Impact of Socio-Economic Factors on Healthcare Expenditures of Vulnerable Elderly People in Morocco

Impact of Socio-Economic Factors on Healthcare Expenditures of Vulnerable Elderly People in Morocco | Research Square window.SnipcartSettings = { analytics: { enabled: false } }; (function() { var accessVector = localStorage.getItem('access_vector') || ''; window.dataLayer = window.dataLayer || []; if (accessVector) { window.dataLayer.push({ user: { profile: { profileInfo: { snid: accessVector } } } }); } })(); (function(w,d,s,l,i){w[l]=w[l]||[];w[l].push({'gtm.start':new Date().getTime(),event:'gtm.js'});var f=d.getElementsByTagName(s)[0],j=d.createElement(s),dl=l!='dataLayer'?'&l='+l:'';j.async=true;j.src='https://www.googletagmanager.com/gtm.js?id='+i+dl;f.parentNode.insertBefore(j,f);})(window,document,'script','dataLayer','GTM-K279D39R'); Browse Preprints In Review Journals COVID-19 Preprints AJE Video Bytes Research Tools Research Promotion AJE Professional Editing AJE Rubriq About Preprint Platform In Review Editorial Policies Our Team Advisory Board Help Center Sign In Submit a Preprint Cite Share Download PDF Research Article Impact of Socio-Economic Factors on Healthcare Expenditures of Vulnerable Elderly People in Morocco SAID LOUCIFI, Abdelkader Salmi, Mohammed Saber Hassainate This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-5767434/v1 This work is licensed under a CC BY 4.0 License Status: Published Journal Publication published 11 May, 2025 Read the published version in Archives of Gerontology and Geriatrics Plus → Version 1 posted You are reading this latest preprint version Abstract In 1960, Morocco had 835,000 people aged 60 and over. This number reached 4.9 million in 2023 and is projected to rise to 10.1 million by 2050. The National Household Consumption and Expenditure Survey of 2013/2014 highlighted a notable disparity between male (135,387) and female (31,792) household heads. This article examines the impact of socio-economic factors on the annual health expenditures of vulnerable elderly individuals in Morocco, as well as their standard of living. Data used in this study come from the HCP-ENCDM survey (2014). We employed analysis of variance (ANOVA), t-tests, and multiple comparisons (Games-Howell) to identify variables influencing the increase in health expenditures among the elderly. The results indicate that health expenditures vary significantly based on regions, residence settings (urban/rural), household size, and the profession of the household head, while no significant difference was observed concerning gender. These findings underscore the importance of public health policies tailored to regional and socio-economic needs to reduce health inequalities. elderly vulnerable individuals health expenditures ANOVA t-tests Introduction In Morocco, the situation of the elderly has become a major concern, particularly with the evolution of social protection. The current age pyramid reveals a dominance of youth and adults (15-59 years), but the proportion of those aged 60 and over is constantly increasing, rising from 12.2% in 2022 to a projected 23.2% in 2050 (HCP, 2023). This trend is explained by a rising life expectancy—78 years for men and 82 years for women—and the overall improvement in healthcare services. The general census of population and housing, conducted every ten years by the High Commission for Planning (HCP), allows for monitoring the evolution of different age groups. These data are essential to raise public awareness about adopting social policies in favor of seniors. According to the National Household Consumption and Expenditure Survey of 2013/2014, there were 135,387 male household heads compared to 31,792 female heads, highlighting a notable gender disparity. The central question of our research is to identify the socio-economic factors that influence the annual health expenditures of vulnerable elderly household heads in Morocco. The concept of "elderly" is complex, encompassing not only chronological age but also vulnerability linked to various socio-economic and health factors. The traditional definition based on retirement age is insufficient, as it does not account for all aspects of vulnerability that seniors may experience. Context and Rationale Population aging is a global phenomenon that presents unique challenges for healthcare systems, especially in developing countries like Morocco. The increasing number of vulnerable elderly individuals requires special attention to understand how socio-economic factors affect their health expenditures. Regional inequalities, differences between urban and rural areas, as well as professional and educational disparities, can exacerbate the vulnerability of this population. For instance, rural regions in Morocco often face limited access to healthcare services, which can increase costs for the elderly who must travel long distances to receive care (Benbrahim et al., 2015). Moreover, elderly individuals with low levels of education may be less informed about available health services, affecting their ability to access necessary care (Chatterji et al., 2008). Objectives of the Study Identify the socio-economic factors influencing the annual health expenditures of vulnerable elderly household heads in Morocco. Assess the impact of variables such as region, residence setting, household size, profession, and education level on these expenditures. Formulate recommendations for adapted public health policies aimed at reducing health inequalities. Significance of the Study This research makes a significant contribution to the existing literature by providing a detailed analysis of health expenditures among vulnerable elderly individuals in Morocco. It highlights regional and socio-economic disparities, offering a solid basis for developing more equitable public policies. Additionally, by employing rigorous statistical methods such as ANOVA and t-tests, the study ensures the reliability and validity of its conclusions. Structure of the Article The article is structured as follows: 1. Literature Review: Presentation of key concepts related to successful aging and existing models, as well as an analysis of previous studies on socio-economic factors affecting health expenditures. 