child mortality. Even in environments where maternal healthcare services are widely accessible, adoption of these services is far from universal (Srivastava et al., 2014). It is well acknowledged that when it comes to the use of maternal medical services, a mother\u0026apos;s age can occasionally be used as a proxy for her level of education about healthcare services (Fosu, \u003cspan class=\"CitationRef\"\u003e1994\u003c/span\u003e). Women may be more aware of the healthcare services that are accessible to them and value modern medicine more as a result of recent improvements in educational possibilities for women (Elo, \u003cspan class=\"CitationRef\"\u003e1992a\u003c/span\u003e). According to a study\u0026apos;s logistic regression results, mothers\u0026apos; educational attainment was correlated with an increase in medical checkups (Rahman, Islam, \u0026amp; Rahman 2010). According to a survey, just 10.8% of moms without formal education received \u0026quot;excellent\u0026quot; maternal care services, while 49.6% of mothers with at least a high school diploma did (Srivastava et al., 2014). It is well knowledge that using contemporary healthcare services is positively impacted by higher income (Elo, \u003cspan class=\"CitationRef\"\u003e1992b\u003c/span\u003e). Husband\u0026apos;s employment is portrayed as a component that makes it possible for mothers to receive healthcare services by acting as a stand-in for income (Fosu, \u003cspan class=\"CitationRef\"\u003e1994\u003c/span\u003e). According to an analysis, women from households below the poverty line were less likely than women from families above it to use maternal healthcare services. It was clear that, compared to women from the lowest quintile of society, women from the richest quintile had a 4.53 times higher chance of obtaining ANC during pregnancy (Jat, Ng, \u0026amp; San, 2011). This indicates that, in comparison to mothers from lower-income families, moms from higher-income families used maternal healthcare services at a higher percentage. According to Shahjahan et al. (2013), the study also revealed that moms with one living child had the highest percentage of appropriate maternal healthcare utilization when compared to women with two or more children. Due to time constraints that prevent them from seeking healthcare, women who have a large number of children underutilize the resources that are accessible to them (McKinlay, 1972). The size of the family, which is the fundamental social unit, has a significant impact on matters pertaining to health, including the utilization of healthcare services (Zhang et al., 2016). Large family mothers typically underuse healthcare services since they have too many responsibilities taking up their time. Resource limitations brought on by larger families also have a detrimental impact on healthcare use (Wong et al., 1987).\u003c/p\u003e\n \u003cp\u003eDistance appears to work as a key deterrent to access. Because pregnancy and labor are physical conditions that cannot permit pregnant mothers to walk or travel for long, these can bring about adverse effects on the health of a mother and the child she is carrying (Leslie \u0026amp; Gupta 1989). Lack of transport, distance, difficult topography, the cost of ambulance fuel, use of an ambulance for unintended purpose and uncooperative behavior of ambulance drivers affected the accessibility also (Kea et al., 2018).\u003c/p\u003e\n \u003cp\u003eAdequate experienced and skill pediatrics, skill midwives, and obstetricians and female gynecologist may improve the accessibility condition to maternal and child healthcare in rural areas (Barnea et al., \u003cspan class=\"CitationRef\"\u003e2021\u003c/span\u003e). Pregnant mothers\u0026apos; decisions to use formal maternal healthcare services are significantly influenced by the gender of the medical staff. It is typically seen that during the complication phase, female patients are reluctant to visit the facility in order to receive care from male specialists (Banik, \u003cspan class=\"CitationRef\"\u003e2003\u003c/span\u003e). This is caused by a number of causes, one of which being the country\u0026apos;s primarily Muslim population. As a result, cultural and religious preferences have a significant role in whether or not mothers receive treatment from male doctors, which may be viewed as a religious sin (Sarker et al., 2016). There are situations when women might not want to give birth to a male doctor or divulge important and complicated medical information. According to certain studies, the lack of privacy and secrecy in receiving maternal healthcare from male doctors was a source of distaste for expectant mothers (Some, Sombie, \u0026amp; Meda, 2011; Titaley et al., 2010).\u003c/p\u003e\n \u003cp\u003eThe constituents of static healthcare services have served as a representation of their quality. High-quality health posts are those that have a complete staff of medical professionals, at least 50% of recommended medications on hand, distinct maternal health and child health clinics, and a respectable physical infrastructure. Health articles are categorized as low quality if they don\u0026apos;t meet all of these requirements (Acharya \u0026amp; Cleland \u003cspan class=\"CitationRef\"\u003e2000\u003c/span\u003e). Access to healthcare services in low-resource countries like Bangladesh may be severely hampered by factors such as service location, unqualified healthcare workers, staff absenteeism, inadequate health services, costs and prices of services, including unofficial payments, and staff interpersonal skills, including trust (Jacobs et al., \u003cspan class=\"CitationRef\"\u003e2012\u003c/span\u003e; Ensor \u0026amp; Cooper, \u003cspan class=\"CitationRef\"\u003e2004\u003c/span\u003e; Peters et al., 2008). According to the results of a linked poll, the majority of respondents (89%) were willing to pay for healthcare services if pharmaceuticals were easily accessible, and 92.4% wanted to pay if overall quality improved. However, the use of maternity and child healthcare services was hindered by long waiting lines, the actions of providers, and a shortage of doctors (Uzochukwu, Onwujekwe, \u0026amp; Akpala, 2004).\u003c/p\u003e\n \u003cp\u003eThe decision regarding when and where a woman should seek maternal healthcare services is influenced by a collective consensus involving the husband, mother-in-law, and other family members. This decision-making process is further shaped by gender dynamics and economics constraints. Women often rely on men for financial support, and local cultural norms emphasize the importance of respecting the opinions of mothers-in-law. Similar findings have been documented in studies conducted in various countries. For instance, Pradhan and Mondal (2023) showed that, among nuclear families, women with with stronger marital relationships are more likely to utilize MCH services and give birth in healthcare facilities. In joint families, women who maintain positive relationships with their in-laws are also more inclined to seek antenatal care services (Allendorf, \u003cspan class=\"CitationRef\"\u003e2010\u003c/span\u003e; Warren, 2010).