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Evaluating the Effectiveness of Maternal, Neonatal, and Child Healthcare in Moroccan Hospitals and SDG 3: Using Two-Stage Data Envelopment Analysis and Tobit Regression

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Title: Evaluating the Effectiveness of Maternal, Neonatal, and Child Healthcare in Moroccan Hospitals and SDG 3: Using Two-Stage Data Envelopment Analysis and Tobit Regression
Language: English
Authors: Youssef Er-Rays (ORCID 0000-0002-6691-9226), Meriem M'dioud (ORCID 0000-0002-7855-3495)
Source: Evaluation Review. 2025 49(2):343-379.
Availability: SAGE Publications. 2455 Teller Road, Thousand Oaks, CA 91320. Tel: 800-818-7243; Tel: 805-499-9774; Fax: 800-583-2665; e-mail: journals@sagepub.com; Web site: https://sagepub.com
Peer Reviewed: Y
Page Count: 37
Publication Date: 2025
Document Type: Journal Articles
Reports - Research
Descriptors: Foreign Countries, Sustainable Development, Hospitals, Mothers, Neonates, Perinatal Influences, Child Health, Well Being, Mortality Rate, Resource Allocation, Health Services, Efficiency, Physicians, Allied Health Personnel, Data Collection, Data Interpretation, Medical Care Evaluation
Geographic Terms: Morocco
DOI: 10.1177/0193841X241264863
ISSN: 0193-841X
1552-3926
Abstract: Maternal, neonatal, and child health play crucial roles in achieving the objectives of Sustainable Development Goal (SDG) 2030, particularly in promoting health and wellbeing. However, maternal, neonatal, and child services in Moroccan public hospitals face challenges, particularly concerning mortality rates and inefficient resource allocation, which hinder optimal outcomes. This study aimed to evaluate the operational effectiveness of 76 neonatal and child health services networks (MNCSN) within Moroccan public hospitals. Using Data Envelopment Analysis (DEA), we assessed technical efficiency (TE) employing both Variable Returns to Scale for inputs (VRS-I) and outputs (VRS-O) orientation. Additionally, the Tobit method (TM) was utilized to explore factors influencing inefficiency, with hospital, doctor, and paramedical staff considered as inputs, and admissions, cesarean interventions, functional capacity, and hospitalization days as outputs. Our findings revealed that VRS-I exhibited a higher average TE score of 0.76 compared to VRS-O (0.23). Notably, the Casablanca-Anfa MNCSN received the highest referrals (30) under VRS-I, followed by the Khemisset MNCSN (24). In contrast, under VRS-O, Ben Msick, Rabat, and Mediouna MNCSN each had three peers, with 71, 22, and 17 references, respectively. Moreover, the average Malmquist Index under VRS-I indicated a 7.7% increase in productivity over the 9-year study period, while under VRS-O, the average Malmquist Index decreased by 8.7%. Furthermore, doctors and functional bed capacity received the highest Tobit model score of 0.01, followed by hospitalization days and cesarean sections. This study underscores the imperative for policymakers to strategically prioritize input factors to enhance efficiency and ensure optimal maternal, neonatal, and child healthcare outcomes.
Abstractor: As Provided
Entry Date: 2025
Accession Number: EJ1466588
Database: ERIC
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  Value: <anid>AN0183370754;evr01apr.25;2025Mar04.03:58;v2.2.500</anid> <title id="AN0183370754-1">Evaluating the Effectiveness of Maternal, Neonatal, and Child Healthcare in Moroccan Hospitals and SDG 3: Using Two-Stage Data Envelopment Analysis and Tobit Regression </title> <p>Maternal, neonatal, and child health play crucial roles in achieving the objectives of Sustainable Development Goal (SDG) 2030, particularly in promoting health and wellbeing. However, maternal, neonatal, and child services in Moroccan public hospitals face challenges, particularly concerning mortality rates and inefficient resource allocation, which hinder optimal outcomes. This study aimed to evaluate the operational effectiveness of 76 neonatal and child health services networks (MNCSN) within Moroccan public hospitals. Using Data Envelopment Analysis (DEA), we assessed technical efficiency (TE) employing both Variable Returns to Scale for inputs (VRS-I) and outputs (VRS-O) orientation. Additionally, the Tobit method (TM) was utilized to explore factors influencing inefficiency, with hospital, doctor, and paramedical staff considered as inputs, and admissions, cesarean interventions, functional capacity, and hospitalization days as outputs. Our findings revealed that VRS-I exhibited a higher average TE score of 0.76 compared to VRS-O (0.23). Notably, the Casablanca-Anfa MNCSN received the highest referrals (<reflink idref="bib30" id="ref1">30</reflink>) under VRS-I, followed by the Khemisset MNCSN (<reflink idref="bib24" id="ref2">24</reflink>). In contrast, under VRS-O, Ben Msick, Rabat, and Mediouna MNCSN each had three peers, with 71, 22, and 17 references, respectively. Moreover, the average Malmquist Index under VRS-I indicated a 7.7% increase in productivity over the 9-year study period, while under VRS-O, the average Malmquist Index decreased by 8.7%. Furthermore, doctors and functional bed capacity received the highest Tobit model score of 0.01, followed by hospitalization days and cesarean sections. This study underscores the imperative for policymakers to strategically prioritize input factors to enhance efficiency and ensure optimal maternal, neonatal, and child healthcare outcomes.</p> <p>Keywords: technical efficiency; Sustainable Development Goals 2030; maternal; newborn and child services; data envelopment analysis; Index Malmquist; Tobit regression; public hospital; Morocco</p> <hd id="AN0183370754-2">Introduction</hd> <p>Public hospitals play an important role in the healthcare system, with maternity, newborn, and child services (MNCS) being a crucial component within this hospital system ([<reflink idref="bib92" id="ref3">92</reflink>]). Additionally, they contribute to achieving the Sustainable Development Goals (SDGs) by 2030, which are a call to action by all countries—poor, rich, and middle-income—to promote prosperity while protecting the planet ([<reflink idref="bib87" id="ref4">87</reflink>]). These goals recognize that ending poverty must be accompanied by strategies that foster economic growth and address various social needs, including health and wellbeing (SDG 3) ([<reflink idref="bib45" id="ref5">45</reflink>]). Maternity, newborn, and child health are essential components of SDG 3.1 and 3.2 ([<reflink idref="bib95" id="ref6">95</reflink>]). However, Moroccan like many countries, which MNCS face difficulties due to the limited availability of resources, which can result in misallocation and inefficiency in healthcare delivery and prenatal and postnatal quality, equity of access to care, and inefficiency of healthcare personnel ([<reflink idref="bib9" id="ref7">9</reflink>]; [<reflink idref="bib12" id="ref8">12</reflink>]; [<reflink idref="bib51" id="ref9">51</reflink>]; [<reflink idref="bib73" id="ref10">73</reflink>]; [<reflink idref="bib88" id="ref11">88</reflink>]; [<reflink idref="bib89" id="ref12">89</reflink>]; [<reflink idref="bib15" id="ref13">15</reflink>]; [<reflink idref="bib37" id="ref14">37</reflink>], [<reflink idref="bib38" id="ref15">38</reflink>], [<reflink idref="bib28" id="ref16">28</reflink>]; [<reflink idref="bib24" id="ref17">24</reflink>]; [<reflink idref="bib27" id="ref18">27</reflink>], [<reflink idref="bib36" id="ref19">36</reflink>], [<reflink idref="bib34" id="ref20">34</reflink>]; [<reflink idref="bib22" id="ref21">22</reflink>]; [<reflink idref="bib23" id="ref22">23</reflink>], [<reflink idref="bib25" id="ref23">25</reflink>], [<reflink idref="bib26" id="ref24">26</reflink>]; [<reflink idref="bib30" id="ref25">30</reflink>]; [<reflink idref="bib29" id="ref26">29</reflink>]; [<reflink idref="bib32" id="ref27">32</reflink>], [<reflink idref="bib33" id="ref28">33</reflink>]; [<reflink idref="bib100" id="ref29">100</reflink>]; [<reflink idref="bib101" id="ref30">101</reflink>]; [<reflink idref="bib35" id="ref31">35</reflink>]).</p> <p>According to the World Health Organization (WHO), significant cost reductions in national health expenditures can be achieved through efficiency improvements. These benefits can be realized by reallocating resources, healthcare workforce, infrastructure, and health services to meet the actual needs of the population ([<reflink idref="bib23" id="ref32">23</reflink>], [<reflink idref="bib25" id="ref33">25</reflink>]; [<reflink idref="bib78" id="ref34">78</reflink>]; [<reflink idref="bib96" id="ref35">96</reflink>]; [<reflink idref="bib97" id="ref36">97</reflink>]).</p> <p>To conduct an efficiency study, it is imperative to examine the key indicators for Maternal, Neonatal, and Child Health (MNCH). These indicators reveal significant improvements over time. For instance, maternal mortality decreased from 227 per 100,000 live births in 2003–2004 ([<reflink idref="bib94" id="ref37">94</reflink>]) to 72.6 in 2018 ([<reflink idref="bib48" id="ref38">48</reflink>]; [<reflink idref="bib50" id="ref39">50</reflink>]). Additionally, the rate of deliveries in supervised environments increased from 72.7% in 2011 to 86.1% in 2018 ([<reflink idref="bib48" id="ref40">48</reflink>]; [<reflink idref="bib50" id="ref41">50</reflink>]). Furthermore, infant and juvenile mortality rates saw a decline from 30.5 per 10,000 live births in 2011 to 22.2 per 10,000 live births in 2018 ([<reflink idref="bib48" id="ref42">48</reflink>]; [<reflink idref="bib50" id="ref43">50</reflink>]). Similarly, the neonatal mortality rate reduced from 21.7 in 2011 to 12 in 2019 ([<reflink idref="bib48" id="ref44">48</reflink>]; [<reflink idref="bib50" id="ref45">50</reflink>]). These trends indicate positive advancements in MNCH outcomes over the years ([<reflink idref="bib48" id="ref46">48</reflink>]; [<reflink idref="bib50" id="ref47">50</reflink>]).</p> <p>Nonetheless, substantial disparities in access to healthcare exist across regions and socio-economic environments. The present issues noted in the healthcare system include an inadequate supply of human and logistical resources and an inefficient geographical distribution of healthcare workers. In 2021, Morocco had a national density of 17.5 healthcare professionals per 10,000 inhabitants ([<reflink idref="bib49" id="ref48">49</reflink>]), with 7.7 doctors and 9.9 paramedics per 10,000 inhabitants ([<reflink idref="bib49" id="ref49">49</reflink>]). For instance, there are 23 professionals per 10,000 inhabitants, which is notably low in comparison to comparable nations such as Tunisia and Jordan, which have doctor's rates of 13.1 and 27, respectively ([<reflink idref="bib68" id="ref50">68</reflink>]).</p> <p>There is a significant disparity in the number of medical and paramedical staff across different health directorates regions in Morocco. For instance, the Drâa-Tafilal region has only 3.1 physicians per 10,000 inhabitants ([<reflink idref="bib48" id="ref51">48</reflink>], [<reflink idref="bib49" id="ref52">49</reflink>]; [<reflink idref="bib70" id="ref53">70</reflink>]), while the Beni-Mellal-Khénifra region has 4.2 physicians per 10,000 inhabitants ([<reflink idref="bib48" id="ref54">48</reflink>], [<reflink idref="bib49" id="ref55">49</reflink>]; [<reflink idref="bib70" id="ref56">70</reflink>]). Similarly, the Casablanca-Settat region has 7.4 paramedics per 10,000 inhabitants ([<reflink idref="bib48" id="ref57">48</reflink>], [<reflink idref="bib49" id="ref58">49</reflink>]; [<reflink idref="bib70" id="ref59">70</reflink>]), whereas the Beni-Mellal-Khénifra region has 7.8 paramedics per 10,000 inhabitants ([<reflink idref="bib48" id="ref60">48</reflink>], [<reflink idref="bib49" id="ref61">49</reflink>]; [<reflink idref="bib70" id="ref62">70</reflink>]).</p> <p>In terms of bed capacity, the current count of 10.8 beds per 10,000 inhabitants (combining the public and private sectors) falls short of the WHO's recommendation of 20 beds per 10,000 inhabitants ([<reflink idref="bib48" id="ref63">48</reflink>], [<reflink idref="bib49" id="ref64">49</reflink>]; [<reflink idref="bib70" id="ref65">70</reflink>]).</p> <p>The demand for hospital healthcare has increased in recent years, particularly during and after the COVID-19 crisis, demanding the effective and optimum usage of resources. Despite government attempts to improve efficacy and efficiency, the Moroccan Ministry of Health has launched several initiatives, including the Hospital Establishment Project and Hospital Reform 2010. Furthermore, the WHO has set goals in several areas of action for the period 2019–2023 ([<reflink idref="bib93" id="ref66">93</reflink>]), with an emphasis on enhancing access to high-quality, effective, safe, and affordable medical goods. However, the worldwide backdrop is dominated by the COVID-19 health crisis, which will persist from 2019 to 2022, resulting in a significant demand for hospital-based healthcare services.</p> <p>In response to this situation, Morocco has already committed to the "Health 2016–2021" initiative ([<reflink idref="bib71" id="ref67">71</reflink>], pp. 2016–2021), emphasizing access to healthcare services, as articulated in Axis 4. Amidst the COVID-19 crisis, the "Health 2025" plan reinforced this strategy ([<reflink idref="bib72" id="ref68">72</reflink>]), grounded in three pillars consisting of 25 integrated axes organized into approximately 125 actions ([<reflink idref="bib74" id="ref69">74</reflink>]). Pillar 1 specifies the organization and development of healthcare services to enhance access within the hospital network, encompassing medical and nursing consultations ([<reflink idref="bib74" id="ref70">74</reflink>]).</p> <p>Currently, there is no research addressing this type of subject globally, particularly in Morocco. This study contributes to the existing knowledge on maternal, newborn, and child services (MNCS) across Moroccan public hospitals. Given the lack of academic studies on this topic in Morocco, researching and policymaking in the health system would greatly benefit from this study.</p> <p>This comprehensive examination can enhance the efficient allocation of resources in MNCS and provide a foundation for reducing health disparities at the level of Moroccan public hospitals and a basis for narrowing the gaps in MNCS. One of the most commonly used methods in this regard is Data Envelopment Analysis (DEA), introduced by Farrell ([<reflink idref="bib43" id="ref71">43</reflink>]) and further developed by Cooper ([<reflink idref="bib13" id="ref72">13</reflink>]). DEA is a linear programming-based technique that has been widely accepted as a competing methodology to evaluate the relative efficiency of entities or decision-making units ([<reflink idref="bib6" id="ref73">6</reflink>]; [<reflink idref="bib17" id="ref74">17</reflink>]; [<reflink idref="bib60" id="ref75">60</reflink>]; [<reflink idref="bib64" id="ref76">64</reflink>]; [<reflink idref="bib67" id="ref77">67</reflink>]; [<reflink idref="bib69" id="ref78">69</reflink>]; [<reflink idref="bib75" id="ref79">75</reflink>]; [<reflink idref="bib29" id="ref80">29</reflink>]). Several studies have explored its application in the healthcare domain ([<reflink idref="bib7" id="ref81">7</reflink>]; [<reflink idref="bib10" id="ref82">10</reflink>], p. 201; [<reflink idref="bib14" id="ref83">14</reflink>]; [<reflink idref="bib61" id="ref84">61</reflink>]; [<reflink idref="bib76" id="ref85">76</reflink>]; [<reflink idref="bib79" id="ref86">79</reflink>]; [<reflink idref="bib82" id="ref87">82</reflink>]; [<reflink idref="bib102" id="ref88">102</reflink>]).</p> <p>The study aimed to assess the operational effectiveness of MNCS in a Moroccan public hospital setting. The importance of these services in public health systems, their policy relevance, and efficient resource allocation were key factors. MNCS are crucial for optimal healthcare delivery and improving health outcomes for vulnerable populations. Understanding their operational effectiveness is important for policymakers to develop targeted interventions to address gaps in service delivery. Assessing operational effectiveness helps identify inefficiencies and optimize resource utilization, ultimately improving the quality and accessibility of MNCS. The study also aimed to bridge the research gap by providing empirical insights into the efficiency and performance of MNCS, contributing to the existing body of knowledge in this field. The study aimed to inform policy decisions, optimize resource allocation, and contribute to improving maternal, neonatal, and child health outcomes in Moroccan public hospitals.