2. Methodology: Description of the data used, the hypotheses formulated, and the statistical methods employed for analysis. 3. Results: Detailed presentation of statistical analyses and interpretation of the obtained results. 4. Discussion: Interpretation of the results in the context of public health policies and implications for reducing health inequalities. 5. Conclusion: Synthesis of the main findings, limitations of the study, and suggestions for future research. In summary, this study aims to provide an in-depth understanding of the factors influencing health expenditures among vulnerable elderly individuals in Morocco, to contribute to more effective and equitable health policies. Theoretical background and hypotheses development In recent years, the concept of "successful aging" has gained prominence in the field of gerontology. More and more people are reaching advanced ages without experiencing the declines once associated with aging. Introduced during the annual meeting of the Gerontological Society of America in 1986, this concept encompasses terms like "aging well" and "active aging." It offers a positive perception of aging, emphasizing the possibility of preventing or delaying problems associated with old age. Successful aging refers to a state of good physical and mental health, as well as active participation in society. Several conceptual models have been developed to explain this phenomenon, integrating biological, psychological, and social dimensions. For example, Rowe and Kahn's (1997) three-dimensional model identifies three interconnected components of successful aging: the absence of disease, a high level of physical and cognitive functioning, and an active life in professional and social domains. Socio-Economic Factors and Health Expenditures Health expenditures among the elderly are influenced by a multitude of socio-economic factors, including income, education level, access to healthcare, and social support. Fuchs (2004) demonstrated that individuals with higher education and income levels tend to spend more on their health, resulting in better health outcomes and longevity. (Bartley, Ferrie, Montgomery, Marmot, & Wilkinson, 2006)emphasized the importance of social determinants of health, indicating that social inequalities significantly contribute to health disparities among the elderly. Specific studies, such as (Chatterji, Alegria, Lu, & Takeuchi , 2008), have shown that elderly individuals with higher education levels and stable income are more likely to invest in preventive care and have easier access to health services. This underscores the importance of education and financial stability in promoting healthy aging. Vulnerability and Health of the Elderly The vulnerability of the elderly is a multidimensional concept encompassing physical health, mental health, and social well-being. It is often exacerbated by socio-economic factors such as poverty, social isolation, and limited access to healthcare services. (Guralnik, 1996) showed that elderly individuals living in socio-economically disadvantaged conditions are more likely to suffer from chronic diseases and functional limitations. Additionally, gender plays an important role in the vulnerability of the elderly, with women often more likely to live in poverty and suffer from chronic illnesses (Arber & Ginn, 1991). Challenges in Developing Countries In developing countries, challenges are often more complex due to limited resources and inadequate health infrastructures. Nair et al. (2013) showed that in these countries, the elderly spend a significant portion of their income on healthcare, which can lead to considerable financial difficulties. It is therefore essential that public health programs make healthcare more affordable and accessible. The Case of Morocco In Morocco, health expenditures among vulnerable elderly household heads vary considerably across regions. Benbrahim et al. (2015) revealed that urban regions generally benefit from better access to healthcare than rural regions, leading to significant disparities in health expenditures. These regional differences can be attributed to the availability of health services, the quality of medical infrastructures, and regional health policies. Synthesis of the Literature The existing literature underscores the importance of understanding regional and socio-economic disparities in health expenditures among the elderly. Public health policies must be adapted to meet the specific needs of regions and socio-economic groups to reduce health inequalities. This study aims to fill gaps by providing a detailed analysis of socio-economic factors influencing health expenditures among vulnerable elderly individuals in Morocco. Methodology Research Question What are the socio-economic factors that influence the annual health expenditures of vulnerable elderly household heads in Morocco? Research Hypotheses To answer this question, we formulated the following hypotheses, based on existing literature and the Moroccan socio-economic context: Hypothesis H1: Annual health expenditures of vulnerable households differ by region. As a consequence, we hypothesize that regional disparities play a crucial role in access to health services and associated expenditures. Previous studies have shown that health infrastructure, availability of medical personnel, and regional health policies vary considerably from one region to another (Benbrahim et al., 2015). For example, a study in Tanzania found that rural households had relatively lower health expenditures but faced disproportionate costs due to limited access to quality health services, compared to urban households that have better access but higher overall costs (BMC International Health and Human Rights, 2020). Therefore, it is relevant to examine whether such regional disparities exist in Morocco and how they influence health expenditures