\u003c/p\u003e\n \u003cp\u003eRural women of northeastern Bangladesh face several adverse circumstances. This study targeted those barriers and results showed that several demographical factors were influencing rural women whether to make decisions about seeking institutional maternal healthcare services or not, such as age, education of the mothers, household financial status, cooperation of family members, number of living children and size of the family. The distance of the maternal healthcare center from home and the availability of healthcare facilities are also evident factors. Women are not interested to utilize maternal healthcare services in the facilities, because it was not staffed with encouraging, respectful, skilled healthcare providers, did not provide the required drugs and the environment was very unhealthy. Not only that, some women were also found not cooperating with the services provided to them. They were not interested to go there again for the scarcity of female doctors. Besides these factors, several cultural barriers, superstitious mindset and lack of awareness was also an issue. This study not only examined the significance level, but also identified the probable positive outcomes which could be made by bringing necessary changes in those identified factors held responsible for the healthcare-seeking behavior of northeastern rural women of Bangladesh.\u003c/p\u003e\n\u003c/div\u003e"},{"header":"Conclusions","content":"\u003cp\u003eIn the villages of this region, there has been a notable improvement in the availability of healthcare services, particularly MCH facilities. These services play a crucial role in addressing factors related to maternal health, such as maternal mortality and morbidity rates, as well as maternal nutrition status. Additionally, process indicators related to service availability and utilization contribute to enhancing overall maternal well-being. Many healthcare centers now operate in rural areas, both government and private organizations, including non-government organizations (NGOs). These organizations complete to provide maternal healthcare services, resulting in better quality healthcare and more affordable options for patients. Dispite these efforts, a gap still between available facilities and the target population. Accessing maternal healthcare services is not a one-sided process. Both recipients and service providers play crucial roles.\u003c/p\u003e\u003cp\u003eEnsuring adequate facilities in maternal and child healthcare centers is essential to attract women. Effective utilization of services is equally critical, Otherwise, efforts may not yield fruitful results. Women and their families must understand the importance of qualified professionals providing MCH services. Establishinh more healthcare centers in rural areas will enhance accessibility. Regular monitoring of health workers\u0026rsquo; activities is vital. Raising awareness among rural residents, particularly those receiving maternal and child healthcare services, along with government and policymaker interventions, can significantly enhance the quality and accessibility of maternal and child healthcare services for rural women in northeast Bangladesh. Maternal and child health remains a top priority in the villages of this region. Therefore, it is crucial to place special emphasis on maternal health, examining existing policies, strategies, and interventions aimed at improving MCH outcomes.\u003c/p\u003e"},{"header":"Declarations","content":"\u003cp\u003e\u003cstrong\u003eData availability statements\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe data supporting this article cannot be made publicly accessible due to privacy concerns for the individuals who participated in the study. 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Family characteristics and the use of maternal health services: a population-based survey in Eastern China. \u003cem\u003eAsia Pacific Family Medicine,15\u003c/em\u003e, 5. https://doi.org/10.1186/s12930-016-0030-2 \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":"Health seeking behavior, Healthcare barriers, Antenatal care, Maternal public health, Postnatal care","lastPublishedDoi":"10.21203/rs.3.rs-6234069/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-6234069/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003eDespite efforts at the government level, maternal and child healthcare (MCH) services continue to pose significant challenges in Bangladesh. However, limited empirical and country-specific research has been conducted to understand how health system elements impact the effectiveness of MCH services. This paper takes an initial step in identifying socio-demographic and infrastructural factors that influence MCH service accessibility in rural areas of northeast Bangladesh. Through in-depth interviews and logistic regression analysis, data from respondents were collected to explore the relative importance of various factors. Results showed that age, level of education, monthly income, and farm size are significant driving factors of MCH services. On the hand, availability of healthcare services, satisfaction with service providers, knowledge about maternal and child healthcare services, and availability of experienced and skill predictrics are significantly associated to MCH services. The model developed in the study provides a detailed understanding of the determinants of MCH and how they influence MCH outcomes. The findings of the study serves as a foundation for the \u0026lsquo;white box\u0026rsquo; approach to theory-driven evaluation of MCH.\u003c/p\u003e","manuscriptTitle":"Factors influencing the accessibility to maternal and child healthcare services: Case study of rural areas of northeast Bangladesh","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2025-07-09 11:38:23","doi":"10.21203/rs.3.rs-6234069/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":"da452779-a212-4499-95f6-ee80d4e4f6a5","owner":[],"postedDate":"July 9th, 2025","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"posted","subjectAreas":[{"id":51208600,"name":"Humanities/Health humanities"},{"id":51208601,"name":"Social science/Sociology"}],"tags":[],"updatedAt":"2025-09-18T11:23:35+00:00","versionOfRecord":[],"versionCreatedAt":"2025-07-09 11:38:23","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-6234069","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-6234069","identity":"rs-6234069","version":["v1"]},"buildId":"8U1c8b4HqxoKbykW_rLl7","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}