</p> <p>Current research focused on estimating the technical efficiency of 76 MNCS across Moroccan public hospital from 2012 to 2020, using Data Envolepment Analysis (DEA) in the first stage. In the second stage, we used Tobit Model (TM) to analysis the factors influencing MNCS activities.</p> <p>This study was organized into sections including literature review, methods, findings, discussion, conclusions, recommendations, limitations, and suggestions for future research.</p> <hd id="AN0183370754-3">Theoretical Background and Review of the Literature</hd> <p>Analyzing the performance of healthcare hospitals is essential for maintaining the sustainability of population health ([<reflink idref="bib92" id="ref89">92</reflink>]). Therefore, measuring efficiency represents a crucial tool for assessing performance levels ([<reflink idref="bib39" id="ref90">39</reflink>]). It also involves establishing a conceptual framework that models the system's components and identifies performance indicators. Vrijens et al. conducted a measurement of healthcare system performance ([<reflink idref="bib90" id="ref91">90</reflink>]), facilitating the identification of information collection needs. Efficiency in healthcare delivery is the optimal use of resources to achieve maximum health outcomes, minimizing expenditure on financial, human, and infrastructure resources. Assessing efficiency in MNCS is crucial for healthcare policymakers and administrators to allocate resources effectively and improve service delivery.</p> <hd id="AN0183370754-4">Theoretical Background</hd> <p></p> <hd id="AN0183370754-5">First Stage: DEA</hd> <p>[<reflink idref="bib43" id="ref92">43</reflink>] introduced Data Envelopment Analysis (DEA) ([<reflink idref="bib43" id="ref93">43</reflink>]), a nonparametric method for analysing technical efficiency (TE) ([<reflink idref="bib52" id="ref94">52</reflink>]). DEA is a linear programming-based statistical technique that constructs an efficiency frontier by utilizing the combination of input‒output variables of a set of homogeneous decision-making units (DMUs). Subsequently, [<reflink idref="bib13" id="ref95">13</reflink>] ([<reflink idref="bib13" id="ref96">13</reflink>]) and [<reflink idref="bib8" id="ref97">8</reflink>] ([<reflink idref="bib8" id="ref98">8</reflink>]) developed this idea by introducing the variable returns to scale (VRS) assumption (BCC: ([<reflink idref="bib8" id="ref99">8</reflink>])), which is commonly used in most studies due to its flexibility and realism ([<reflink idref="bib1" id="ref100">1</reflink>]; [<reflink idref="bib20" id="ref101">20</reflink>]; [<reflink idref="bib21" id="ref102">21</reflink>]; [<reflink idref="bib86" id="ref103">86</reflink>]) compared to the constant returns to scale (CRS) assumption (CCR: [<reflink idref="bib13" id="ref104">13</reflink>] CCR ([<reflink idref="bib13" id="ref105">13</reflink>])).</p> <hd id="AN0183370754-6">Input–Output Orientation</hd> <p>Data Envelopment Analysis (DEA) focuses on two distinct approaches to assessing technical efficiency in public hospitals.</p> <p>Input orientation evaluates the efficiency of resource utilization by evaluating how efficiently inputs are being utilized to produce a given level of outputs, such as beds, medical staff, and equipment. The goal is to minimize resource usage while maintaining a constant level of output ([<reflink idref="bib18" id="ref106">18</reflink>], [<reflink idref="bib19" id="ref107">19</reflink>]; [<reflink idref="bib21" id="ref108">21</reflink>]; [<reflink idref="bib31" id="ref109">31</reflink>]; [<reflink idref="bib54" id="ref110">54</reflink>]).</p> <p>Output orientation evaluates the efficiency of output production by assessing how effectively hospitals are producing desired outputs given the available inputs. This approach examines the relationship between inputs and outputs, aiming to maximize output levels while keeping inputs constant. The choice between these orientations depends on factors like organizational goals, resource availability, and the nature of healthcare services provided ([<reflink idref="bib18" id="ref111">18</reflink>], [<reflink idref="bib19" id="ref112">19</reflink>]; [<reflink idref="bib54" id="ref113">54</reflink>]).</p> <p>Input orientation offers advantages such as resource optimization, flexibility, and focus on resource management. However, it may overlook output variations and may not adequately address the quality of healthcare services provided. On the other hand, output orientation emphasizes service delivery, maximizes output levels, and facilitates performance benchmarking. However, it may overlook resource constraints and lead to overutilization of resources, which can be unsustainable in the long term ([<reflink idref="bib18" id="ref114">18</reflink>], [<reflink idref="bib19" id="ref115">19</reflink>]; [<reflink idref="bib21" id="ref116">21</reflink>]; [<reflink idref="bib54" id="ref117">54</reflink>]).</p> <p>Both input and output orientations have their merits and limitations in assessing technical efficiency in public hospitals like MNCSN. The choice between these orientations should consider the specific context and goals of the healthcare organization, as well as the need to balance resource optimization with service delivery and quality of care.</p> <hd id="AN0183370754-7">DEA Model</hd> <p>The Constant Returns to Scale (CRS) ([<reflink idref="bib13" id="ref118">13</reflink>]) and Variable Returns to Scale (VRS) ([<reflink idref="bib8" id="ref119">8</reflink>]) models are two common methods within the Department of Economic Analysis (DEA) for assessing technical efficiency. CRS assumes that a proportional increase in inputs leads to a proportional increase in outputs, maintaining constant returns to scale ([<reflink idref="bib13" id="ref120">13</reflink>]). It is useful for evaluating efficiency when the scale of operation remains constant and there are no economies or diseconomies of scale ([<reflink idref="bib1" id="ref121">1</reflink>]; [<reflink idref="bib21" id="ref122">21</reflink>]).</p> <p>VRS, on the other hand, acknowledges that for example hospitals may operate at varying levels of scale efficiency, meaning they may experience increasing or decreasing returns to scale ([<reflink idref="bib8" id="ref123">8</reflink>]). It allows for the assessment of technical efficiency under conditions where hospitals may not be operating at their optimal scale, thus capturing scale inefficiencies. VRS accounts for potential economies or diseconomies of scale, providing a more nuanced understanding of efficiency ([<reflink idref="bib1" id="ref124">1</reflink>]; [<reflink idref="bib21" id="ref125">21</reflink>]).</p> <p>The choice between CRS and VRS depends on the specific characteristics of the DMU and the research objectives. For MNCSNs, which often operate under changing conditions and resource constraints, VRS may be more suitable as it accounts for variations in scale efficiency. However, CRS can still be useful for evaluating technical efficiency in MNCSNs when the scale of operation is relatively stable and there is a need for simplicity in the analysis.</p> <p>Both CRS and VRS models have their advantages and constraints in assessing technical efficiency in public hospitals like MNCSN. The choice between the two depends on the specific characteristics of the healthcare system, the research objectives, and the need to capture variations in scale efficiency ([<reflink idref="bib1" id="ref126">1</reflink>]; [<reflink idref="bib21" id="ref127">21</reflink>]).</p> <p>Malmquist combined DEA with the Malmquist index (MI) to improve technical efficiency analysis ([<reflink idref="bib66" id="ref128">66</reflink>]). The main objective of this integration is to estimate the overall factor production over two or more periods. Additionally, Tobit regression was employed to investigate the internal and external variables that affect technological efficiency. This model is particularly effective at dealing with issues related to the regression of shortened or limited explanatory variables ([<reflink idref="bib85" id="ref129">85</reflink>]).</p> <hd id="AN0183370754-8">The Second Stage of Data Envelopment Analysis (DEA): Tobit Model</hd> <p>The second stage of DEA using econometric models like Tobit regression is crucial in understanding the complexities of assessing efficiency in the health sector. It acknowledges that inefficiency can be influenced by exogenous factors, such as institutional frameworks, socio-economic conditions, and structural characteristics, which can impact healthcare system performance ([<reflink idref="bib40" id="ref130">40</reflink>]). Tobit regression allows for the inclusion of these exogenous factors in the analysis, providing a more comprehensive understanding of efficiency determinants. It also helps in addressing environmental influences, enabling researchers to identify and quantify the influence of environmental variables on healthcare performance. This helps in isolating true inefficiencies within the system and developing targeted interventions. The second stage of DEA complements initial efficiency scores by delving deeper into the underlying drivers of inefficiency, enabling policymakers and healthcare administrators to make informed decisions to improve overall system performance. By identifying key determinants of inefficiency, the second stage informs evidence-based policymaking in the health sector, guiding the design and implementation of interventions aimed at enhancing efficiency and optimizing resource allocation. Integrating econometric techniques like Tobit regression enhances the methodological rigor of efficiency assessments, ensuring robustness and reliability in findings.</p> <p>Tobit regression was employed to investigate the internal and external variables that affect technological efficiency. This model is particularly effective at dealing with issues related to the regression of shortened or limited explanatory variables ([<reflink idref="bib85" id="ref131">85</reflink>])</p> <hd id="AN0183370754-9">Up to Day</hd> <p>Several studies have been conducted to examine the efficiency of MNCHs in hospitals using DEA in many countries via the PRISMA approach (Figure 1). For example, in Europe, studies have been conducted in Belgium ([<reflink idref="bib44" id="ref132">44</reflink>]), Scotland ([<reflink idref="bib63" id="ref133">63</reflink>]), the United Kingdom ([<reflink idref="bib53" id="ref134">53</reflink>]), Turkey ([<reflink idref="bib56" id="ref135">56</reflink>]), and Norway ([<reflink idref="bib46" id="ref136">46</reflink>]). Second, in Asia, there have been studies in India ([<reflink idref="bib80" id="ref137">80</reflink>]; [<reflink idref="bib81" id="ref138">81</reflink>]) and China ([<reflink idref="bib57" id="ref139">57</reflink>]; [<reflink idref="bib65" id="ref140">65</reflink>]). Third, Ethiopia ([<reflink idref="bib4" id="ref141">4</reflink>]; [<reflink idref="bib62" id="ref142">62</reflink>]; [<reflink idref="bib98" id="ref143">98</reflink>], [<reflink idref="bib99" id="ref144">99</reflink>]), Kenya ([<reflink idref="bib59" id="ref145">59</reflink>]), South Africa ([<reflink idref="bib3" id="ref146">3</reflink>]), and Nigeria ([<reflink idref="bib3" id="ref147">3</reflink>]) were identified as African countries.</p> <p>Graph: Figure 1.PRISMA.</p> <p>The most common inputs were the number of doctors and nurses ([<reflink idref="bib3" id="ref148">3</reflink>]; [<reflink idref="bib4" id="ref149">4</reflink>]; [<reflink idref="bib44" id="ref150">44</reflink>]; [<reflink idref="bib46" id="ref151">46</reflink>]; [<reflink idref="bib53" id="ref152">53</reflink>]; [<reflink idref="bib56" id="ref153">56</reflink>]; [<reflink idref="bib57" id="ref154">57</reflink>]; [<reflink idref="bib62" id="ref155">62</reflink>]; [<reflink idref="bib65" id="ref156">65</reflink>]; [<reflink idref="bib77" id="ref157">77</reflink>]; [<reflink idref="bib81" id="ref158">81</reflink>]; [<reflink idref="bib98" id="ref159">98</reflink>], [<reflink idref="bib99" id="ref160">99</reflink>]), followed by the number of open beds ([<reflink idref="bib3" id="ref161">3</reflink>]; [<reflink idref="bib4" id="ref162">4</reflink>]; [<reflink idref="bib62" id="ref163">62</reflink>]; [<reflink idref="bib65" id="ref164">65</reflink>]; [<reflink idref="bib81" id="ref165">81</reflink>]; [<reflink idref="bib91" id="ref166">91</reflink>]; [<reflink idref="bib56" id="ref167">56</reflink>]). These studies used most output variables, such as the number of patients in the MNCH ([<reflink idref="bib46" id="ref168">46</reflink>]; [<reflink idref="bib58" id="ref169">58</reflink>]; [<reflink idref="bib62" id="ref170">62</reflink>]; [<reflink idref="bib65" id="ref171">65</reflink>]; [<reflink idref="bib81" id="ref172">81</reflink>]; [<reflink idref="bib91" id="ref173">91</reflink>]; [<reflink idref="bib56" id="ref174">56</reflink>]), followed by the number of antenatal visits ([<reflink idref="bib3" id="ref175">3</reflink>]; [<reflink idref="bib4" id="ref176">4</reflink>]; [<reflink idref="bib58" id="ref177">58</reflink>]; [<reflink idref="bib99" id="ref178">99</reflink>]) and the number of neonates ([<reflink idref="bib53" id="ref179">53</reflink>]; [<reflink idref="bib62" id="ref180">62</reflink>]; [<reflink idref="bib81" id="ref181">81</reflink>]; [<reflink idref="bib98" id="ref182">98</reflink>]).</p> <p>According to the bibliometric analysis (Figure 1), the variables doctor and nurse are often utilized as inputs in most related research. Africa was mentioned six times, Asia four times, and Europe three times. While variable outputs are extensively utilized in patients with MNCH, they are used three times more in Asia, two times more in Africa, and three times more in Europe. The literature review showed that these studies used DEA with an input orientation greater than the output orientation and an efficient frontier, assuming CRS more than VRS technology. This means that input orientation is adequate for the analysis of public hospitals to optimize the allocation of resources (Figure 2).</p> <p>Graph: Figure 2.Variables and methods used.</p> <p>This study used technical variables, including functional capacity, hospitalization days, MNC admissions, and surgical interventions (caesarean sections and others associated with the MNCH), as outputs. Hence, to our knowledge, there are limited studies concerning the technical efficiency of MNCH services in Morocco. A recent study conducted in Morocco confirmed that health services at the provincial or prefectural level were not fully utilized between 2012 and 2015 ([<reflink idref="bib23" id="ref183">23</reflink>]). Other studies analyzing health systems using one or more variables related to the MNCH, such as Arab countries ([<reflink idref="bib55" id="ref184">55</reflink>]), Asian countries ([<reflink idref="bib1" id="ref185">1</reflink>]), African countries ([<reflink idref="bib86" id="ref186">86</reflink>]), the MENA region ([<reflink idref="bib20" id="ref187">20</reflink>]; [<reflink idref="bib47" id="ref188">47</reflink>]), the Gulf region ([<reflink idref="bib2" id="ref189">2</reflink>]), and Eastern Mediterranean countries ([<reflink idref="bib84" id="ref190">84</reflink>]), have also analyzed the efficiency of healthcare in hospital settings.</p> <hd id="AN0183370754-10">Research Methodology</hd> <p></p> <hd id="AN0183370754-11">Data and Variables Sources</hd> <p>This research aimed to examine the performance of Morocco's maternal, newborn and child services network (MNCSN) across Moroccan public hospital. We used a benchmarking technique to achieve this goal, analyzing 76 MNCSN between 2012 and 2020 ([<reflink idref="bib50" id="ref191">50</reflink>]). According to the Morocco 2020's hospital, 162 hospitals were divided into 12 health directorate regions (annex 1 and 2), a 4.32% increase from 2015 to 2020. Each health directorate regions manage and coordinate one or more provinces or prefectures health delegation, and each health delegation had a hospital network contains regional, provincial, or local hospitals (Annex 1 and 2).