among vulnerable elderly individuals. Hypothesis H2: Annual health expenditures of vulnerable households differ by residence setting (urban/rural). In light of this, we hypothesize that the residence setting is a determining factor in access to healthcare. Rural areas are often characterized by limited access to health infrastructures, a shortage of health professionals, and greater distances to reach care centers (Nair & Webster, 2013). This can lead to lower expenditures due to restricted access or, conversely, higher expenditures due to additional costs related to transportation and obtaining care (Benbrahim et al., 2015). Understanding the impact of residence setting is essential to identify potential inequalities in health expenditures among vulnerable elderly individuals in Morocco. Hypothesis H3: Annual health expenditures of vulnerable households differ by the gender of the household head. Thus, we posit that the gender of the household head can influence budget priorities and the allocation of resources to healthcare. Households headed by women tend to allocate a larger share of their budget to healthcare, partly due to responsibilities related to caring for children and elderly family members (Globalization and Health, 2021). Moreover, female household heads may face specific socio-economic challenges, such as lower incomes and limited access to resources, which can affect their health expenditures (Arber & Ginn, 1991). It is therefore relevant to examine whether the gender of the household head influences health expenditures within vulnerable households. Hypothesis H4: Annual health expenditures of vulnerable households differ by household size. Accordingly, we propose that household size is an important factor that can affect health expenditures. Studies show that larger households have lower per capita health expenditures due to the dilution effect of fixed costs but higher total costs due to the increased number of people needing care (Xu, Lazar, & Ruger, 2021). This can place additional financial pressure on vulnerable households, making it essential to examine the impact of household size on health expenditures. Hypothesis H5: Annual health expenditures of vulnerable households differ by the profession of the household head. In light of this, we suggest that the profession of the household head is an indicator of socio-economic status and can influence available income, access to health insurance, and exposure to occupational risks. Households whose heads occupy precarious or low-income jobs allocate a larger share of their budget to healthcare due to the absence of social benefits such as health insurance (Fisher & Ryan, 2018). For example, high-risk professions like construction and agriculture are associated with higher health costs due to frequent occupational injuries and illnesses (Work, Aging and Retirement, 2018). It is therefore crucial to explore how the profession of the household head affects health expenditures. Hypothesis H6: Annual health expenditures of vulnerable households differ by the education level of the household head. The education level of the household head plays a determining role in health behaviors. Individuals with higher education levels better understand the importance of preventive care and use health services more effectively, which can influence health expenditures (Stansfeld, Marmot, & Wilkinson, 2006). A study showed that education is associated with increased use of health services and higher costs but also with better overall health (Globalization and Health, 2021). It is therefore pertinent to examine the impact of education level on health expenditures of vulnerable households. Methodological Framework Methods and Measurement Tools This research is based on data from the National Household Consumption and Expenditure Survey (ENCDM) conducted by the High Commission for Planning (HCP) in 2014. Data were collected through standardized questionnaires in Arabic and French, administered individually to household heads at their homes. The questionnaires included detailed information on health expenditures, socio-economic characteristics of the household and household head, and other variables relevant to the study. Sample The study population includes household heads aged 60 and over, identified during the 2013/2014 ENCDM. The national sample includes 7,190,456 households, representing 33,579,281 people. For this study, we focused on vulnerable household heads aged 60 and over, which represents a sample of 122,219 households. Statistical Tests Used To test these hypotheses, we used appropriate statistical tests, conducted using IBM SPSS Statistics 28 software: One-way Analysis of Variance (ANOVA): Used to compare annual health expenditures between multiple groups (more than two), particularly according to the 12 regions, household size, profession of the household head, and education level. ANOVA allows us to determine whether there are significant differences between group means. Independent Samples t-tests: Used to compare annual health expenditures between two independent groups, notably according to residence setting (urban/rural) and the gender of the household head. This test evaluates whether the observed differences between the means of the two groups are statistically significant. We analyzed the relationships between health expenditures (in Moroccan dirhams) of this population and various variables, including: Gender of the Household Head: Male or female. Region: The 12 administrative regions of Morocco, allowing examination of regional disparities. Residence Setting: Urban or rural. Professional Activity: Classification of the household head according to profession, enabling assessment of the impact of profession on health expenditures. Education Level: Educational attainment of the household head, to analyze the influence of education on health expenditures. Household Size: Number of people living in the household, to study the effect of family composition on health expenditures. This approach allows us to thoroughly explore