</p> <p>The database was collected from the Directorate of Planning and Financial Resources (DPFR) and was created by the Planning and Studies Division, Health Studies, and Information Service from Ministry of health and Social Protection.</p> <p>The data collected included inputs and explanations, such as hospital, doctor, and staff paramedical data, while the outputs were MNC admission, cesarean interventions, functional capacity, and hospitalization days (Table 1).</p> <p>Table 1. Justification of Inputs and Outputs Variable.</p> <p>Graph</p> <p></p> <p> <ephtml> <table><tbody valign="top"><tr><td align="center" colspan="2">Justification of Inputs used</td></tr><tr><td align="left"> Hospital</td><td align="left">This variable represents the infrastructure and facilities available in the hospital, including the number of beds, equipment, and other resources necessary for providing maternal, neonatal, and child healthcare. Hospitals with better infrastructure are expected to provide more effective healthcare services. Hospital including infrastructure, beds, and equipment.</td></tr><tr><td align="left"> Doctor</td><td align="left">The number of doctors indicates the availability of medical professionals responsible for delivering maternal, neonatal, and child healthcare services. A higher number of doctors suggests better access to medical expertise and timely care for patients.</td></tr><tr><td align="left"> Paramedical staff</td><td align="left">Paramedical staff, including nurses, midwives, and other healthcare professionals, play a crucial role in providing support services and assistance during childbirth and neonatal care. Adequate paramedical staff ensures the smooth functioning of maternal, neonatal, and child healthcare services. Paramedical staff included number of nurses, midwives, and healthcare staff</td></tr><tr><td align="center" colspan="2">Justification of outputs used</td></tr><tr><td align="left"> MNC admission (number of maternal, neonatal, and child patient)</td><td align="left">This output variable measures the number of maternal, neonatal, and child admissions to the hospital, reflecting the demand for healthcare services in these areas. Higher admission rates may indicate increased utilization of healthcare services or higher incidence of health issues.</td></tr><tr><td align="left"> Cesarean interventions</td><td align="left">Cesarean sections are common procedures in maternal healthcare, and the number of interventions performed reflects the hospital's capability to handle complicated deliveries and ensure the safety of mothers and babies</td></tr><tr><td align="left"> Functional capacity</td><td align="left">Functional capacity refers to the hospital's ability to provide maternal, neonatal, and child healthcare services efficiently, including timely interventions, adequate staffing, and proper infrastructure. Assessing functional capacity helps identify areas for improvement in service delivery.</td></tr><tr><td align="left"> Hospitalization days</td><td align="left">This variable measure the duration of hospital stays for maternal, neonatal, and child patients. Lower hospitalization days indicate efficient management of cases and timely discharge, which can reduce healthcare costs and improve patient outcomes.</td></tr></tbody></table> </ephtml> </p> <p>These variables are essential for evaluating the effectiveness of maternal, neonatal, and child healthcare services in Moroccan hospitals and align with the objectives of Sustainable Development Goal 3 (SDG 3) aimed at ensuring healthy lives and promoting well-being for all at all ages.</p> <hd id="AN0183370754-12">Method</hd> <p>DEAP v2.1 was used to generate the results of the first stage, which employed the DEA technique. The second step involved Tobit regression analysis, which was conducted using STATA 18 software.</p> <p>The calculation of technical efficiency relies on the DEA method, which involves the virtual combination of ratios between all outputs and inputs of each DMU. This is achieved by utilizing the CRS and VRS models with input orientation ([<reflink idref="bib16" id="ref192">16</reflink>]).</p> <p>We can consider the total number of Maternal Newborn Child Services Network (MNCSN) across Moroccan public hospital in each province or prefecture health delegation. Each MNCSN uses a combination of U inputs to produce U' outputs.</p> <p> <ephtml> <math xmlns="http://www.w3.org/1998/Math/MathML"><mrow><msub><mi>A</mi><mrow><mi>m</mi><mi>n</mi><mi>c</mi><mi>s</mi><mi>n</mi><mo>=</mo></mrow></msub><mrow><mo>(</mo><mrow><msub><mi>a</mi><mrow><mn>1</mn><mi>m</mi><mi>n</mi><mi>c</mi><mi>s</mi><mi>n</mi></mrow></msub><mo>,</mo><msub><mi>a</mi><mrow><mn>2</mn><mi>m</mi><mi>n</mi><mi>c</mi><mi>s</mi><mi>n</mi></mrow></msub><mo>,</mo><mo>...</mo><mo>,</mo><msub><mi>a</mi><mrow><mi>U</mi><mi>m</mi><mi>n</mi><mi>c</mi><mi>s</mi><mi>n</mi></mrow></msub></mrow><mo>)</mo></mrow></mrow></math> </ephtml> : observed input vectors of the <emph>i</emph>th MNCSN, and <ephtml> <math xmlns="http://www.w3.org/1998/Math/MathML"><mrow><msub><mi>B</mi><mrow><mi>m</mi><mi>n</mi><mi>c</mi><mi>s</mi><mi>n</mi><mo>=</mo></mrow></msub><mrow><mo>(</mo><mrow><msub><mi>b</mi><mrow><mn>1</mn><mi>m</mi><mi>n</mi><mi>c</mi><mi>s</mi><mi>n</mi></mrow></msub><mo>,</mo><msub><mi>b</mi><mrow><mn>2</mn><mi>m</mi><mi>n</mi><mi>c</mi><mi>s</mi><mi>n</mi></mrow></msub><mo>,</mo><mo>...</mo><mo>,</mo><msub><mi>b</mi><mrow><msup><mi>U</mi><mo>′</mo></msup><mi>m</mi><mi>n</mi><mi>c</mi><mi>s</mi><mi>n</mi></mrow></msub></mrow><mo>)</mo></mrow></mrow></math> </ephtml> : observed output vectors of the <emph>i</emph>th MNCSN.</p> <p>The various works of Farrell have emphasized these results ([<reflink idref="bib43" id="ref193">43</reflink>]) and the use of a nonparametric frontier.</p> <p>Technical efficiency model (<reflink idref="bib1" id="ref194">1</reflink>). <ephtml> <math xmlns="http://www.w3.org/1998/Math/MathML"><mrow><mo>{</mo><mtable columnalign="center"><mtr><mtd><mrow><mi>E</mi><mi>T</mi><mo>=</mo><mi mathvariant="italic">min</mi><mo /><mi>λ</mi></mrow></mtd></mtr><mtr><mtd><mrow><mtext>Sujet</mtext><mo /><mi mathvariant="normal">à</mi></mrow></mtd></mtr><mtr><mtd><mrow><msub><mo>∑</mo><mi>i</mi></msub><msub><mi>υ</mi><mi>i</mi></msub><msub><mi>b</mi><mrow><msup><mi>u</mi><mo>′</mo></msup><mi>i</mi><mo>≥</mo></mrow></msub><msub><mi>b</mi><mrow><mrow><msup><mi>u</mi><mo>′</mo></msup><mtext>mncsn</mtext></mrow><mo>)</mo></mrow></msub><mo /><mrow><mo>(</mo><mrow><mi>m</mi><mo>=</mo><mn>1</mn><mo>,</mo><mo>...</mo><mo>,</mo><msup><mi>U</mi><mo>′</mo></msup></mrow><mo>)</mo></mrow></mrow></mtd></mtr><mtr><mtd><mrow><msub><mo>∑</mo><mi>i</mi></msub><msub><mi>υ</mi><mi>i</mi></msub><msub><mi>a</mi><mrow><mi>u</mi><mi>i</mi></mrow></msub><mo>≤</mo><msub><mi>λ</mi><mrow><mi>e</mi><mi>p</mi></mrow></msub><msub><mi>a</mi><mrow><mi>u</mi><mi>m</mi><mi>n</mi><mi>c</mi><mi>s</mi><mi>n</mi></mrow></msub><mo /><mrow><mo>(</mo><mrow><mi>n</mi><mo>=</mo><mn>1</mn><mo>,</mo><mo>...</mo><mo>,</mo><mi>U</mi></mrow><mo>)</mo></mrow></mrow></mtd></mtr><mtr><mtd><mrow><msub><mo>∑</mo><mi>i</mi></msub><msub><mi>υ</mi><mi>i</mi></msub><mo>=</mo><mn>1</mn><mo>.</mo><mo /><mrow><mo>(</mo><mrow><msub><mi>υ</mi><mi>i</mi></msub><mo>≥</mo><mn>0</mn></mrow><mo>)</mo></mrow></mrow></mtd></mtr></mtable></mrow></math> </ephtml></p> <p>Graph</p> <p>The method determines the efficiency score to be assigned to each entity by solving the linear programming problem based on the input orientation and constant and variable returns to scale assumptions.</p> <p>There are a = 3 inputs and b = 4 outputs for an MNCSN; mncsn = 76 DMUs for 2012–2020; A is the input matrix (u × mncsn); B is the output matrix (u <ephtml> <math xmlns="http://www.w3.org/1998/Math/MathML"><mrow><mo>′</mo></mrow></math> </ephtml> × mncsn); and z is a vector of constants (<emph>n</emph> × 1) that measures the weights used to measure the location of a DMU. In the above problem, λ is a scalar ranging from 1 to ∞.</p> <p>The Malmquist index (MI) allows for the analysis of total production efficiency over two or more periods, providing a measure of total factor productivity that complements the DEA method ([<reflink idref="bib66" id="ref195">66</reflink>]). The objective is to analyze overall production efficiency across multiple periods; for this purpose, we apply the DEA method in combination with the Malmquist production index (MPI), which is a preferred tool for panel data analysis. Malmquist laid the foundation for this index ([<reflink idref="bib11" id="ref196">11</reflink>]). Färe introduced the MI framework in the DEA literature ([<reflink idref="bib41" id="ref197">41</reflink>]). The MI always compares two adjacent periods ([<reflink idref="bib41" id="ref198">41</reflink>], [<reflink idref="bib42" id="ref199">42</reflink>]; [<reflink idref="bib66" id="ref200">66</reflink>]; [<reflink idref="bib83" id="ref201">83</reflink>]).</p> <p>The MI always compares two adjacent periods ([<reflink idref="bib83" id="ref202">83</reflink>]). <ephtml> <math xmlns="http://www.w3.org/1998/Math/MathML"><mrow><msubsup><mi>T</mi><mi>o</mi><mi>t</mi></msubsup><mrow><mo>(</mo><mrow><msup><mi>u</mi><mi>t</mi></msup><mo>,</mo><msup><mi>a</mi><mi>t</mi></msup></mrow><mo>)</mo></mrow></mrow></math> </ephtml> and <ephtml> <math xmlns="http://www.w3.org/1998/Math/MathML"><mrow><msubsup><mi>T</mi><mi>o</mi><mrow><mi>t</mi><mo>+</mo><mn>1</mn></mrow></msubsup><mrow><mo>(</mo><mrow><msup><mi>u</mi><mrow><mi>t</mi><mo>+</mo><mn>1</mn></mrow></msup><mo>,</mo><msup><mi>a</mi><mrow><mi>t</mi><mo>+</mo><mn>1</mn></mrow></msup></mrow><mo>)</mo></mrow></mrow></math> </ephtml> are intraperiod distance functions.</p> <p>Malmquist Production Index in Periods t and t + 1. <ephtml> <math xmlns="http://www.w3.org/1998/Math/MathML"><mrow><msubsup><mi mathvariant="normal">M</mi><mi mathvariant="normal">o</mi><mi mathvariant="normal">t</mi></msubsup><mo>=</mo><mfrac><mrow><msubsup><mi mathvariant="normal">T</mi><mi mathvariant="normal">o</mi><mrow><mi mathvariant="normal">t</mi><mo>+</mo><mn>1</mn></mrow></msubsup><mrow><mo>(</mo><mrow><msup><mi mathvariant="normal">u</mi><mrow><mi mathvariant="normal">t</mi><mo>+</mo><mn>1</mn></mrow></msup><mo>,</mo><msup><mi mathvariant="normal">a</mi><mrow><mi mathvariant="normal">t</mi><mo>+</mo><mn>1</mn></mrow></msup></mrow><mo>)</mo></mrow></mrow><mrow><msubsup><mi mathvariant="normal">T</mi><mi mathvariant="normal">o</mi><mi mathvariant="normal">t</mi></msubsup><mrow><mo>(</mo><mrow><msup><mi mathvariant="normal">u</mi><mi mathvariant="normal">t</mi></msup><mo>,</mo><msup><mi mathvariant="normal">a</mi><mi mathvariant="normal">t</mi></msup></mrow><mo>)</mo></mrow></mrow></mfrac><msubsup><mrow><mo /><mo>;</mo><mo /><mi mathvariant="normal">M</mi></mrow><mi mathvariant="normal">o</mi><mrow><mi mathvariant="normal">t</mi><mo>+</mo><mn>1</mn></mrow></msubsup><mo>=</mo><mfrac><mrow><msubsup><mi mathvariant="normal">T</mi><mi mathvariant="normal">o</mi><mrow><mi mathvariant="normal">t</mi><mo>+</mo><mn>1</mn></mrow></msubsup><mrow><mo>(</mo><mrow><msup><mi mathvariant="normal">u</mi><mrow><mi mathvariant="normal">t</mi><mo>+</mo><mn>1</mn></mrow></msup><mo>,</mo><msup><mi mathvariant="normal">a</mi><mrow><mi mathvariant="normal">t</mi><mo>+</mo><mn>1</mn></mrow></msup></mrow><mo>)</mo></mrow></mrow><mrow><msubsup><mi mathvariant="normal">T</mi><mi mathvariant="normal">o</mi><mrow><mi mathvariant="normal">t</mi><mo>+</mo><mn>1</mn></mrow></msubsup><mrow><mo>(</mo><mrow><msup><mi mathvariant="normal">u</mi><mi mathvariant="normal">t</mi></msup><mo>,</mo><msup><mi mathvariant="normal">a</mi><mi mathvariant="normal">t</mi></msup></mrow><mo>)</mo></mrow></mrow></mfrac></mrow></math> </ephtml></p> <p>Graph</p> <p>The Tobit model, also known as the censored regression model, is utilized to analyze data in which the dependent variable is observed only under specific conditions, resulting in censorship. This model is commonly applied when the dependent variable has either a lower or upper limit, and observations that exceed these boundaries are not completely recorded but are instead considered censored or truncated.</p> <p>The mathematical expression of the Tobit model can be represented as follows ([<reflink idref="bib5" id="ref203">5</reflink>]): <ephtml> <math xmlns="http://www.w3.org/1998/Math/MathML"><mrow><mtext>Yi</mtext><mo>=</mo><mtext>Xi</mtext><mi>β</mi><mo>+</mo><mi>ϵ</mi><mi mathvariant="normal">i</mi></mrow></math> </ephtml></p> <p>Graph</p> <hd id="AN0183370754-13">Results</hd> <p></p> <hd id="AN0183370754-14">Descriptive Analysis of Input and Output Variable</hd> <p>The Table 2 presents descriptive statistics for inputs and outputs variables from 2012 to 2020. The statistical measures include the average (mean), standard deviation (standard deviation), maximum (maximum), and minimum (minimum):</p> <p>Table 2. Descriptive Analyses Input and Outputs 2012–2020.</p> <p>Graph</p> <p></p> <p> <ephtml> <table><thead valign="top"><tr><th align="left" /><th align="center" colspan="4">Output</th><th align="center" colspan="3">Input</th></tr><tr><th align="left">Variable</th><th align="center">Functional Beds Capacity</th><th align="center">Hospitalization Days</th><th align="center">MNC Admission</th><th align="center">Cesarean Interventions</th><th align="center">Staff Paramedical</th><th align="center">Doctor</th><th align="center">Hospital</th></tr></thead><tbody valign="top"><tr><td align="left">Average</td><td align="char" char=".">282.6</td><td align="char" char=".">61,296.7</td><td align="char" char=".">17,365.2</td><td align="char" char=".">4034.1</td><td align="char" char=".">218.8</td><td align="char" char=".">80.8</td><td align="char" char=".">2.2</td></tr><tr><td align="left">Std. Dev.</td><td align="char" char=".">12.15</td><td align="char" char=".">10,977.4</td><td align="char" char=".">25,106.84</td><td align="char" char=".">598.42</td><td align="char" char=".">141.67</td><td align="char" char=".">97.75</td><td align="char" char=".">0.5</td></tr><tr><td align="left">Max.</td><td align="char" char=".">2645.0</td><td align="char" char=".">650,126.0</td><td align="char" char=".">689,203.0</td><td align="char" char=".">33,269.0</td><td align="char" char=".">2579.0</td><td align="char" char=".">1661.0</td><td align="char" char=".">15.0</td></tr><tr><td align="left">Min.</td><td align="char" char=".">16.0</td><td align="char" char=".">772.0</td><td align="char" char=".">222.0</td><td align="char" char=".">4.0</td><td align="char" char=".">18.0</td><td align="char" char=".">1.0</td><td align="char" char=".">1.0</td></tr></tbody></table> </ephtml> </p> <p>The average represents the central tendency of the data, indicating the typical or average value of each variable. For example, the average functional bed capacity was 282.6, indicating 282.6 functional beds available in hospitals each year.</p> <p>The standard deviation measures the dispersion or variability of data points around the mean, with higher standard deviations indicating greater variability and lower standard deviations indicating less variability. For instance, the standard deviation for hospitalization days was 10,977.4, indicating considerable variability in the number of hospitalization days across the years.</p> <p>The maximum value is the highest value observed for each variable during the study period, such as the maximum number of hospitalization days recorded at 650,126.0.</p> <p>The minimum value is the lowest value observed for each variable, such as the minimum number of functional beds capacity recorded at 16.0. These descriptive statistics offer insights into the distribution and characteristics of the data.</p> <p>The correlation (Table 3) reveals a robust positive correlation among functional bed capacity, hospitalization days, admission patient-related MNCS, surgical interventions-related MNCS, staff paramedical, doctors, and overall hospital activity. Longer hospitalization durations correspond to increased admissions, surgical interventions, staff, doctor presence, and overall hospital activity. Similarly, higher admissions are linked to elevated occurrences of surgical interventions, staff, doctor presence, and overall hospital activity. Additionally, the presence of paramedical staff correlates positively with the presence of doctors and overall hospital activity. A higher doctor presence is associated with heightened hospital activity. These correlations underscore significant interdependencies among various factors within the hospital setting, indicating that modifications in one variable are likely to impact others. The Table 3 emphasizes the necessity of addressing these interdependencies to effectively manage MNCSN resources and operations. This is the subject of the following sections utilizing DEA and Tobit regression.</p> <p>Table 3. Correlation Between Inputs and Outputs Variables.</p> <p>Graph</p> <p></p> <p> <ephtml> <table><thead valign="top"><tr><th align="left" /><th align="center" colspan="4">Output</th><th align="center" colspan="3">Input</th></tr><tr><th align="left" /><th align="center">Functional Beds Capacity</th><th align="center">Hospitalization Days</th><th align="center">MNC Admission</th><th align="center">Cesarean Interventions</th><th align="center">Staff Paramedical</th><th align="center">Doctor</th><th align="center">Hospital</th></tr></thead><tbody valign="top"><tr><td align="left">Functional beds capacity</td><td align="char" char=".">1.00</td><td align="left" /><td align="left" /><td align="left" /><td align="left" /><td align="left" /><td align="left" /></tr><tr><td align="left">Hospitalization days</td><td align="char" char=".">0.99</td><td align="char" char=".">1.00</td><td align="left" /><td align="left" /><td align="left" /><td align="left" /><td align="left" /></tr><tr><td align="left">MNC admission</td><td align="char" char=".">0.86</td><td align="char" char=".">0.85</td><td align="char" char=".">1.00</td><td align="left" /><td align="left" /><td align="left" /><td align="left" /></tr><tr><td align="left">Cesarean interventions</td><td align="char" char=".">0.96</td><td align="char" char=".">0.96</td><td align="char" char=".">0.87</td><td align="char" char=".">1.00</td><td align="left" /><td align="left" /><td align="left" /></tr><tr><td align="left">Staff paramedical</td><td align="char" char=".">0.96</td><td align="char" char=".">0.95</td><td align="char" char=".">0.87</td><td align="char" char=".">0.95</td><td align="char" char=".">1.00</td><td align="left" /><td align="left" /></tr><tr><td align="left">Doctor</td><td align="char" char=".">0.83</td><td align="char" char=".">0.83</td><td align="char" char=".">0.78</td><td align="char" char=".">0.89</td><td align="char" char=".">0.89</td><td align="char" char=".">1.00</td><td align="left" /></tr><tr><td align="left">Hospital</td><td align="char" char=".">0.88</td><td align="char" char=".">0.87</td><td align="char" char=".">0.83</td><td align="char" char=".">0.84</td><td align="char" char=".">0.90</td><td align="char" char=".">0.74</td><td align="char" char=".">1.00</td></tr></tbody></table> </ephtml> </p> <hd id="AN0183370754-15">Data Envelopment Analysis</hd> <p>Figure 3 and Table 4 shows the descriptive data for technical efficiency, which were used in the VRSI and VRSO models to provide a more nuanced understanding of efficiency ratings across various provinces from 2012 to 2020.</p> <p>Graph: Figure 3.Average technical efficiency for each MNCHN for each health delegation 2012–2020.</p> <p>Table 4. MNCSN for Each Health Delegation.</p> <p>Graph</p> <p></p> <p> <ephtml> <table><thead valign="top"><tr><th align="left">Health Directorate Region</th><th align="center">MNCSN for Each Health Delegation</th></tr></thead><tbody valign="top"><tr><td align="left">1. Tanger -Tétouan- Al Hoceima</td><td align="left">Al Hoceima (1), Chefchaouen (2), Larache (3), Mdiq-Fnideq (4), Ouezzane (5), TangerAssilah (6), Tetouan (7)</td></tr><tr><td align="left">2. Oriental</td><td align="left">Berkane (8), Figuig (9), Guercif (10), Jerada (11), Nador (12), Oujda Angad (13), Taourirt (14)</td></tr><tr><td align="left">3. Fès-Meknès</td><td align="left">Boulemane (15), El Hajeb (16), Fes (17), Ifrane (18), Meknes (19), Sefrou (20), Taounate (21), Taza (22)</td></tr><tr><td align="left">4. Rabat-Salé-Kénitra</td><td align="left">Kenitra (23), Khemisset (24), Rabat (25), Sale (26), Sidi Kacem (27), Sidi Slimane (28), Skhirate-Tema (29)</td></tr><tr><td align="left">5. Béni Mellal- Khénifra</td><td align="left">Azilal (30), Beni Mellal (31), Fkih Ben Saleh (32), Khouribga (33), Khenifra (34)</td></tr><tr><td align="left">6. Casablanca-Settat</td><td align="left">Benslimane (35), Berrechid (36), Settat (37), Mediouna (38), Mohammedia (39), Nouaceur (40), CasablancaAnfa (41), Al Fida-Mers (42), Aïn Sebaa (43), Hay Hassani (44), Aïn Chock (45), Sidi Bernoussi (46), Ben Msick (47), My Rachid (48), El Jadida (49), Sidi Bennour (50)</td></tr><tr><td align="left">7. Marrakech–Safi</td><td align="left">Al Haouz (51), Chichaoua (52), El Kelaa Sraghn (53), Essaouira (54), Marrakech (55), Rehamena (56), Safi (57), Youssoufia (58)</td></tr><tr><td align="left">8. Darâa-Tafilalet</td><td align="left">Errachidia (59), Midelt (60), Ouarzazate (61), Tinghir (62), Zagora (63)</td></tr><tr><td align="left">9. Souss-Massa</td><td align="left">Agadir Ida (64), Chtouka (65), Inezgane (66), Tata (67), Taroudant (68), Tiznit (69)</td></tr><tr><td align="left">10. Guelmim–Oued Noun</td><td align="left">Assa-Zag (70), Guelmim (71), Tan Tan (72), Sidi Ifni (73)</td></tr><tr><td align="left">11. Laâyoune-Sakia El Hamra</td><td align="left">Boujdour (74) and Es Semara (75)</td></tr><tr><td align="left">12. Eddakhla-Oued Eddahab</td><td align="left">Oued Ed-Dahab (76)</td></tr></tbody></table> </ephtml> </p> <p>The mean TE was 0.76 for the VRS-I score, that mean MNCSN indicate that healthcare systems in the MNCSN can save around 24% of their health resources (i.e., hospital, doctor, and staff paramedical data) while achieving the same level of health outputs (i.e., MNC admission, cesarean interventions, functional capacity, and hospitalization days), and 0.23 for the VRS-O score, that mean MNCSN have an opportunity to improve their health outputs by almost 77% using the same level of resources (Figure 4).</p> <p>Graph: Figure 4.Descriptive analyses of technical efficiency score of MNCSN 2012–2020.</p> <p>The median of technical efficiency score was also 0.81 for the VRSI, whereas the median was 0.17 for the VRSO. The VRSI had a greater mean, median and mode than did the VRSO, indicating a concentration of efficiency ratings at higher levels. The mode of technical efficiency score was 1 in VRSI indicated that a specific MNCH regularly achieved full efficiency. In contrast, VRSO has a greater spread of efficiency ratings, with a lower mean, median, and mode, indicating greater variability in MNCH performance (Figure 4).</p> <p>The annual efficiency ratings showed temporal oscillations that indicate dynamic changes in MNCSN efficiency. The VRSI ranges from 0.242 to 20 for MNCSNs (MNCSN Al-Hoceima (DMO = 1), Mdiq-Fnideq (<reflink idref="bib4" id="ref204">4</reflink>), Tanger-Assilah (<reflink idref="bib6" id="ref205">6</reflink>), Tetouan (<reflink idref="bib7" id="ref206">7</reflink>), Figuig (<reflink idref="bib9" id="ref207">9</reflink>) ... (Figure 3 and Table 4)), while the VRSO scores vary between MNCSN Al-Haouz [<reflink idref="bib51" id="ref208">51</reflink>] with a score of 0.027 and 3 MNCSN with a score of 1 (MNCSN Rabat (<reflink idref="bib25" id="ref209">25</reflink>), Mediouna (<reflink idref="bib38" id="ref210">38</reflink>) and Ben Msick (<reflink idref="bib47" id="ref211">47</reflink>)). These fluctuations may reflect changing MNCSN capabilities and resource usage strategies during the last nine years (Figure 3).</p> <p>The descriptive statistics of first stage provided information on the predominant trend, spread, and structure of the technical efficiency score distributions. Both models' positive skewness and negative kurtosis indicated distributions with longer and lighter tails, respectively, than a normal distribution. This demonstrated that while many MNCSN provinces have relatively high efficiency, some have inferior efficiency, contributing to the observed skewness.</p> <p>Figure 5 shows that 10 MNCHs were referred to, with Casablanca-Anfa (<reflink idref="bib41" id="ref212">41</reflink>) referred to 30 times and Khemisset (<reflink idref="bib24" id="ref213">24</reflink>) referred to 24 times under the VRSI. Under VRSO, the number of peer counts was 3 at MNCH, with Ben Msick (<reflink idref="bib47" id="ref214">47</reflink>) having 71, Rabat (<reflink idref="bib25" id="ref215">25</reflink>) having 22, and Mediouna (<reflink idref="bib38" id="ref216">38</reflink>) having 17.</p> <p>Graph: Figure 5.Peer count for MNCSN 2012–2020.</p> <hd id="AN0183370754-16">Malmquist Index</hd> <p>The average total factor productivity (TFP) of MNC hospitals decreased between 2012 and 2020 (Figure 6).</p> <p>Graph: Figure 6.Malmquist Index mean MNCSN 2012–2020.</p> <p>The findings revealed that 92% (72 decision-making units) of the MNCHs had a TFP of less than 1 under VRSI, while 6% had a productivity gain of more than 1, explaining why these MNCHs harmed their total productivity factors. This finding contrasts with the 14.4% observed in the survey from 2012 to 2015 ([<reflink idref="bib23" id="ref217">23</reflink>]).</p> <p>Under the input orientation, the average TFP was 1.077, indicating a 7.7% improvement in productivity during the 9 years of research, as influenced by EffchI (1.012) and TechchI (1.065). The findings indicated six periods with a productivity gain greater than one, with a score of 1.323, 1.019, 1.108, 1.031, 1.093, and 1.266 in the periods 2012–2013, 2014–2015, 2015–2016, 2017–2018, 2018–2019, and 2019–2020, respectively. In contrast, two periods had PTF values less than one: 2013–2014 (0.945) and 2016–2017 (0.899).</p> <p>However, in the output direction, the average Malmquist index was 0.883, with an 8.7% decrease in productivity during the 9 years of study attributed to Techch (0.76). The findings revealed three periods of production greater than one: 1.256, 1.17, and 1.086 in 2013–2014, 2016–2017, and 2017–2018, respectively. In contrast, three periods had PTF values smaller than one: 2012–2013 (0.648), 2014–2015 (0.782), 2016–2016 (0.578), 2018–2019 (0.929), and 2019–2020 (0.852).</p> <hd id="AN0183370754-17">Tobit Regression</hd> <p>Tobit regression analysis was employed to investigate inefficiency in MNCSN under the VRSI and VRSO models, elucidating the impact of various factors on technical inefficiency. We considered six hypotheses to analyze the factors influencing efficiency, comprising models 1, 2, and 3 under input variables (Table 5), and models 3, 4, and 4 under output orientation (Table 6). The dependent variable was technical efficiency, while in models 1 and 4, inputs variables were used as independent variables, in models 2 and 5, the output variables served as independent variables, and in models 3 and 6, both inputs and outputs were used as independent variables.</p> <p>Table 5. Tobit Regression Under DEA-VRSI.</p> <p>Graph</p> <p></p> <p> <ephtml> <table><thead valign="top"><tr><th align="left">Inefficiency under DEAVRS<sub>I</sub></th><th align="center" colspan="2">Model 1</th><th align="center" colspan="2">Model 2</th><th align="center" colspan="2">Model 3</th></tr><tr><th align="left" /><th align="center">Coef. (Std. Err.)</th><th align="center">t-P>|t|</th><th align="center">Coef. (Std. Err.)</th><th align="center">t-P>|t|</th><th align="center">Coef. (Std. Err.)</th><th align="center">t-P>|t|</th></tr></thead><tbody valign="top"><tr><td align="left">Hospital</td><td align="center">−0.59 (0.153)</td><td align="center">−0.39–0.701</td><td align="left" /><td align="left" /><td align="center">−.046 (.146)</td><td align="center">−0.310.754</td></tr><tr><td align="left">Doctor</td><td align="center">0.003 (0.002)</td><td align="center">1.29–0.202</td><td align="left" /><td align="left" /><td align="center">.0004 (.0028)</td><td align="center">0.15–0.884</td></tr><tr><td align="left">Staff paramedical</td><td align="center">−0.003 (0.002)</td><td align="center">−1.53–0.131</td><td align="left" /><td align="left" /><td align="center">−.001 (.002)</td><td align="center">−0.71–0.482</td></tr><tr><td align="left">Functional beds capacity</td><td align="left" /><td align="left" /><td align="center">−.008 (.002)</td><td align="center">−3.45–0.001*</td><td align="center">−.008 (.002)</td><td align="center">−2.86–0.006*</td></tr><tr><td align="left">MNC hospitalization days</td><td align="left" /><td align="left" /><td align="center">.000021 (9.51)</td><td align="center">2.21–0.030**</td><td align="center">.00002 (9.79)</td><td align="center">2.03–0.046**</td></tr><tr><td align="left">MNCAdmission</td><td align="left" /><td align="left" /><td align="center">9.33 (.00001)</td><td align="center">0.08–0.934</td><td align="center">2.81 (.00001)</td><td align="center">0.24–0.814</td></tr><tr><td align="left">Cesareaninterventions</td><td align="left" /><td align="left" /><td align="center">.0001333 (.00007)</td><td align="center">1.88–0.064**</td><td align="center">.0002 (.00008)</td><td align="center">1.87–0.066**</td></tr><tr><td align="left">_cons</td><td align="center">0.770 (0.078)</td><td align="center">0.000 (---)</td><td align="center">.708 (.072)</td><td align="center">6.22–0.00</td><td align="center">.979 (.187)</td><td align="center">5.21–0.000</td></tr><tr><td align="left">/sigma</td><td align="center">Coef. (Std. Err.) =</td><td align="center">95% Conf. Interval] =</td><td align="center">Coef. (Std. Err.) =</td><td align="center">95% Conf. Interval] =</td><td align="center">Coef. (Std. Err.) =</td><td align="center">95% Conf. Interval] =</td></tr><tr><td align="left">Log likelihood</td><td align="center" colspan="2">−78.28</td><td align="center" colspan="2">−73.28</td><td align="center" colspan="2">−72.498041</td></tr><tr><td align="left">Number of obs</td><td align="center" colspan="6">76</td></tr><tr><td align="left">LR chi2 (3)</td><td align="center" colspan="2">15.72</td><td align="center" colspan="2">25.62</td><td align="center" colspan="2">27.19</td></tr><tr><td align="left">Prob > chi2</td><td align="center" colspan="2">0.0013</td><td align="center" colspan="2">0.000</td><td align="center" colspan="2">0.0003</td></tr><tr><td align="left">Pseudo R2</td><td align="center" colspan="2">0.0913</td><td align="center" colspan="2">0.1488</td><td align="center" colspan="2">0.1579</td></tr></tbody></table> </ephtml> </p> <p>1 *0.01, **0.05 and ***0.1.</p> <p>2 MNC: maternal, newborn and child.</p> <p>Table 6. Tobit Regression Under the DEA-VRSO.</p> <p>Graph</p> <p></p> <p> <ephtml> <table><thead valign="top"><tr><th align="left">Inefficiency under DEAVRS<sub>I</sub></th><th align="center" colspan="2">Model 4</th><th align="center" colspan="2">Model 5</th><th align="center" colspan="2">Model 6</th></tr><tr><th align="left" /><th align="center">Coef. (Std. Err.)</th><th align="center">t- P>|t|</th><th align="center">Coef. (Std. Err.)</th><th align="center">t- P>|t|</th><th align="center">Coef. (Std. Err.)