how these socio-economic variables influence the annual health expenditures of vulnerable elderly individuals in Morocco Table 1. Descriptive Statistics of the Study Population Residence Area Frequency Percent Valid Percent Cumulative Percent Urban 51,685 42.3% 42.3% 42.3% Rural 70,534 57.7% 57.7% 100.0% Total 122,219 100.0% 100.0% 100.0% Regions Frequency Percent Valid Percent Cumulative Percent Tanger-Tétouan-Al Hoceïma 6,754 5.5% 5.5% 5.5% Oriental 4,760 3.9% 3.9% 9.4% Fès-Meknès 20,364 16.7% 16.7% 26.1% Rabat-Salé-Kénitra 12,540 10.3% 10.3% 36.3% Béni Mellal-Khénifra 14,526 11.9% 11.9% 48.2% Casablanca-Settat 22,988 18.8% 18.8% 67.0% Marrakech-Safi 15,278 12.5% 12.5% 79.5% Drâa-Tafilalet 6,199 5.1% 5.1% 84.6% Souss-Massa 17,030 13.9% 13.9% 98.5% Guelmim-Oued Noun 1,749 1.4% 1.4% 100.0% Dakhla-Oued Ed Dahab 31 0.0% 0.0% 100.0% Total 122,219 100.0% 100.0% 100.0% Gender of Household Head Frequency Percent Valid Percent Cumulative Percent Male 99,974 81.8% 81.8% 81.8% Female 22,245 18.2% 18.2% 100.0% Total 122,219 100.0% 100.0% 100.0% Marital Status of Household Head Frequency Percent Valid Percent Cumulative Percent Married 99,093 81.1% 81.1% 81.1% Widowed 23,126 18.9% 18.9% 100.0% Total 122,219 100.0% 100.0% 100.0% Sector of Usual Activity During the Last 12 Months Frequency Percent Valid Percent Cumulative Percent Agriculture, Forestry, and Fishing 37,655 30.8% 52.9% 52.9% Industry 5,663 4.6% 8.0% 60.9% Construction and Public Works 6,021 4.9% 8.5% 69.3% Commerce 9,869 8.1% 13.9% 83.2% Services 9,947 8.1% 14.0% 97.2% Not Declared 1,997 1.6% 2.8% 100.0% Total 71,152 58.2% 100.0% 100.0% Missing System 51,067 41.8% Total 122,219 100.0% Descriptive Statistics N Minimum Maximum Mean Std. Deviation Age (in 5-year intervals) of Household Head 122,219 13 16 14.19 1.136 Annual Healthcare Expenditure per Household (in MAD) 122,219 24.00 30,650.00 2,807.6483 3,983.09708 Household Size 122,219 1 6 5.63 0.787 Valid N (listwise) 122,219 Source: Prepared by the authors based on data from the HCP-ENCDM survey (2014). Results This section presents the results of the statistical analyses conducted to test the hypotheses related to healthcare expenditures of vulnerable older adult household heads in Morocco. Each hypothesis is tested using appropriate statistical methods, and the results are interpreted accordingly. H1: The annual healthcare expenditures of vulnerable households differ across the 12 regions of Morocco This section presents the results of the statistical analyses conducted to test the hypotheses related to healthcare expenditures of vulnerable older adult household heads in Morocco. The results include ANOVA tests followed by post hoc tests for multiple comparisons, taking into account the assumption of variance homogeneity. Hypotheses: H0: There is no significant difference in annual healthcare expenditures between regions. H1: There is a significant difference in annual healthcare expenditures between regions. Table 2. Test of Homogeneity of Variances Method Levene Statistic df1 df2 Sig. Based on Mean 1116.633 10 122,208 .000 Based on Median 639.421 10 122,208 .000 Based on Median and with adjusted df 639.421 10 72,026.502 .000 Based on trimmed mean 787.721 10 122,208 .000 Source: Prepared by the authors based on data from the HCP-ENCDM survey (2014). The hypothesis of homogeneity of variances is rejected (p < 0.05). Therefore, we use the Games-Howell post hoc test for multiple comparisons. Statistical Interpretation Analysis of Variance (ANOVA) An analysis of variance (ANOVA) was conducted to examine differences in annual healthcare expenditures across the 12 regions of Morocco. The ANOVA results revealed a significant difference between regions (F(10, 122,208) = 540.973, p < .001). Descriptive statistics showed that average healthcare expenditures varied considerably from one region to another. Table 3. ANOVA Results for Regional Differences in Healthcare Expenditures Source of Variation Sum of Squares df Mean Square F Sig. Between Groups 82,194,312,609.081 10 8,219,431,260.908 540.973 .000 Within Groups 1,856,801,880,619.078 122,208 15,193,783.391 Total 1,938,996,193,228.158 122,218 Source: Prepared by the authors based on data from the HCP-ENCDM survey (2014). These results indicate that annual healthcare expenditures vary significantly across regions in Morocco. Multiple Comparisons: Games-Howell Test To identify the specific regions where healthcare expenditures differ, a Games-Howell post hoc test was conducted. The results show that several regions exhibit significant differences in annual healthcare expenditures. Statistical Interpretation of the Post Hoc Test (Games-Howell) To analyze specific differences between regions in terms of annual healthcare expenditures, a Games-Howell post hoc test was performed. This test is particularly appropriate due to the unequal variances between groups. The results revealed several significant differences: Tanger-Tétouan-Al Hoceïma: Significantly higher expenditures than in the Oriental and Marrakech-Safi regions. Significantly lower expenditures than in the Fès-Meknès, Rabat-Salé-Kénitra, Béni Mellal-Khénifra, Drâa-Tafilalet, Souss-Massa, Guelmim-Oued Noun, and Dakhla-Oued Ed Dahab regions. Oriental: Significantly lower expenditures than in the Fès-Meknès, Drâa-Tafilalet, Souss-Massa, and Dakhla-Oued Ed Dahab regions. Significantly higher expenditures than in the Marrakech-Safi and Casablanca-Settat regions. Fès-Meknès: Significantly higher expenditures than all other regions except Dakhla-Oued Ed Dahab. Significantly lower expenditures than Dakhla-Oued Ed Dahab. Rabat-Salé-Kénitra: Significantly higher expenditures than Marrakech-Safi and Casablanca-Settat. Significantly lower expenditures than Fès-Meknès, Drâa-Tafilalet, Souss-Massa, and Dakhla-Oued Ed Dahab. Béni Mellal-Khénifra: Significantly higher expenditures than Casablanca-Settat, Marrakech-Safi, Guelmim-Oued Noun, and Dakhla-Oued Ed Dahab. Significantly lower expenditures than Fès-Meknès, Rabat-Salé-Kénitra, Drâa-Tafilalet, and Souss-Massa. Casablanca-Settat: Significantly lower