</th><th align="center">t- P>|t|</th></tr></thead><tbody valign="top"><tr><td align="left">Hospital</td><td align="left" /><td align="left" /><td align="center">−.418 (1.318)</td><td align="center">−0.32–0.752</td><td align="center">−.2081 (.236)</td><td align="center">−0.17–0.867</td></tr><tr><td align="left">Doctor</td><td align="left" /><td align="left" /><td align="center">.048 (.020)</td><td align="center">2.40–0.019**</td><td align="center">.058 (.0204)</td><td align="center">2.85–0.006*</td></tr><tr><td align="left">Staff paramedical</td><td align="left" /><td align="left" /><td align="center">−.0430 (.017)</td><td align="center">−2.45–0.017**</td><td align="center">.017 (.021)</td><td align="center">0.84–0.406</td></tr><tr><td align="left">Functional bedscapacity</td><td align="center">−.049 (.022)</td><td align="center">−2.25–0.027**</td><td align="left" /><td align="left" /><td align="center">−.0719 (.023)</td><td align="center">−3.01–0.004*</td></tr><tr><td align="left">MNC hospitalization days</td><td align="center">.0001 (.00008)</td><td align="center">1.22–0.228</td><td align="left" /><td align="left" /><td align="center">.0001 (.00001)</td><td align="center">1.54–0.128</td></tr><tr><td align="left">MNCAdmission</td><td align="center">−.0001 (.0001)</td><td align="center">−0.78–0.437</td><td align="left" /><td align="left" /><td align="center">.00001 (.0001)</td><td align="center">0.20–0.842</td></tr><tr><td align="left">Cesareaninterventions</td><td align="center">.0008 (.0006)</td><td align="center">1.32–0.192</td><td align="left" /><td align="left" /><td align="center">−.0007 (.0007)</td><td align="center">−0.95–0.346</td></tr><tr><td align="left">Cons</td><td align="center">14.18 (1.36)</td><td align="center">10.44–0.00</td><td align="center">15.04 (1.61)</td><td align="center">9.33–0.000</td><td align="center">14.72 (1.61)</td><td align="center">9.15–0.000</td></tr><tr><td align="left">/sigma</td><td align="center">Coef. (Std. Err.) = 7.58–.63</td><td align="center">95% Conf. Interval] = 6.32–8.84</td><td align="center">Coef. (Std. Err.) = 7.808 (.651)</td><td align="center">[95% Conf. Interval] = 6.51–9.11</td><td align="center">Coef. (Std. Err.) = 6.929–.575</td><td align="center">[95% Conf. Interval] = 5.78–8.07</td></tr><tr><td align="left">Log likelihood</td><td align="center" colspan="2">253.85243</td><td align="center" colspan="2">−256.21296</td><td align="center" colspan="2">--246.32363</td></tr><tr><td align="left">Number of obs</td><td align="center" colspan="6">76</td></tr><tr><td align="left">LR chi2 (3)</td><td align="center" colspan="2">29.02</td><td align="center" colspan="2">24.30</td><td align="center" colspan="2">44.07</td></tr><tr><td align="left">Prob > chi2</td><td align="center" colspan="6">0.00</td></tr><tr><td align="left">Pseudo R2</td><td align="center" colspan="2">0.0541</td><td align="center" colspan="2">0.0453</td><td align="center" colspan="2">0.0821</td></tr></tbody></table> </ephtml> </p> <ulist> <item>3 *0.01, **0.05 and ***0.1.</item> <item>4 MNC: maternal, newborn and child.</item> </ulist> <p>Under input orientation (Table 5), the results showed that Models 2 and 3 had significant factors, including functional bed capacity, which had a greater significance at 0.01, followed by hospitalization days and cesarean interventions, which had medium significance at 0.05. These factors significantly influence inefficiency, with negative coefficients indicating that higher values reduce inefficiency. Fewer influencing factors, such as doctor, staff paramedical, MNC admission, and cesarean interventions, have little influence on inefficiency. According to the intercept, the constant component (_cons) in all the models is statistically significant, suggesting a degree of inefficiency that the included variables cannot account for. The pseudo R2 values are relatively low, indicating that the models explain only a small amount of the variability in inefficiency.</p> <p>Under output orientation (Table 6), Model 6 was significant at 0.01 for doctor and functional bed capacity. Model 5 showed that the doctor and staff paramedical variables had a substantial impact on inefficiency (<emph>p</emph> <.05), with positive coefficients indicating greater inefficiency. The constant term (cons) is statistically significant, suggesting a certain amount of inefficiency. The pseudo R2 values remain moderate, indicating that the models have minimal explanatory ability.</p> <hd id="AN0183370754-18">Discussion</hd> <p>Morocco's healthcare system is undergoing transformation through recent reforms and strategies, but existing weaknesses are not only due to the Ministry of Health's management but also to the management of the MNCS across Moroccan hospital Establishment. This study employed DEA and Malmquist indices to assess maternal and child services (MNCS) efficiency and Tobit regression for various MNCHs from 2012–2020.</p> <p>The findings revealed temporal oscillations in MNCSN efficiency, between 76% under VRSI and 23% under VRSO. The results indicate concentration at higher efficiency levels under VRSI, while under VRSO exhibit variability, suggesting dynamic changes in MNCSN capabilities and resource usage. The Malmquist Index showed a decrease in average total factor productivity, with fluctuations in productivity gains and losses over the years. The peer count analysis identified the referred MNCSN, providing insights into collaborative learning opportunities. The findings also highlight a significant change in the overall efficiency score of MNCSN in Morocco from 2012 to 2020 under the input orientation. This implies that the factors influencing MNCSN efficiency in Morocco have experienced notable shifts over the nine-year span. Moreover, there is evidence indicating that MNCSN across hospital establishments, along with their associated programs and strategic changes during the COVID-19 crisis, have had a positive impact on the performance of MNCSN in Morocco. Tobit regression analysis underlines factors influencing technical inefficiency, with varying significance levels.</p> <p>Under VRSI, the Functional bed scapacity, MNC hospitalization days, MNC admission and Cesarean interventions influences on inefficiency, while under VRSO, the doctor, staff paramedical, and functional beds capacity influences on inefficiency. Furthermore, there is no empirical evidence supporting the hypothesis that health systems in affluent provinces or prefectures such as Rabat (administrative capital), Casablanca (economic capital), Marrakech (tourist capital), and Tangier are inherently more efficient than those operating in less prosperous provinces or prefectures. This conclusion is reinforced by the results of the first stage of DEA analysis, which indicates that the efficiency frontier encompasses both affluent and less affluent provinces or prefectures. Additionally, the structure and organization of MNCSN across hospital establishments does not appear to significantly impact the efficiency of maternal, newborn, and child health services.</p> <p>Comparatively, the other papers showed a wide range of data on maternal and child healthcare efficiency across countries. In Ethiopia, hospitals suffer resource restrictions and differ in their efficiency levels ([<reflink idref="bib99" id="ref218">99</reflink>]). Southwest Ethiopia's newborn health services had inadequate technical efficiency ([<reflink idref="bib98" id="ref219">98</reflink>]). In Guangxi, China, experiences generally poor efficiency, necessitating regulatory changes ([<reflink idref="bib91" id="ref220">91</reflink>]). Tamil Nadu's emergency obstetric care showed room for improvement, particularly at taluk-level institutions ([<reflink idref="bib81" id="ref221">81</reflink>]). The study of comprehensive emergency obstetric treatment in Tamil Nadu underlines the need for additional research ([<reflink idref="bib80" id="ref222">80</reflink>]). Kenya and Swaziland's combined HIV and SRH programs have variable implications for technical efficiency ([<reflink idref="bib77" id="ref223">77</reflink>]). In Shanxi, China, obstetrics and gynecology units were inefficient ([<reflink idref="bib65" id="ref224">65</reflink>]). Northwest Ethiopia's newborn health care has demonstrated overall technical efficiency and is impacted by a variety of factors ([<reflink idref="bib62" id="ref225">62</reflink>]). Kwazulu-Natal's main clinics were inefficient and required input and output changes ([<reflink idref="bib59" id="ref226">59</reflink>]). In addition, the efficiency of maternal and child health institutions was low in Hubei Province, indicating a need for better resource allocation ([<reflink idref="bib57" id="ref227">57</reflink>]). Turkish research has examined efficiency in maternity and child health hospitals, with a focus on resource usage ([<reflink idref="bib56" id="ref228">56</reflink>]). The Norwegian research on child and adolescent mental health services investigated productivity increases while considering case mix modifications ([<reflink idref="bib46" id="ref229">46</reflink>]). The performance of new born care units in Scotland City demonstrates possible inefficiencies, indicating a need for improvement ([<reflink idref="bib44" id="ref230">44</reflink>]). Research on maternal health care in Northwest Ethiopia has focused on the determinants of efficiency and inefficiency ([<reflink idref="bib4" id="ref231">4</reflink>]). A study by Southeast Nigeria on mission hospitals demonstrated general efficiency but also highlighted areas for improvement (H.E. [<reflink idref="bib3" id="ref232">3</reflink>]).</p> <p>This study revealed that the efficiency of MNCH improved over 9 years, partly due to the commitment of the Ministry of Health to MNC healthcare during recent years. This led to a significant increase in healthcare expenditure, from 8% in 2018 to 12% in 2020. Despite these improvements, the focus of the facilities has primarily been on preventive care, which has not been fully effective at addressing health challenges, including the ongoing battle against the pandemic. However, from 2012 to 2020, there was some progress in healthcare provisioning, mainly through increased infrastructure, financial resources, and medical personnel. However, the overall performance of MNCH remains suboptimal. The key issues are equitable access to care, quality of healthcare provisioning, effective management practices, the absence of adequate mechanisms, and a culture of control and audit.</p> <p>The efficiency of MNCSN within the examined health delegation could be improved by enhancing the organizational management of these networks, with a focus on transparency, healthcare quality control, and addressing issues of corruption and adherence to the rule of law. Regional health directorates may consider leveraging big-data analytics and techniques in formulating and implementing healthcare management strategies to achieve this goal. This approach could help reduce the wastage of healthcare resources and enhance efficiency, effectiveness, and the success rates of healthcare policies. Additionally, the implementation of appropriate incentives for the healthcare workforce may contribute to enhancing the efficiency of MNCSN within the examined health delegation. Based on the findings of the second stage of analysis, MNCSN should exert additional efforts to combat corruption at all levels, as these efforts appear to positively impact healthcare efficiency. Conversely, while advocating for increased investment in healthcare workers and infrastructure, MNCSN should also prioritize measures to control corruption and relationship between personnel staff and patient.</p> <hd id="AN0183370754-19">Conclusion and Recommendation</hd> <p>To enhance maternal, newborn and child service performance to achieve SDG 3, health policymakers require information on how effectively MNC hospitals utilize the resources they receive. This research illustrated the application of DEA methods, the Malmquist index, and Tobit regression at the MNCH level in each region to better understand the variation in efficiency of MNCHs in Moroccan public hospitals.</p> <p>The conclusion presented empirical evidence of the average technical efficiency scores for MNCHs in the first stage, characterized by inefficiency. The Malmquist Index influenced scale efficiency change, and technological change promoted the MNCH, positively impacting overall productivity. In the second stage, factors such as functional bed capacity and doctor status negatively influenced MNCH inefficiency.</p> <p>Monitoring, evaluation, and control parameters in MNCSN across hospital establishments in Morocco are crucial for understanding the mechanisms that promote the utilization of resource care services within highly efficient MNCSN networks, using DEA and Tobit regression. By engaging with managers and improving care quality, policymakers, managers, and healthcare personnel can develop context-specific strategies to assist less efficient hospitals in enhancing their services and better addressing the unmet needs for MNC hospital services in Morocco. These strategies can be developed through collaboration with managers and healthcare staff, focusing on improving the quality of care and enhancing the overall efficiency of the hospital network.</p> <p>The study focuses on optimizing healthcare systems and improving outcomes for maternal, neonatal, and child health. It uses methodologies like DEA and Tobit regression to identify efficiency drivers in Maternal, Neonatal, and Child Health (MNCH) service networks. The findings reveal areas for improvement in resource utilization and healthcare outcomes. The study provides evidence-based insights that can inform policy decisions, enabling strategic resource allocation and organizational changes. Understanding factors contributing to inefficiency within MNCH service networks enables better resource allocation, maximizing the impact of healthcare systems and serving the needs of the population. The findings can be applied by healthcare providers and administrators to improve care delivery quality and efficiency. The study contributes to academic knowledge by providing empirical evidence on the efficiency of MNCH service networks, allowing researchers to explore efficiency drivers and test alternative methodologies. The study contributes to ongoing efforts to optimize healthcare systems and improve outcomes by providing actionable insights that can inform policy, practice, and future research in this critical area of public health.</p> <p>Despite the theoretical, methodological, and managerial contributions of our research, it has several limitations, both theoretical and methodological, particularly in the choice of variables and the study's approach.</p> <hd id="AN0183370754-20">Limitations and Challenges</hd> <p></p> <hd id="AN0183370754-21">Theoretical Limitations</hd> <p>Practically, we could not comprehensively account for contingency factors influencing the content and utility of DEA methods. These factors include psychological aspects of managers', physicians', nurses', and patients' personalities, as well as decision-making styles and cultural influences within healthcare institutions. Unfortunately, our research model did not incorporate these elements. Additionally, our approach did not consider inter-factor relationships, such as those within the "diversity of indicator use" variable. An inter-factorial study could enhance our model by analyzing how financial and non-financial indicators predict and anticipate healthcare system performance.</p> <hd id="AN0183370754-22">Variables Selection limitations</hd> <p>Our choice of input and output variables did not fully cover the scope of healthcare provision in Morocco. Consequently, studying the performance of the MNCSN healthcare system proved challenging, requiring technical indicators unavailable to healthcare facilities.</p> <hd id="AN0183370754-23">Methodological limitations</hd> <p>A notable limitation is the sample size, consisting of 76 MNCS Networks from each health directorate. While sizable for a management sciences researcher, statisticians may find this sample relatively limited, potentially restricting result generalizability. Conducting a similar study with a larger sample would address this limitation. Moreover, our data collection method relied on older data due to the lack of recent technical updates. Collecting only essential perceptual data carries risks of discrepancies between reality and perception, despite efforts to mitigate perceptual biases through careful documentation and validation interviews.</p> <p>Furthermore, our study identified inefficiencies in MNCSN healthcare activities without methodologically explaining the underlying causes. Understanding these inefficiencies is challenging due to a lack of information on MNCS Network operational intricacies. Thus, each approach has its strengths and weaknesses, which should be considered when interpreting and applying our findings.</p> <hd id="AN0183370754-24">Further Research</hd> <p>This study identifies several areas for further research to improve the efficiency of maternal, newborn, and child healthcare (MNCH) services. It suggests expanding the range of variables to include healthcare infrastructure, service delivery models, patient demographics, and socio-economic factors. Researchers could also use alternative methodologies like non-radial (such as range-adjusted measure (RAM) and slack-based measured (SBM)), Stochastic Frontier Analysis (SFA), Bootstrapping, Decomposing Productivity Index, Tornqvist and Fisher TFP index, Hicks-Moorsteen and Färe-Primont Indexes, Paasche, Laspeyres, Fisher and Lowe Indexes, Mix Efficiency, Allocative Efficiency, Shadow Prices, Technical Change Optionsto assess efficiency and identify determinants of inefficiency within MNCH service networks. Longitudinal studies can capture trends and patterns in MNCH service efficiency, allowing for a more nuanced analysis of factors influencing efficiency over time. Qualitative research methods like interviews, focus groups, and case studies can provide deeper insights into contextual factors shaping MNCH service efficiency. Comparative analyses across different regions or countries can offer valuable insights into variations in MNCH service efficiency and the effectiveness of different healthcare delivery models. Evaluating management practices like New Public Management (NPM) and Citizen Relationship Management (CiRM) in MNCH service networks can help inform policy decisions and resource allocation. Technological innovations, such as telemedicine, digital health platforms, and health information systems, can optimize MNCH service efficiency by streamlining healthcare processes, enhancing communication between healthcare providers and patients, and improving access to quality MNCH services. By pursuing these research directions, scholars can advance our understanding of MNCH service efficiency and contribute to the development of evidence-based strategies to improve maternal, newborn, and child health outcomes in Morocco and beyond.</p> <hd id="AN0183370754-25">Supplemental Material</hd> <p>Graph: Supplemental Material for Evaluating the Effectiveness of Maternal, Neonatal, and Child Healthcare in Moroccan Hospitals and SDG 3: Using Two-Stage DEA and Tobit Regression by Youssef Er-Rays and Meriem M'dioud in Evaluation Review.</p> <hd id="AN0183370754-26">Acknowledgments</hd> <p>We are immensely grateful to the Health Ministry Moroccans for their cooperation in collecting the data and making available the Annual Health Hospital Activity Reports 2017, 2018, 2019, and 2020. We are also thankful to Ibn Tofail University for facilitating the coordination of the data collection for the study.</p> <hd id="AN0183370754-27">ORCID iDs</hd> <p>Youssef Er-Rays https://orcid.org/0000-0002-6691-9226</p> <p>Meriem M'dioud https://orcid.org/0000-0002-7855-3495</p> <hd id="AN0183370754-28">Data Availability Statement</hd> <p>Data from the Health Ministry Moroccan 2012, 2013, 2014, 2015, 2016, 2017, 2018, 2019 and 2020.</p> <hd id="AN0183370754-29">Appendix</hd> <p></p> <hd id="AN0183370754-30">Abbreviations</hd> <p></p> <p>• CRS</p> <p></p> <ulist> <item> Constant Returns Scale</item> <p></p> </ulist> <p>• DEA</p> <p></p> <ulist> <item> Data Envelopment Analysis</item> <p></p> </ulist> <p>• DEAP</p> <p></p> <ulist> <item> Data Envelopment Analysis Programming</item> <p></p> </ulist> <p>• DMU</p> <p></p> <ulist> <item> Decision-Making Unit</item> <p></p> </ulist> <p>• IM</p> <p></p> <ulist> <item> Index Malmquist</item> <p></p> </ulist> <p>• Effch</p> <p></p> <ulist> <item> Technical Efficiency Change</item> <p></p> </ulist> <p>• ET</p> <p></p> <ulist> <item> Technical Efficiency</item> <p></p> </ulist> <p>• pech</p> <p></p> <ulist> <item> Pure Change</item> <p></p> </ulist> <p>• MNCH</p> <p></p> <ulist> <item> maternal, newborn and child Hospital</item> <p></p> </ulist> <p>• SE</p> <p></p> <ulist> <item> Scale Efficiency</item> <p></p> </ulist> <p>• sech</p> <p></p> <ulist> <item> Scale Change</item> <p></p> </ulist> <p>• Techch</p> <p></p> <ulist> <item> Technology Change</item> <p></p> </ulist> <p>• TFP</p> <p></p> <ulist> <item> Total Factor Production</item> <p></p> </ulist> <p>• tfpch</p> <p></p> <ulist> <item> Total Factor Productivity</item> <p></p> </ulist> <p>• VRS</p> <p></p> <ulist> <item> Variable Returns Scale</item> <p></p> </ulist> <p>• MNCSN</p> <p></p> <ulist> <item> maternal, newborn and child services network</item> <p></p> </ulist> <p>• VRSI</p> <p></p> <ulist> <item> Variable Returns Scale with input orientation</item> <p></p> </ulist> <p>• VRSO</p> <p></p> <ulist> <item> Variable Returns Scale with output orientation</item> <p></p> </ulist> <p>• MNCS</p> <p></p> <ulist> <item> maternal, newborn and child services</item> </ulist> <ref id="AN0183370754-31"> <title> References </title> <blist> <bibl id="bib1" idref="ref100" type="bt">1</bibl> <bibtext> Ahmed S., Hasan M. Z., MacLennan M., Dorin F., Ahmed M. W., Hasan M. M., Hasan S. M., Islam M. T., Khan J. A. M. (2019). Measuring the efficiency of health systems in Asia: A data envelopment analysis. BMJ Open, 9(3), Article e022155. https://doi.org/10.1136/bmjopen-2018-022155</bibtext> </blist> <blist> <bibl id="bib2" idref="ref189" type="bt">2</bibl> <bibtext> Alatawi A., Ahmed S., Niessen L., Khan J. (2019). Systematic review and meta-analysis of public hospital efficiency studies in Gulf region and selected countries in similar settings. Cost Effectiveness and Resource Allocation: C/E, 17, 17. https://doi.org/10.1186/s12962-019-0185-4</bibtext> </blist> <blist> <bibl id="bib3" idref="ref146" type="bt">3</bibl> <bibtext> Aloh H. E., Onwujekwe O. E., Ichoku H. E., Osigwe A. C. (2019). Scaling up maternal and child healthcare delivery among mission hospitals in Southeast Nigeria: An Empirical application of data envelopment analysis for setting benchmarks and targets. African Journal of Reproductive Health, 23(3), 57–67. https://doi.org/10.29063/ajrh2019/v23i3.6</bibtext> </blist> <blist> <bibl id="bib4" idref="ref141" type="bt">4</bibl> <bibtext> Amare T., Yitayal M., Amare G. (2020). Technical efficiency of maternal health services provision in public hospitals of northwest Ethiopia: A two-stage data envelopment analysis. Risk Management and Healthcare Policy, 13, 3135–3146. https://doi.org/10.2147/RMHP.S285045</bibtext> </blist> <blist> <bibl id="bib5" idref="ref203" type="bt">5</bibl> <bibtext> Amemiya T. (1984). Tobit models: A survey. Journal of Econometrics, 24(1-2), 3–61. https://doi.org/10.1016/0304-4076(84)90074-5</bibtext> </blist> <blist> <bibl id="bib6" idref="ref73" type="bt">6</bibl> <bibtext> Amirteimoori A., Sahoo B. K., Charles V., Mehdizadeh S. (2022). An introduction to data envelopment analysis. Stochastic benchmarking: Theory and applications. International series in operations research & management science (pp. 13–29). Springer International Publishing. https://doi.org/10.1007/978-3-030-89869-4%5f2 (accessed 4 March 2024).</bibtext> </blist> <blist> <bibl id="bib7" idref="ref81" type="bt">7</bibl> <bibtext> Babalola T. K., Moodley I. (2020). Assessing the efficiency of health-care facilities in Sub-Saharan Africa: A systematic review. Health Services Research and Managerial Epidemiology, 7, 2333392820919604. https://doi.org/10.1177/2333392820919604</bibtext> </blist> <blist> <bibl id="bib8" idref="ref97" type="bt">8</bibl> <bibtext> Banker R. D., Charnes A., Cooper W. W. (1984). Some models for estimating technical and scale inefficiencies in data envelopment analysis. Management Science, 30(9), 1078–1092. https://doi.org/10.1287/mnsc.30.9.1078</bibtext> </blist> <blist> <bibl id="bib9" idref="ref7" type="bt">9</bibl> <bibtext> Benfethllah R. (2020). Social dashboard in Morocco. Ministry of economy, finance, and administrative reform 2020. https://manar.finances.gov.ma/manar/initAccueilInscription (accessed 5 September 2023).</bibtext> </blist> <blist> <bibtext> Cantor V. J. M., Poh K. L. (2017). Integrated analysis of healthcare efficiency: A systematic review. Journal of Medical Systems, 42(1), 8. https://doi.org/10.1007/s10916-017-0848-7</bibtext> </blist> <blist> <bibtext> Caves D. W., Christensen L. R., Diewert W. E. (1982). The economic theory of index numbers and the measurement of input, output, and productivity. Econometrica, 50(6), 1393–1414. https://doi.org/10.2307/1913388</bibtext> </blist> <blist> <bibtext> CESE. (2013). Basic health care: Towards equitable and widespread access: Saisine n° 4/2013. Economic, Social and Environmental Council. https://<ulink href="http://www.cese.ma/docs/soins-de-sante-de-base-vers-un-acces-equitable-et-generalise/">www.cese.ma/docs/soins-de-sante-de-base-vers-un-acces-equitable-et-generalise/</ulink> (accessed 25 February 2024).</bibtext> </blist> <blist> <bibtext> Charnes A., Cooper W. W., Rhodes E. (1978). Measuring the efficiency of decision making units. European Journal of Operational Research, 2(6), 429–444. https://doi.org/10.1016/0377-2217(78)90138-8</bibtext> </blist> <blist> <bibtext> Chen S., Yue W., Liu N., Han X., Yang M. (2022). The progression on the measurement instruments of maternal health literacy: A scoping review. Midwifery, 109, 103308. https://doi.org/10.1016/j.midw.2022.103308</bibtext> </blist> <blist> <bibtext> Christophe C. J.-P. (2017). Morocco 2040: Emerging by investing in intangible capital. Text/HTML. https://documents.worldbank.org/en/publication/documents-reports/documentdetail/227341508753592304/Morocco-2040-emerging-by-investing-in-intangible-capital (accessed 25 February 2024).</bibtext> </blist> <blist> <bibtext> Coelli T. (1996). A guide to DEAP version 2.1: A data envelopment (computer) program. Centre for Efficiency and Productivity Analysis University of New England. Epub ahead of print 1996.</bibtext> </blist> <blist> <bibtext> Coelli T. J., Prasada Rao D. S., O'Donnell C. J. (Eds.), (2005). Data envelopment analysis. An introduction to efficiency and productivity analysis (pp. 161–181). Springer US. https://doi.org/10.1007/0-387-25895-7%5f6 (accessed 8 October 2023).</bibtext> </blist> <blist> <bibtext> Cooper W. W., Seiford L. M., Tone K. (2000). Data envelopment analysis: A comprehensive text with models, applications, references and DEA-solver software. Springer US. https://doi.org/10.1007/b109347 (accessed 28 February 2024).</bibtext> </blist> <blist> <bibtext> Cooper W. W., Seiford L. M., Zhu J. (Eds.), (2011). Handbook on data envelopment analysis. International series in operations research & management science. Springer US. https://doi.org/10.1007/978-1-4419-6151-8 (accessed 28 February 2024).</bibtext> </blist> <blist> <bibtext> Dhaoui I. (2019). Healthcare system efficiency and its determinants: A two-stage data envelopment analysis (DEA) from MENA countries. https://<ulink href="http://www.semanticscholar.org/paper/Healthcare-system-efficiency-and-its-determinants%3A-Dhaoui/c02ac77ecf3ec6ffe9bccb98c388a19dd8ab1e57">www.semanticscholar.org/paper/Healthcare-system-efficiency-and-its-determinants%3A-Dhaoui/c02ac77ecf3ec6ffe9bccb98c388a19dd8ab1e57</ulink> (accessed 22 November 2023).</bibtext> </blist> <blist> <bibtext> El Husseiny I. A. (2022). The efficiency of healthcare systems in the Arab countries: A two-stage data envelopment analysis approach. Journal of Humanities and Applied Social Sciences, 5(4), 339–358. https://doi.org/10.1108/jhass-10-2021-0168</bibtext> </blist> <blist> <bibtext> Er-Rays Y., Ait-Lemqaddem H. (2021a). La performance de Système de Santé Marocain et COVID-19 entre le système bismarckien et le système beveridgien. International Journal of Accounting, Finance, Auditing, Management and Economics, 4(1), 17. from https://ijarimss.org/Publishing/volume4-issue1-ER-RAYS-YOUSSEF.pdf</bibtext> </blist> <blist> <bibtext> Er-Rays Y., Ait-Lemqeddem H. (2020a). Hospital performance in Morocco and covid-19: application of data envelopment analysis and the Malmquist index (La performance hospitalière au Maroc et COVID-19: Application d'Analyse d'Enveloppement des Données et l'indice de Malmquist). International Journal of Accounting, Finance, Auditing, Management and Economics, 1(2), 334–352.</bibtext> </blist> <blist> <bibtext> Er Rays Y., Ait Lemqeddem H. (2020c). La performance des établissements des soins de santé de bases au Maroc et COVID-19: Application d'Analyse d'Enveloppement des Données et l'indice de Malmquist. Epub ahead of print 17 October 2020. https://doi.org/10.5281/zenodo.4097235</bibtext> </blist> <blist> <bibtext> Er-Rays Y., Ait-Lemqeddem H. (2021) (Submitted for publication). Data envelopment analysis and Malmquist index application: Efficiency of primary health care in Morocco and covid-19. Turkish Journal of Computer and Mathematics Education (TURCOMAT), 12(5), 971–983. https://scholar.google.com/citations?view_op=view_citation&hl=en&user=lpAh4UYAAAAJ&citation_for_view=lpAh4UYAAAAJ:9yKSN-GCB0IC.</bibtext> </blist> <blist> <bibtext> Er-Rays Y., Ait-Lemqeddem H. (2021b). Concept de la performance et la crise Covid-19: quelle ambiguïté ? Epub ahead of print 20 October 2021. https://doi.org/10.5281/zenodo.5587156</bibtext> </blist> <blist> <bibtext> Er-Rays Y., Ait-Lemqeddem H., Ezzahiri M. (2022). La posture épistémologique en science de gestion: quelle revue de littérature? Epub ahead of print 13 February 2022. https://doi.org/10.5281/zenodo.6060878</bibtext> </blist> <blist> <bibtext> Er Rays Y., Lequaddem H. A., Ezzahiri M. (2024). Bibliometric analysis of global research trends on digital occupational health using Scopus database. In Ezziyyani M., Kacprzyk J., Balas V. E. (Eds.), International conference on Advanced Intelligent Systems for Sustainable Development (AI2SD'2023) (pp. 1–12). Springer Nature Switzerland. https://doi.org/10.1007/978-3-031-52388-5_1</bibtext> </blist> <blist> <bibtext> Er-Rays Y., M'Dioud M. (2024). Assessment of technical efficiency in the Moroccan public hospital network: Using the DEA method. medRxiv. https://<ulink href="http://www.medrxiv.org/content/10.1101/2024.02.22.24303214v1">www.medrxiv.org/content/10.1101/2024.02.22.24303214v1</ulink> (accessed 22 April 2024).</bibtext> </blist> <blist> <bibtext> Er-Rays Y., M'dioud M. (2024a). Analyzing the efficiency of Moroccan hospital network regions via DEA and Tobit regression: Assessing DEAP 2.1 software versus generative AI ChatGPT 3.5. https://<ulink href="http://www.researchsquare.com/article/rs-4369365/v1">www.researchsquare.com/article/rs-4369365/v1</ulink> (accessed 29 May 2024).</bibtext> </blist> <blist> <bibtext> Er-Rays Y., M'dioud M. (2024b). Assessment of technical efficiency in the Moroccan public hospital network: Using the DEA method. arXiv:2402.14940. https://arxiv.org/abs/2402.14940 (accessed 1 March 2024).</bibtext> </blist> <blist> <bibtext> Er-Rays Y., M'dioud M. (2024c). ChatGPT in healthcare facilities: An overview and innovations in technical efficiency analysis (p. 4771070). SSRN Scholarly Paper. https://papers.ssrn.com/abstract=4771070 (accessed 23 April 2024).</bibtext> </blist> <blist> <bibtext> Er-Rays Y., M'dioud M. (2024d). Evaluating the financial factors influencing maternal, newborn, and child health in Africa. arXiv:2402.14939. https://arxiv.org/abs/2402.14939 (accessed 22 April 2024).</bibtext> </blist> <blist> <bibtext> Er-Rays Y., M'dioud M., Ait-Lemqaddem H. A. (2024d). Mapping global research trends in digital occupational health: A bibliometric analysis utilizing the Scopus database. medRxiv. https://<ulink href="http://www.medrxiv.org/content/10.1101/2024.04.18.24306040v1">www.medrxiv.org/content/10.1101/2024.04.18.24306040v1</ulink> (accessed 22 April 2024).