expenditures than Oriental, Rabat-Salé-Kénitra, Béni Mellal-Khénifra, Drâa-Tafilalet, Souss-Massa, Guelmim-Oued Noun, and Dakhla-Oued Ed Dahab. Similar expenditures to Tanger-Tétouan-Al Hoceïma. Marrakech-Safi: Significantly lower expenditures than all other regions except Casablanca-Settat. Drâa-Tafilalet: Significantly higher expenditures than all other regions except Souss-Massa and Dakhla-Oued Ed Dahab. Souss-Massa: Significantly higher expenditures than all other regions except Dakhla-Oued Ed Dahab. Guelmim-Oued Noun: Significantly lower expenditures than Fès-Meknès, Rabat-Salé-Kénitra, Béni Mellal-Khénifra, Drâa-Tafilalet, Souss-Massa, and Dakhla-Oued Ed Dahab. Similar expenditures to Casablanca-Settat and Tanger-Tétouan-Al Hoceïma. Dakhla-Oued Ed Dahab: Significantly higher expenditures than all other regions. Summary of Results The results of the post hoc test indicate significant differences in healthcare expenditures between several regions, highlighting substantial heterogeneity in the healthcare spending of vulnerable older adult households in Morocco. Regions such as Dakhla-Oued Ed Dahab, Souss-Massa, and Drâa-Tafilalet exhibit higher healthcare expenditures, while regions like Marrakech-Safi and Casablanca-Settat show lower expenditures. These disparities could be attributed to various factors, such as differences in access to healthcare services, medical infrastructure, and regional health policies. Economic Conclusion of the Hypothesis The results of the ANOVA and Games-Howell post hoc tests reveal significant disparities in annual healthcare expenditures across different regions of Morocco, highlighting crucial economic implications for public health policies and resource allocation. Regions such as Dakhla-Oued Ed Dahab, Souss-Massa, and Drâa-Tafilalet show significantly higher healthcare expenditures, which could indicate better availability and utilization of healthcare services in these areas but also potentially higher healthcare costs. Conversely, regions like Marrakech-Safi and Casablanca-Settat show lower expenditures, which could reflect limited access or lower utilization of healthcare services, signaling specific intervention needs. These marked differences underline the importance of adopting a regionally tailored approach in public health policies. Regions with lower expenditures could benefit from programs aimed at improving access to healthcare, enhancing medical infrastructure, and raising public awareness about the importance of preventive healthcare. Additionally, the findings suggest that public health resources need to be reevaluated and potentially reallocated to reduce regional disparities. For instance, regions with higher healthcare expenditures may require additional funding to maintain the quality of care, while regions with lower expenditures may need increased investments to improve access and availability of healthcare services. The variation in healthcare expenditures may also reflect broader economic disparities between regions. Regions with higher healthcare expenditures may correspond to areas with higher income levels, allowing for a greater allocation of funds for healthcare. Conversely, regions with lower expenditures may signal a population with limited purchasing power, necessitating economic assistance and subsidies to improve living conditions and access to healthcare. Given these findings, it is recommended to strengthen healthcare infrastructure in underserved regions, introduce targeted subsidies to reduce healthcare costs in economically disadvantaged areas, and implement awareness programs aimed at encouraging the use of healthcare services, particularly in rural and urban areas with lower expenditures. It is also crucial to continuously assess healthcare expenditures to adjust policies according to evolving regional needs, ensuring a flexible and effective response to the specific health challenges of each region. In conclusion, the regional disparities in healthcare expenditures of vulnerable older adult household heads in Morocco require particular attention to ensure equitable resource distribution and improved access to healthcare for all. These results should guide policymakers in developing effective strategies to reduce health inequalities and promote overall well-being on a national scale. The ultimate goal is to create a healthcare system that not only meets the immediate needs of different regions but also proactively adapts to emerging challenges, ensuring equitable and quality healthcare access for every Moroccan citizen. H2: The annual healthcare expenditures of vulnerable households differ by place of residence (rural vs. urban) Statistical Interpretation (Hypothesis H2) Hypothesis H2: The annual healthcare expenditures of vulnerable households differ by place of residence (rural vs. urban). Hypotheses: H0: There is no significant difference in annual healthcare expenditures between rural and urban residences. H1: There is a significant difference in annual healthcare expenditures between rural and urban residences. Table 4. Descriptive Statistics Residence Area N Mean Standard Deviation Standard Error of the Mean Urban 51,685 2,907.4273 4,636.55762 20.39452 Rural 70,534 2,734.5334 3,424.21961 12.89325 Source: Prepared by the authors based on data from the HCP-ENCDM survey (2014). The descriptive statistics show that households in urban areas spend an average of 2,907.43 MAD per year on medical care, while those in rural areas spend an average of 2,734.53 MAD per year. Independent Samples t-Test Table 5. Test for Equality of Variances Levene's Test for Equality of Variances F Sig. Equal variances assumed 356.140 .000 Source: Prepared by the authors based on data from the HCP-ENCDM survey (2014). The hypothesis of homogeneity of variances is rejected (p < .001), indicating that the variances of the two groups are not equal. Table 6. t-Test for Equality of Means t df Sig. (2-tailed) Mean Difference Standard Error Difference 95% CI Lower 95% CI Upper Equal variances assumed 7.498 122,217 .000 172.89387 23.05742 127.70174 Equal variances not assumed 7.166 90,642.938 .000 172.89387 24.12824 125.60277 Source: Prepared by the authors based on data from the HCP-ENCDM survey (2014). The t-test shows a significant difference in healthcare expenditures between urban and rural areas (t(122,217) = 7.498, p < .001 for equal variances; t(90,642.938) = 7.166, p < .001 for unequal variances). The mean difference in annual expenditures between the two groups is 172.89 MAD, with a 95% confidence interval ranging from 127.70 to 218.09 MAD. Table 7. Effect Size Effect Size Estimate 95% CI Lower 95% CI Upper Cohen's d .043 .032 .055 Hedges' correction .043 .032 .055 Glass's delta .050 .039 .062 Source: Prepared by the authors based on data from the HCP-ENCDM survey (2014). The effect size, measured by Cohen's d, is 0.043, indicating a small difference between the two groups. Economic Conclusion The t-test results show a statistically significant difference in annual healthcare expenditures between urban and rural households, with urban households spending an average of 172.89 MAD more than rural households on medical care. Although the effect size is small, this difference may have important economic implications. Urban households appear to benefit from better access to healthcare services, resulting in higher expenditures, while rural households, despite spending less, may face limited access to quality healthcare services. To address this situation, it is essential to improve healthcare infrastructure in rural areas to reduce this expenditure gap and ensure equitable access to healthcare for all citizens. Targeted subsidies for rural households could also be considered to offset the additional costs associated with limited access and the quality of healthcare services. Additionally, the implementation of awareness and health education programs in rural areas could encourage greater use of preventive healthcare services, ultimately contributing to balancing expenditures between urban and rural areas. In summary, the observed differences in healthcare expenditures between urban and rural areas highlight the need for an integrated policy approach aimed at improving access to healthcare and reducing regional health disparities in Morocco. H3: The annual healthcare expenditures of vulnerable households differ by gender Statistical Interpretation (Hypothesis H3) Hypothesis H3: The annual healthcare expenditures of vulnerable households differ by the gender of the household head. Hypotheses: H0: There is no significant difference in annual healthcare expenditures between male and female household heads. H1: There is a significant difference in annual healthcare expenditures between male and female household heads. Table 8. Descriptive Statistics Gender of Household Head N Mean Standard Deviation Standard Error of the Mean Male 99,974 2,799.2360 3,769.88046 11.92296 Female 22,245 2,845.4550 4,826.33663 32.35947 Source: Prepared by the authors based on data from the HCP-ENCDM survey (2014). The descriptive statistics show that households headed by men spend an average of 2,799.24 MAD per year on medical care, while those headed by women spend an average of 2,845.46 MAD per year. Independent Samples t-Test Table 9. Test for Equality of Variances Levene's Test for Equality of Variances F Sig. Equal variances assumed 508.870 .000 Source: Prepared by the authors based on data from the HCP-ENCDM survey (2014). The hypothesis of homogeneity of variances is rejected (p < .001), indicating that the variances of the two groups are not equal. Table 10. t-Test for Equality of Means t df Sig. (2-tailed) Mean Difference Standard Error Difference 95% CI Lower 95% CI Upper Equal variances assumed -1.565 122,217 .118 -46.21901 29.52759 -104.09257 Equal variances not assumed -1.340 28,576.378 .180 -46.21901 34.48612 -113.81340 Source: Prepared by the authors based on data from the HCP-ENCDM survey (2014). The t-test shows that the difference in healthcare expenditures between households headed by men and women is not statistically significant (t(122,217) = -1.565, p = .118 for equal variances; t(28,576.378) = -1.340, p = .180 for unequal variances). The mean difference in annual expenditures between the two groups is -46.22 MAD, with a 95% confidence interval ranging from -104.09 to 11.65 MAD for equal variances and from -113.81 to 21.38 MAD for unequal variances. Table 11: Effect Size Effect Size Estimate 95% CI Lower 95% CI Upper Cohen's d -.012 -.026 .003 Hedges' correction -.012 -.026 .003 Glass's delta -.010 -.024 .005 Source: Prepared by the authors based on data from the HCP-ENCDM survey (2014). The effect size, measured by Cohen's d, is -0.012, indicating a negligible difference between the two groups. Economic Conclusion The t-test results reveal that there is no statistically significant difference in annual healthcare expenditures between male and female household heads. Although female household heads have slightly higher average expenditures, this difference is not statistically significant. This suggests that the gender of the household head does not have a substantial impact on healthcare expenditures, which may indicate relative equality in access to healthcare services for male and female household heads. Consequently, health policies can legitimately continue to focus on other factors that influence healthcare expenditures, such as residence area and socio-economic status. However, it remains crucial to maintain inclusive and non-discriminatory health awareness and education programs to ensure that all household heads, regardless of gender, have equitable access to the necessary information and services. Additionally, financial support policies should remain fair and accessible to all household heads, ensuring that female household heads do not encounter additional barriers in accessing healthcare. In conclusion, these results show relative equity in healthcare expenditures between male and female household heads, which is encouraging for initiatives aimed at promoting gender equality in healthcare access in Morocco. H4: The