</bibtext> </blist> <blist> <bibtext> Er-Rays Y., M'dioud M., Ait-Lemqedde H., Ezzahir M. (2024c). Data envelopment analysis and Malmquist index application: Efficiency of hospitals networks in Morocco. In Ezziyyani M., Kacprzyk J., Balas V. E. (Eds.), International Conference on advanced Intelligent Systems for Sustainable Development (AI2SD'2023) (pp. 13–24): Springer Nature Switzerland. https://doi.org/10.1007/978-3-031-52388-5_2</bibtext> </blist> <blist> <bibtext> Er-Rays Y., M'dioud M., Ait-Lemqeddem H., Ezzahiri M. (2024a). Assessing efficiency maternal and child health services in Morocco: Data envelopement analysis and Tobit model. Quality and Quantity. Epub ahead of print 2024. https://doi.org/10.1007/s11135-024-01893-y</bibtext> </blist> <blist> <bibtext> Er Rays Y., Ait Lemqeddem H., Ezzahiri M. (2022b) La Transformation Numérique au Maroc à l'ère des variantes de Covid-19: quelle approche? Epub ahead of print 13 February 2022. https://doi.org/10.5281/zenodo.6060806</bibtext> </blist> <blist> <bibtext> Er Rays Y., Ait Lemqeddem H., Ezzahiri M. (2022c). Le New Management Public à l'ère de Covid 19 et ses variantes: quelle performance au Maroc? Epub ahead of print 13 February 2022. https://doi.org/10.5281/zenodo.6060764</bibtext> </blist> <blist> <bibtext> Europe WRO for, Policies EO on HS Smith P. C., Mossialos E., Papanicolas I. (2008). Performance measurement for health system improvement: Experiences, challenges and prospects: Background document 2. WHO/EURO:2008-4077-43836-61716. World Health Organization. Regional Office for Europe. Epub ahead of print 2008.</bibtext> </blist> <blist> <bibtext> European Commission. (2008). Directorate-general for economic and financial Affairs, Dierx A and Ilzkovitz F. The effectiveness and efficiency of public spending. Publications Office. https://data.europa.eu/doi/10.2765/25272 (accessed 12 March 2024).</bibtext> </blist> <blist> <bibtext> Färe R., Grosskopf S., Lindgren B., Roos P. (1994). Productivity developments in Swedish hospitals: A Malmquist output index approach. In Charnes A., Cooper W. W., Lewin A. Y. (Eds.), Data envelopment analysis: Theory, methodology, and applications (pp. 253–272). Springer Netherlands. https://doi.org/10.1007/978-94-011-0637-5%5f13 (accessed 22 November 2023).</bibtext> </blist> <blist> <bibtext> Färe R., Grosskopf S., Norris M. (1997). Productivity growth, technical progress, and efficiency change in industrialized countries: Reply. The American Economic Review, 87(5), 1040–1044.</bibtext> </blist> <blist> <bibtext> Farrell M. J. (1957). The measurement of productive efficiency. Journal of the Royal Statistical Society: Series A, 120(3), 253–281. https://doi.org/10.2307/2343100</bibtext> </blist> <blist> <bibtext> Field K., Emrouznejad A. (2003). Measuring the performance of neonatal care units in Scotland. Journal of Medical Systems, 27(4), 315–324. https://doi.org/10.1023/a:1023749102724</bibtext> </blist> <blist> <bibtext> Goal 3, Department of Economic and Social Affairs (nd). https://sdgs.un.org/goals/goal3 (accessed 10 March 2024).</bibtext> </blist> <blist> <bibtext> Halsteinli V., Kittelsen S. A., Magnussen J. (2010). Productivity growth in outpatient child and adolescent mental health services: The impact of case-mix adjustment. Social Science & Medicine, 70(3), 439–446. https://doi.org/10.1016/j.socscimed.2009.11.002</bibtext> </blist> <blist> <bibtext> Hamidi S., Akinci F. (2016). Measuring efficiency of health systems of the Middle East and north Africa (MENA) region using stochastic frontier analysis. Applied Health Economics and Health Policy, 14(3), 337–347. https://doi.org/10.1007/s40258-016-0230-9</bibtext> </blist> <blist> <bibtext> HCP. (2018). High Commission for Planning of the Kingdom of Morocco (Haut-Commissariat au Plan du Royaume du Maroc). https://<ulink href="http://www.hcp.ma">www.hcp.ma</ulink> (accessed 22 November 2023).</bibtext> </blist> <blist> <bibtext> HCP. (2023). Population and demographics, Institutional site of the High Commission for Planning of the Kingdom of Morocco (Population & démographie, Site institutionnel du Haut-Commissariat au Plan du Royaume du Maroc). https://<ulink href="http://www.hcp.ma/Population-demographie%5fr513.html">www.hcp.ma/Population-demographie%5fr513.html</ulink> (accessed 22 November 2023).</bibtext> </blist> <blist> <bibtext> Health M. (2020). Report: Health statistics 2020, 2019, 2018, 2017, 2016, 2015, 2014, 2013 and 2012. Ministry of Health. https://<ulink href="http://www.sante.gov.ma/Publications/Etudes%5fenquete/Pages/default.aspx">www.sante.gov.ma/Publications/Etudes%5fenquete/Pages/default.aspx</ulink></bibtext> </blist> <blist> <bibtext> High Commission for Planning of Morocco, National Documentation Center. (2023). Statistics & studies and data base of Morroco. https://<ulink href="http://www.hcp.ma/">www.hcp.ma/</ulink> (accessed 24 September 2023).</bibtext> </blist> <blist> <bibtext> Hollingsworth B. (2003). Non-parametric and parametric applications measuring efficiency in health care. Health Care Management Science, 6(4), 203–218. https://doi.org/10.1023/a:1026255523228</bibtext> </blist> <blist> <bibtext> Hollingsworth B., Parkin D. (2001). The efficiency of the delivery of neonatal care in the UK. Journal of Public Health Medicine, 23(1), 47–50. https://doi.org/10.1093/pubmed/23.1.47</bibtext> </blist> <blist> <bibtext> Hribernik M., Kierzenkowski R. (2013). Assessing the efficiency of welfare spending in Slovenia with data envelopment analysis. OECD economics department working papers. OECD economics department working papers 1058. OECD Publishing. Epub ahead of print 14 June 2013.</bibtext> </blist> <blist> <bibtext> Husseiny I. A. E. (2021). The efficiency of healthcare spending in lower-middle-income countries: An empirical investigation using a two-stage data envelopment analysis approach. International Journal of Healthcare Technology and Management, 18(3/4), 250–274. https://doi.org/10.1504/ijhtm.2021.119171</bibtext> </blist> <blist> <bibtext> Ilgun G., Sönmez S., Konca M., Yetim B. (2022). Measuring the efficiency of Turkish maternal and child health hospitals: A two-stage data envelopment analysis. Evaluation and Program Planning, 91, 102023. https://doi.org/10.1016/j.evalprogplan.2021.102023</bibtext> </blist> <blist> <bibtext> Jiang D., Liu X., Chen Y., Hao J., Huang H., Huang Q., Chen Q., Wang Q., Li H. (2022). The technical efficiency of maternal and child health hospitals in China: A case study of Hubei province. Reproductive Health, 19(1), 85. https://doi.org/10.1186/s12978-022-01386-x</bibtext> </blist> <blist> <bibtext> Kirigia J. M. (2001). Kenya. East African Medical Journal, 78(3 SUPPL).</bibtext> </blist> <blist> <bibtext> Kirigia J. M., Sambo L. G., Scheel H. (2001). Technical efficiency of public clinics in Kwazulu-Natal Province of South Africa. East African Medical Journal, 78(3 Suppl), S1–S13. https://doi.org/10.4314/eamj.v78i3.9070</bibtext> </blist> <blist> <bibtext> Knox Lovell C., Pastor J. T., Turner J. A. (1995). Measuring macroeconomic performance in the OECD: A comparison of European and non-European countries. European Journal of Operational Research, 87(3), 507–518. https://doi.org/10.1016/0377-2217(95)00226-x</bibtext> </blist> <blist> <bibtext> Kohl S., Schoenfelder J., Fügener A., Brunner J. O. (2019). The use of Data Envelopment Analysis (DEA) in healthcare with a focus on hospitals. Health Care Management Science, 22(2), 245–286. https://doi.org/10.1007/s10729-018-9436-8</bibtext> </blist> <blist> <bibtext> Lamesgen A., Miniyihun A., Amare T. (2022). Evaluating the technical efficiency of neonatal health service among primary hospitals of northwest Ethiopia: Using two-stage data envelopment analysis and Tobit regression model. PLoS One, 17(11), Article e0277826. https://doi.org/10.1371/journal.pone.0277826</bibtext> </blist> <blist> <bibtext> Lefèvre M., Bouckaert N., Camberlin C., Van de Voorde C. (2022). Economies of scale and optimal size of maternity services in Belgium: A data envelopment analysis. The International Journal of Health Planning and Management, 37(3), 1421–1438. https://doi.org/10.1002/hpm.3408</bibtext> </blist> <blist> <bibtext> Liang X., Li J., Guo G., Li S., Gong Q. (2021). Evaluation for water resource system efficiency and influencing factors in western China: A two-stage network DEA-tobit model. Journal of Cleaner Production, 328, 129674. https://doi.org/10.1016/j.jclepro.2021.129674</bibtext> </blist> <blist> <bibtext> Lu W., Evans R. D., Zhang T., Ni Z., Tao H. (2020). Evaluation of resource utilization efficiency in obstetrics and gynecology units in China: A three-stage data envelopment analysis of the Shanxi province. The International Journal of Health Planning and Management, 35(1), 309–317. https://doi.org/10.1002/hpm.2908</bibtext> </blist> <blist> <bibtext> Malmquist S. (1953). Index numbers and indifference surfaces. Trabajos de Estadística, 4(2), 209–242. https://doi.org/10.1007/bf03006863</bibtext> </blist> <blist> <bibtext> Martín J. C., Indelicato A. (2022). A DEA MCDM approach applied to ESS8 dataset for measuring immigration and refugees citizens' openness. Journal of International Migration and Integration, 23(4), 1941–1961. https://doi.org/10.1007/s12134-021-00920-3</bibtext> </blist> <blist> <bibtext> Mediterranean WHORO for the E. (2020). Health and well-being profile of the eastern mediterranean region: An overview of the health situation in the region and its countries in 2019. World Health Organization. Regional Office for the Eastern Mediterranean. https://iris.who.int/handle/10665/348133 (accessed 22 November 2023).</bibtext> </blist> <blist> <bibtext> Miao C., Duan M., Sun X., Wu X. y. (2020). Safety management efficiency of China's coal enterprises and its influencing factors—based on the DEA-Tobit two-stage model. Process Safety and Environmental Protection, 140, 79–85. https://doi.org/10.1016/j.psep.2020.04.020</bibtext> </blist> <blist> <bibtext> Ministry H. (2023). Health card from the ministry of health in Morocco (Carte Sanitaire du ministère de la Santé au Maroc). https://<ulink href="http://www.sante.gov.ma/EtsSante/cartesanitaire/Pages/default.aspx">www.sante.gov.ma/EtsSante/cartesanitaire/Pages/default.aspx</ulink> (accessed 22 November 2023).</bibtext> </blist> <blist> <bibtext> Ministry of Health. (2012). Action plan 2012-2016. https://r.search.yahoo.com/_ylt=AwrkPUtpF9Vl5mcBoVEk24lQ;_ylu=Y29sbwNpcjIEcG9zAzEEdnRpZAMEc2VjA3Ny/RV=2/RE=1708492778/RO=10/RU=https%3a%2f%2fwww.sante.gov.ma%2fDocs%2fDocuments%2fsecteur%2520sant%25C3%25A9.pdf/RK=2/RS=AbwKgNFPY8yfqKg.rzub3KPSlU8-</bibtext> </blist> <blist> <bibtext> Ministry of Health. (2019). Health plan 2025. Moroccan Ministry of Health. https://r.search.yahoo.com/_ylt=AwrkMddRGtVlSJ4A25Mk24lQ;_ylu=Y29sbwNpcjIEcG9zAzEEdnRpZAMEc2VjA3Ny/RV=2/RE=1708493522/RO=10/RU=https%3a%2f%2fplatform.who.int%2fdocs%2fdefault-source%2fmca-documents%2fpolicy-documents%2fplan-strategy%2fMAR-CC-10-01-PLAN-STRATEGY-2015-fra-Health-Plan-2025.pdf/RK=2/RS=wwazX0YAGC_T2k3Yf1LhG5JKah4-</bibtext> </blist> <blist> <bibtext> Ministry of Health and Social Protection. (2022). Healthcare services offer in Morocco. https://cartesanitaire.sante.gov.ma/dashboard/pages2/about%5f2022.html# (accessed 6 September 2023).</bibtext> </blist> <blist> <bibtext> Ministry of Health M de la S. (2018). National health accounts 2018 (National health accounts 2018). Directorate of Planning and Financial Resource (Direction de la Planification et de Resources Financiers)s. https://<ulink href="http://www.sante.gov.ma/Publications/Etudes%5fenquete/Pages/default.aspx">www.sante.gov.ma/Publications/Etudes%5fenquete/Pages/default.aspx</ulink> (accessed 22 November 2023).</bibtext> </blist> <blist> <bibtext> Mirmozaffari M., Yazdani M., Boskabadi A., Ahady Dolatsara H., Kabirifar K., Amiri Golilarz N. (2020). A novel machine learning approach combined with optimization models for eco-efficiency evaluation. Applied Sciences, 10(15), 5210. https://doi.org/10.3390/app10155210</bibtext> </blist> <blist> <bibtext> Noorihekmat S., Rahimi H., Mehrolhassani M. H., Chashmyazdan M., Haghdoost A. A., Ahmadi Tabatabaei S. V., Dehnavieh R. (2020). Frameworks of performance measurement in public health and primary care system: A scoping review and meta-synthesis. International Journal of Preventive Medicine, 11, 165. https://doi.org/10.4103/ijpvm.IJPVM_34_19</bibtext> </blist> <blist> <bibtext> Obure C. D., Jacobs R., Guinness L., Mayhew S., Vassall A. Integra Initiative. (2016). Does integration of HIV and sexual and reproductive health services improve technical efficiency in Kenya and Swaziland? An application of a two-stage semi parametric approach incorporating quality measures. Social Science & Medicine, 151, 147–156. https://doi.org/10.1016/j.socscimed.2016.01.013</bibtext> </blist> <blist> <bibtext> Organization W. H., Yip W., Hafez R. (2015). Improving health system efficiency: Reforms for improving the efficiency of health systems: Lessons from 10 country cases. https://iris.who.int/handle/10665/185989 (accessed 22 November 2023).</bibtext> </blist> <blist> <bibtext> Pelone F., Kringos D. S., Romaniello A., Archibugi M., Salsiri C., Ricciardi W. (2015). Primary care efficiency measurement using data envelopment analysis: A systematic review. Journal of Medical Systems, 39(1), 156. https://doi.org/10.1007/s10916-014-0156-4</bibtext> </blist> <blist> <bibtext> Rajasulochana S., Dash U. (2012). Technical efficiency of comprehensive emergency obstetric and new-born care centres in Tamil Nadu. Journal of Health Management, 14(2), 151–160. https://doi.org/10.1177/097206341201400205</bibtext> </blist> <blist> <bibtext> Rajasulochana S. R., Matranga D. (2018). Assessing congestion in emergency obstetric care in public hospitals in Tamil Nadu. Benchmarking: An International Journal, 25(7), 2427–2440. https://doi.org/10.1108/bij-05-2017-0089</bibtext> </blist> <blist> <bibtext> Ravaghi H., Afshari M., Isfahani P., Mahboub-Ahari A., Bélorgeot V. D. (2023). Hospital efficiency in the eastern Mediterranean region: A systematic review and meta-analysis. Frontiers in Public Health, 11, 1085459. https://doi.org/10.3389/fpubh.2023.1085459</bibtext> </blist> <blist> <bibtext> Ray S. C. (2004). Data envelopment analysis: Theory and techniques for economics and operations research. Cambridge University Press.</bibtext> </blist> <blist> <bibtext> Seddighi H., Nosrati Nejad F., Basakha M. (2020). Health systems efficiency in eastern Mediterranean region: A data envelopment analysis. Cost Effectiveness and Resource Allocation: C/E, 18, 22. https://doi.org/10.1186/s12962-020-00217-9</bibtext> </blist> <blist> <bibtext> Tobin J. (1958). Estimation of relationships for limited dependent variables. Econometrica, 26(1), 24–36. https://doi.org/10.2307/1907382</bibtext> </blist> <blist> <bibtext> Top M., Konca M., Sapaz B. (2020). Technical efficiency of healthcare systems in African countries: An application based on data envelopment analysis. Health Policy and Technology, 9(1), 62–68. https://doi.org/10.1016/j.hlpt.2019.11.010</bibtext> </blist> <blist> <bibtext> UN. (2020). THE 17 GOALS, sustainable development. https://sdgs.un.org/goals (accessed 10 March 2024).</bibtext> </blist> <blist> <bibtext> Unicef- Morocco. (2021). Morocco (MAR) - demographics, health & infant mortality. https://data.unicef.org/country/mar/ (accessed 30 October 2023).</bibtext> </blist> <blist> <bibtext> Unicef- Morocco 1. (2021). Child-related SDG progress assessment for Morocco. https://data.unicef.org/sdgs/country/mar/ (accessed 30 October 2023).</bibtext> </blist> <blist> <bibtext> Vrijens F., Renard F., Walckiers D., Meeus P., Léonard C. (2015). Évaluer la performance du système de santé: l'expérience de la Belgique. Reflets et Perspectives de la Vie Économique, Tome LIII(4), 83–102. https://doi.org/10.3917/rpve.534.0083</bibtext> </blist> <blist> <bibtext> Wang X., Luo H., Qin X., Feng J., Gao H., Feng Q. (2016). Evaluation of performance and impacts of maternal and child health hospital services using data envelopment analysis in Guangxi Zhuang autonomous region, China: A comparison study among poverty and non-poverty county level hospitals. International Journal for Equity in Health, 15(1), 131. https://doi.org/10.1186/s12939-016-0420-y</bibtext> </blist> <blist> <bibtext> WHO. (2000). The world health report, 2000: For a better health system: World Health Assembly. https://iris.who.int/handle/10665/83428 (accessed 22 November 2023).</bibtext> </blist> <blist> <bibtext> WHO. (2018). The thirteenth general program of work (pp. 2019–2023). WHO.