annual healthcare expenditures of vulnerable households differ by household size Statistical Interpretation (Hypothesis H4) Hypothesis H4: The annual healthcare expenditures of vulnerable households differ by household size. Hypotheses: H0: There is no significant difference in annual healthcare expenditures by household size. H1: There is a significant difference in annual healthcare expenditures by household size. Table 12: Test of Homogeneity of Variances Test of Homogeneity of Variances Levene Statistic df1 df2 Sig. Healthcare Expenditure per Household (in MAD) Based on Mean 556.479 5 122,213 .000 Based on Median 337.538 5 122,213 .000 Based on Median and with adjusted df 337.538 5 105,692.827 .000 Based on trimmed mean 393.173 5 122,213 .000 Source: Prepared by the authors based on data from the HCP-ENCDM survey (2014). The hypothesis of homogeneity of variances is rejected (p < .001), indicating that the variances between groups are not equal. Table 13. ANOVA Results for difference healthcare expenditures by household size Source of Variation Sum of Squares df Mean Square F Sig. Between Groups 27,204,444,285.084 5 5,440,888,857.017 347.814 .000 Within Groups 1,911,791,748,943.075 122,213 15,643,112.835 Total 1,938,996,193,228.158 122,218 Source: Prepared by the authors based on data from the HCP-ENCDM survey (2014). The ANOVA shows a significant difference between household sizes (F(5, 122,213) = 347.814, p < .001). Multiple Comparisons: Games-Howell Test The results of the Games-Howell post hoc test reveal several significant differences between household sizes: 1 person vs. 2 persons: Households with 1 person spend significantly less than those with 2 persons (mean difference = -1,899.00 MAD, p < .001). 1 person vs. 3 persons: Households with 1 person spend significantly less than those with 3 persons (mean difference = -1,368.20 MAD, p < .001). 1 person vs. 4 persons: Households with 1 person spend significantly less than those with 4 persons (mean difference = -1,853.77 MAD, p < .001). 1 person vs. 5 persons: Households with 1 person spend significantly less than those with 5 persons (mean difference = -1,479.80 MAD, p < .001). 1 person vs. 6 persons and more: Households with 1 person spend significantly less than those with 6 persons and more (mean difference = -2,666.75 MAD, p < .001). 2 persons vs. 3 persons: Households with 2 persons spend significantly more than those with 3 persons (mean difference = 530.80 MAD, p < .001). 2 persons vs. 4 persons: No significant difference between households with 2 and 4 persons. 2 persons vs. 5 persons: Households with 2 persons spend significantly less than those with 5 persons (mean difference = 419.20 MAD, p < .001). 2 persons vs. 6 persons and more: Households with 2 persons spend significantly less than those with 6 persons and more (mean difference = -767.75 MAD, p < .001). 3 persons vs. 4 persons: Households with 3 persons spend significantly less than those with 4 persons (mean difference = -485.57 MAD, p < .001). 3 persons vs. 5 persons: No significant difference between households with 3 and 5 persons. 3 persons vs. 6 persons and more: Households with 3 persons spend significantly less than those with 6 persons and more (mean difference = -1,298.56 MAD, p < .001). 4 persons vs. 5 persons: Households with 4 persons spend significantly more than those with 5 persons (mean difference = 373.96 MAD, p < .001). 4 persons vs. 6 persons and more: Households with 4 persons spend significantly less than those with 6 persons and more (mean difference = -812.99 MAD, p < .001). 5 persons vs. 6 persons and more: Households with 5 persons spend significantly less than those with 6 persons and more (mean difference = -1,186.95 MAD, p < .001). Economic Conclusion The results of the ANOVA and Games-Howell post hoc tests reveal significant differences in annual healthcare expenditures based on household size, showing that larger households tend to spend more on healthcare compared to smaller households. This suggests that these households, due to their size, may require greater attention in terms of support policies and access to healthcare services. Therefore, it is crucial that financial support programs are specifically targeted towards larger households to offset the additional costs associated with healthcare. Additionally, public health policies must integrate the dimension of household size when developing interventions and budget allocations. For larger households, preventive healthcare programs and community health services could be particularly beneficial, helping to reduce per capita costs. It is also important that health education and awareness campaigns are tailored to the needs of households of different sizes to ensure effective and equitable use of healthcare services. In summary, the observed differences in healthcare expenditures by household size highlight the importance of adapting health policies and financial interventions to meet the specific needs of larger households, ensuring equitable access and affordable healthcare for all. H5: The annual healthcare expenditures of vulnerable households differ by the profession of the household head Statistical Interpretation (Hypothesis H5) Hypothesis H5: The annual healthcare expenditures of vulnerable households differ by the profession of the household head. Hypotheses: H0: There is no significant difference in annual healthcare expenditures by the profession of the household head. H1: There is a significant difference in annual healthcare expenditures by the profession of the household head. Table 14. Test of Homogeneity of Variances Test of Homogeneity of Variances Levene Statistic df1 df2 Sig. Healthcare Expenditure per Household (in MAD) Based on Mean 285.481 5 122,213 .000 Based on Median 244.569 5 122,213 .000 Based on Median and with adjusted df 244.569 5 95,672.471 .000 Based on trimmed mean 256.641 5 122,213 .000 Source: Prepared by the authors based on data from the HCP-ENCDM survey (2014). The hypothesis of homogeneity of variances is rejected (p < .001), indicating that