</bibtext> </blist> <blist> <bibtext> WHO. (2019). Morocco data, World health organization. https://data.who.int/countries/504 (accessed 22 November 2023).</bibtext> </blist> <blist> <bibtext> WHO. (2023). World health statistics 2023: Monitoring health for the SDGs, sustainable development goals. https://<ulink href="http://www.who.int/publications-detail-redirect/9789240074323">www.who.int/publications-detail-redirect/9789240074323</ulink> (accessed 10 March 2024).</bibtext> </blist> <blist> <bibtext> Winnie Y. (2000). Improving health system efficiency: Reforms for improving the efficiency of health systems: Lessons from 10 country cases. WHO/HIS/HGF/SR/15.1. https://<ulink href="http://www.who.int/publications-detail-redirect/924156198X">www.who.int/publications-detail-redirect/924156198X</ulink> (accessed 22 November 2023).</bibtext> </blist> <blist> <bibtext> World Health Organization. Regional Office for the Eastern Mediterranean. (2019). Health and well-being profile of the Eastern Mediterranean Region: An overview of the health situation in the Region and its countries in 2019. https://<ulink href="http://www.emro.who.int/entity/statistics/statistics.html">www.emro.who.int/entity/statistics/statistics.html</ulink> (accessed 22 November 2023).</bibtext> </blist> <blist> <bibtext> Yitbarek K., Abraham G., Adamu A., Tsega G., Berhane M., Hurlburt S., Mann C., Woldie M. (2019b). Technical efficiency of neonatal health services in primary health care facilities of Southwest Ethiopia: A two-stage data envelopment analysis. Health Economics Review, 9(1), 27. https://doi.org/10.1186/s13561-019-0245-7</bibtext> </blist> <blist> <bibtext> Yitbarek K., Adamu A., Tsega G., Siraneh Y., Erchafo B., Yewhalaw D., Tekle F., Woldie M. (2019a). Technical efficiency of maternal and reproductive health services in public hospitals of Oromia regional state, Ethiopia. Health Services Insights, 12, 1178632919837630. https://doi.org/10.1177/1178632919837630</bibtext> </blist> <blist> <bibtext> Youssef E. R., Hamid A. L. (2020). La performance hospitalière au Maroc et COVID-19 : Application d'Analyse d'Enveloppement des Données et l'indice de Malmquist. International Journal of Accounting, Finance, Auditing, Management and Economics, 1(2), 334–352.</bibtext> </blist> <blist> <bibtext> Youssef E.-R., Meriem M. (2024). Assessment of technical efficiency in the Moroccan public hospital network: Using the DEA method. medRxiv. https://<ulink href="http://www.medrxiv.org/content/10.1101/2024.02.22.24303214v1">www.medrxiv.org/content/10.1101/2024.02.22.24303214v1</ulink> (accessed 24 June 2024).</bibtext> </blist> <blist> <bibtext> Zakowska I., Godycki-Cwirko M. (2020). Data envelopment analysis applications in primary health care: A systematic review. Family Practice, 37(2), 147–153. https://doi.org/10.1093/fampra/cmz057</bibtext> </blist> </ref> <ref id="AN0183370754-32"> <title> Footnotes </title> <blist> <bibtext> The authors were involved in the literature review, data analysis, interpretation of the results, and drafting of the manuscript. The author read and approved the final manuscript.</bibtext> </blist> <blist> <bibtext> The author(s) declared no potential conflicts of interest with respect to the research, authorship, and/or publication of this article.</bibtext> </blist> <blist> <bibtext> The author(s) received no financial support for the research, authorship, and/or publication of this article.</bibtext> </blist> <blist> <bibtext> Supplemental material for this article is available online.</bibtext> </blist> </ref> <aug> <p>By Youssef Er-Rays and Meriem M'dioud</p> <p>Reported by Author; Author</p> </aug> <nolink nlid="nl1" bibid="bib30" firstref="ref1"></nolink> <nolink nlid="nl2" bibid="bib24" firstref="ref2"></nolink> <nolink nlid="nl3" bibid="bib92" firstref="ref3"></nolink> <nolink nlid="nl4" bibid="bib87" firstref="ref4"></nolink> <nolink nlid="nl5" bibid="bib45" firstref="ref5"></nolink> <nolink nlid="nl6" bibid="bib95" firstref="ref6"></nolink> <nolink nlid="nl7" bibid="bib12" firstref="ref8"></nolink> <nolink nlid="nl8" bibid="bib51" firstref="ref9"></nolink> <nolink nlid="nl9" bibid="bib73" firstref="ref10"></nolink> <nolink nlid="nl10" bibid="bib88" firstref="ref11"></nolink> <nolink nlid="nl11" bibid="bib89" firstref="ref12"></nolink> <nolink nlid="nl12" bibid="bib15" firstref="ref13"></nolink> <nolink nlid="nl13" bibid="bib37" firstref="ref14"></nolink> <nolink nlid="nl14" bibid="bib38" firstref="ref15"></nolink> <nolink nlid="nl15" bibid="bib28" firstref="ref16"></nolink> <nolink nlid="nl16" bibid="bib27" firstref="ref18"></nolink> <nolink nlid="nl17" bibid="bib36" firstref="ref19"></nolink> <nolink nlid="nl18" bibid="bib34" firstref="ref20"></nolink> <nolink nlid="nl19" bibid="bib22" firstref="ref21"></nolink> <nolink nlid="nl20" bibid="bib23" firstref="ref22"></nolink> <nolink nlid="nl21" bibid="bib25" firstref="ref23"></nolink> <nolink nlid="nl22" bibid="bib26" firstref="ref24"></nolink> <nolink nlid="nl23" bibid="bib29" firstref="ref26"></nolink> <nolink nlid="nl24" bibid="bib32" firstref="ref27"></nolink> <nolink nlid="nl25" bibid="bib33" firstref="ref28"></nolink> <nolink nlid="nl26" bibid="bib100" firstref="ref29"></nolink> <nolink nlid="nl27" bibid="bib101" firstref="ref30"></nolink> <nolink nlid="nl28" bibid="bib35" firstref="ref31"></nolink> <nolink nlid="nl29" bibid="bib78" firstref="ref34"></nolink> <nolink nlid="nl30" bibid="bib96" firstref="ref35"></nolink> <nolink nlid="nl31" bibid="bib97" firstref="ref36"></nolink> <nolink nlid="nl32" bibid="bib94" firstref="ref37"></nolink> <nolink nlid="nl33" bibid="bib48" firstref="ref38"></nolink> <nolink nlid="nl34" bibid="bib50" firstref="ref39"></nolink> <nolink nlid="nl35" bibid="bib49" firstref="ref48"></nolink> <nolink nlid="nl36" bibid="bib68" firstref="ref50"></nolink> <nolink nlid="nl37" bibid="bib70" firstref="ref53"></nolink> <nolink nlid="nl38" bibid="bib93" firstref="ref66"></nolink> <nolink nlid="nl39" bibid="bib71" firstref="ref67"></nolink> <nolink nlid="nl40" bibid="bib72" firstref="ref68"></nolink> <nolink nlid="nl41" bibid="bib74" firstref="ref69"></nolink> <nolink nlid="nl42" bibid="bib43" firstref="ref71"></nolink> <nolink nlid="nl43" bibid="bib13" firstref="ref72"></nolink> <nolink nlid="nl44" bibid="bib17" firstref="ref74"></nolink> <nolink nlid="nl45" bibid="bib60" firstref="ref75"></nolink> <nolink nlid="nl46" bibid="bib64" firstref="ref76"></nolink> <nolink nlid="nl47" bibid="bib67" firstref="ref77"></nolink> <nolink nlid="nl48" bibid="bib69" firstref="ref78"></nolink> <nolink nlid="nl49" bibid="bib75" firstref="ref79"></nolink> <nolink nlid="nl50" bibid="bib10" firstref="ref82"></nolink> <nolink nlid="nl51" bibid="bib14" firstref="ref83"></nolink> <nolink nlid="nl52" bibid="bib61" firstref="ref84"></nolink> <nolink nlid="nl53" bibid="bib76" firstref="ref85"></nolink> <nolink nlid="nl54" bibid="bib79" firstref="ref86"></nolink> <nolink nlid="nl55" bibid="bib82" firstref="ref87"></nolink> <nolink nlid="nl56" bibid="bib102" firstref="ref88"></nolink> <nolink nlid="nl57" bibid="bib39" firstref="ref90"></nolink> <nolink nlid="nl58" bibid="bib90" firstref="ref91"></nolink> <nolink nlid="nl59" bibid="bib52" firstref="ref94"></nolink> <nolink nlid="nl60" bibid="bib20" firstref="ref101"></nolink> <nolink nlid="nl61" bibid="bib21" firstref="ref102"></nolink> <nolink nlid="nl62" bibid="bib86" firstref="ref103"></nolink> <nolink nlid="nl63" bibid="bib18" firstref="ref106"></nolink> <nolink nlid="nl64" bibid="bib19" firstref="ref107"></nolink> <nolink nlid="nl65" bibid="bib31" firstref="ref109"></nolink> <nolink nlid="nl66" bibid="bib54" firstref="ref110"></nolink> <nolink nlid="nl67" bibid="bib66" firstref="ref128"></nolink> <nolink nlid="nl68" bibid="bib85" firstref="ref129"></nolink> <nolink nlid="nl69" bibid="bib40" firstref="ref130"></nolink> <nolink nlid="nl70" bibid="bib44" firstref="ref132"></nolink> <nolink nlid="nl71" bibid="bib63" firstref="ref133"></nolink> <nolink nlid="nl72" bibid="bib53" firstref="ref134"></nolink> <nolink nlid="nl73" bibid="bib56" firstref="ref135"></nolink> <nolink nlid="nl74" bibid="bib46" firstref="ref136"></nolink> <nolink nlid="nl75" bibid="bib80" firstref="ref137"></nolink> <nolink nlid="nl76" bibid="bib81" firstref="ref138"></nolink> <nolink nlid="nl77" bibid="bib57" firstref="ref139"></nolink> <nolink nlid="nl78" bibid="bib65" firstref="ref140"></nolink> <nolink nlid="nl79" bibid="bib62" firstref="ref142"></nolink> <nolink nlid="nl80" bibid="bib98" firstref="ref143"></nolink> <nolink nlid="nl81" bibid="bib99" firstref="ref144"></nolink> <nolink nlid="nl82" bibid="bib59" firstref="ref145"></nolink> <nolink nlid="nl83" bibid="bib77" firstref="ref157"></nolink> <nolink nlid="nl84" bibid="bib91" firstref="ref166"></nolink> <nolink nlid="nl85" bibid="bib58" firstref="ref169"></nolink> <nolink nlid="nl86" bibid="bib55" firstref="ref184"></nolink> <nolink nlid="nl87" bibid="bib47" firstref="ref188"></nolink> <nolink nlid="nl88" bibid="bib84" firstref="ref190"></nolink> <nolink nlid="nl89" bibid="bib16" firstref="ref192"></nolink> <nolink nlid="nl90" bibid="bib11" firstref="ref196"></nolink> <nolink nlid="nl91" bibid="bib41" firstref="ref197"></nolink> <nolink nlid="nl92" bibid="bib42" firstref="ref199"></nolink> <nolink nlid="nl93" bibid="bib83" firstref="ref201"></nolink>
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  Data: SAGE Publications. 2455 Teller Road, Thousand Oaks, CA 91320. Tel: 800-818-7243; Tel: 805-499-9774; Fax: 800-583-2665; e-mail: journals@sagepub.com; Web site: https://sagepub.com
– Name: PeerReviewed
  Label: Peer Reviewed
  Group: SrcInfo
  Data: Y
– Name: Pages
  Label: Page Count
  Group: Src
  Data: 37
– Name: DatePubCY
  Label: Publication Date
  Group: Date
  Data: 2025
– Name: TypeDocument
  Label: Document Type
  Group: TypDoc
  Data: Journal Articles<br />Reports - Research
– Name: Subject
  Label: Descriptors
  Group: Su
  Data: <searchLink fieldCode="DE" term="%22Foreign+Countries%22">Foreign Countries</searchLink><br /><searchLink fieldCode="DE" term="%22Sustainable+Development%22">Sustainable Development</searchLink><br /><searchLink fieldCode="DE" term="%22Hospitals%22">Hospitals</searchLink><br /><searchLink fieldCode="DE" term="%22Mothers%22">Mothers</searchLink><br /><searchLink fieldCode="DE" term="%22Neonates%22">Neonates</searchLink><br /><searchLink fieldCode="DE" term="%22Perinatal+Influences%22">Perinatal Influences</searchLink><br /><searchLink fieldCode="DE" term="%22Child+Health%22">Child Health</searchLink><br /><searchLink fieldCode="DE" term="%22Well+Being%22">Well Being</searchLink><br /><searchLink fieldCode="DE" term="%22Mortality+Rate%22">Mortality Rate</searchLink><br /><searchLink fieldCode="DE" term="%22Resource+Allocation%22">Resource Allocation</searchLink><br /><searchLink fieldCode="DE" term="%22Health+Services%22">Health Services</searchLink><br /><searchLink fieldCode="DE" term="%22Efficiency%22">Efficiency</searchLink><br /><searchLink fieldCode="DE" term="%22Physicians%22">Physicians</searchLink><br /><searchLink fieldCode="DE" term="%22Allied+Health+Personnel%22">Allied Health Personnel</searchLink><br /><searchLink fieldCode="DE" term="%22Data+Collection%22">Data Collection</searchLink><br /><searchLink fieldCode="DE" term="%22Data+Interpretation%22">Data Interpretation</searchLink><br /><searchLink fieldCode="DE" term="%22Medical+Care+Evaluation%22">Medical Care Evaluation</searchLink>
– Name: Subject
  Label: Geographic Terms
  Group: Su
  Data: <searchLink fieldCode="DE" term="%22Morocco%22">Morocco</searchLink>
– Name: DOI
  Label: DOI
  Group: ID
  Data: 10.1177/0193841X241264863
– Name: ISSN
  Label: ISSN
  Group: ISSN
  Data: 0193-841X<br />1552-3926
– Name: Abstract
  Label: Abstract
  Group: Ab
  Data: Maternal, neonatal, and child health play crucial roles in achieving the objectives of Sustainable Development Goal (SDG) 2030, particularly in promoting health and wellbeing. However, maternal, neonatal, and child services in Moroccan public hospitals face challenges, particularly concerning mortality rates and inefficient resource allocation, which hinder optimal outcomes. This study aimed to evaluate the operational effectiveness of 76 neonatal and child health services networks (MNCSN) within Moroccan public hospitals. Using Data Envelopment Analysis (DEA), we assessed technical efficiency (TE) employing both Variable Returns to Scale for inputs (VRS-I) and outputs (VRS-O) orientation. Additionally, the Tobit method (TM) was utilized to explore factors influencing inefficiency, with hospital, doctor, and paramedical staff considered as inputs, and admissions, cesarean interventions, functional capacity, and hospitalization days as outputs. Our findings revealed that VRS-I exhibited a higher average TE score of 0.76 compared to VRS-O (0.23). Notably, the Casablanca-Anfa MNCSN received the highest referrals (30) under VRS-I, followed by the Khemisset MNCSN (24). In contrast, under VRS-O, Ben Msick, Rabat, and Mediouna MNCSN each had three peers, with 71, 22, and 17 references, respectively. Moreover, the average Malmquist Index under VRS-I indicated a 7.7% increase in productivity over the 9-year study period, while under VRS-O, the average Malmquist Index decreased by 8.7%. Furthermore, doctors and functional bed capacity received the highest Tobit model score of 0.01, followed by hospitalization days and cesarean sections. This study underscores the imperative for policymakers to strategically prioritize input factors to enhance efficiency and ensure optimal maternal, neonatal, and child healthcare outcomes.
– Name: AbstractInfo
  Label: Abstractor
  Group: Ab
  Data: As Provided
– Name: DateEntry
  Label: Entry Date
  Group: Date
  Data: 2025
– Name: AN
  Label: Accession Number
  Group: ID
  Data: EJ1466588
PLink https://search.ebscohost.com/login.aspx?direct=true&site=eds-live&db=eric&AN=EJ1466588
RecordInfo BibRecord:
  BibEntity:
    Identifiers:
      – Type: doi
        Value: 10.1177/0193841X241264863
    Languages:
      – Text: English
    PhysicalDescription:
      Pagination:
        PageCount: 37
        StartPage: 343
    Subjects:
      – SubjectFull: Foreign Countries
        Type: general
      – SubjectFull: Sustainable Development
        Type: general
      – SubjectFull: Hospitals
        Type: general
      – SubjectFull: Mothers
        Type: general
      – SubjectFull: Neonates
        Type: general
      – SubjectFull: Perinatal Influences
        Type: general
      – SubjectFull: Child Health
        Type: general
      – SubjectFull: Well Being
        Type: general
      – SubjectFull: Mortality Rate
        Type: general
      – SubjectFull: Resource Allocation
        Type: general
      – SubjectFull: Health Services
        Type: general
      – SubjectFull: Efficiency
        Type: general
      – SubjectFull: Physicians
        Type: general
      – SubjectFull: Allied Health Personnel
        Type: general
      – SubjectFull: Data Collection
        Type: general
      – SubjectFull: Data Interpretation
        Type: general
      – SubjectFull: Medical Care Evaluation
        Type: general
      – SubjectFull: Morocco
        Type: general
    Titles:
      – TitleFull: Evaluating the Effectiveness of Maternal, Neonatal, and Child Healthcare in Moroccan Hospitals and SDG 3: Using Two-Stage Data Envelopment Analysis and Tobit Regression
        Type: main
  BibRelationships:
    HasContributorRelationships:
      – PersonEntity:
          Name:
            NameFull: Youssef Er-Rays
      – PersonEntity:
          Name:
            NameFull: Meriem M'dioud
    IsPartOfRelationships:
      – BibEntity:
          Dates:
            – D: 01
              M: 04
              Type: published
              Y: 2025
          Identifiers:
            – Type: issn-print
              Value: 0193-841X
            – Type: issn-electronic
              Value: 1552-3926
          Numbering:
            – Type: volume
              Value: 49
            – Type: issue
              Value: 2
          Titles:
            – TitleFull: Evaluation Review
              Type: main
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