the variances between groups are not equal. Table 15. ANOVA Results of annual healthcare expenditures of vulnerable households by the profession Source of Variation Sum of Squares df Mean Square F Sig. Between Groups 1,734,828,276.784 5 346,965,655.357 20.371 .000 Within Groups 1,937,261,364,951.374 122,213 15,847,868.930 Total 1,938,996,193,228.158 122,218 Source: Prepared by the authors based on data from the HCP-ENCDM survey (2014). The ANOVA shows a significant difference between the professions of the household heads (F(5, 122,213) = 20.371, p < .001). Multiple Comparisons: Games-Howell Test The results of the Games-Howell post hoc test reveal several significant differences between the professions of the household heads: Agriculture, Forestry, and Fishing vs. Industry: Households led by heads in the Agriculture, Forestry, and Fishing sector spend significantly less than those in the Industry sector (mean difference = -1,245.63 MAD, p < .001). Agriculture, Forestry, and Fishing vs. Commerce: Households led by heads in the Agriculture, Forestry, and Fishing sector spend significantly less than those in the Commerce sector (mean difference = -838.52 MAD, p < .001). Agriculture, Forestry, and Fishing vs. Services: Households led by heads in the Agriculture, Forestry, and Fishing sector spend significantly less than those in the Services sector (mean difference = -776.97 MAD, p < .001). Industry vs. Construction and Public Works: Households led by heads in the Industry sector spend significantly more than those in the Construction and Public Works sector (mean difference = 352.20 MAD, p = .009). Construction and Public Works vs. Commerce: Households led by heads in the Construction and Public Works sector spend significantly less than those in the Commerce sector (mean difference = -486.32 MAD, p < .001). Construction and Public Works vs. Services: Households led by heads in the Construction and Public Works sector spend significantly less than those in the Services sector (mean difference = -424.77 MAD, p < .001). Commerce vs. Services: No significant difference between households led by heads in the Commerce sector and those in the Services sector. Economic Conclusion The results of the ANOVA and Games-Howell post hoc tests reveal significant differences in annual healthcare expenditures based on the profession of the household head. Households led by heads in the Agriculture, Forestry, and Fishing sector spend significantly less on healthcare compared to those in other sectors, particularly the Industry, Commerce, and Services sectors. These differences may be due to varying income levels, access to healthcare, and health insurance coverage associated with different professions. It is essential for policymakers to consider these disparities when designing healthcare and financial support programs. Targeted interventions should be implemented for households in sectors with lower healthcare expenditures, such as Agriculture, Forestry, and Fishing, to improve access to healthcare and reduce financial barriers. Additionally, expanding health insurance coverage and providing subsidies for healthcare in these sectors could help mitigate the observed disparities. In conclusion, the profession of the household head plays a significant role in determining healthcare expenditures in vulnerable households in Morocco. To ensure equitable access to healthcare, it is crucial to address the unique challenges faced by households in different sectors and to tailor public health policies and financial support accordingly. H6: The annual healthcare expenditures of vulnerable households differ by the education level of the household head Statistical Interpretation (Hypothesis H6) Hypothesis H6: The annual healthcare expenditures of vulnerable households differ by the education level of the household head. Hypotheses: H0: There is no significant difference in annual healthcare expenditures by the education level of the household head. H1: There is a significant difference in annual healthcare expenditures by the education level of the household head. Table 16. Test of Homogeneity of Variances Test of Homogeneity of Variances Levene Statistic df1 df2 Sig. Healthcare Expenditure per Household (in MAD) Based on Mean 251.106 4 122,214 .000 Based on Median 174.493 4 122,214 .000 Based on Median and with adjusted df 174.493 4 93,631.315 .000 Based on trimmed mean 204.824 4 122,214 .000 Source: Prepared by the authors based on data from the HCP-ENCDM survey (2014). The hypothesis of homogeneity of variances is rejected (p < .001), indicating that the variances between groups are not equal. Table 17. ANOVA Results of The annual healthcare expenditures of vulnerable households by the education level Source of Variation Sum of Squares df Mean Square F Sig. Between Groups 3,421,664,447.907 4 855,416,111.977 50.760 .000 Within Groups 1,935,574,528,780.251 122,214 15,834,129.468 Total 1,938,996,193,228.158 122,218 Source: Prepared by the authors based on data from the HCP-ENCDM survey (2014). The ANOVA shows a significant difference between the education levels of the household heads (F(4, 122,214) = 50.760, p < .001). Multiple Comparisons: Games-Howell Test The results of the Games-Howell post hoc test reveal several significant differences between the education levels of the household heads: Illiterate vs. Primary Education: Households led by illiterate heads spend significantly less than those led by heads with primary education (mean difference = -602.31 MAD, p < .001). Illiterate vs. Secondary Education: Households led by illiterate heads spend significantly less than those led by heads with secondary education (mean difference = -1,116.65 MAD, p < .001). Illiterate vs. Higher Education: Households led by illiterate heads spend significantly less than those led by heads with higher education (mean difference = -2,348.94 MAD, p < .001). Illiterate vs. Other Education Levels: Households led by illiterate heads spend significantly less than those led by heads with other education levels (mean difference = -413.52 MAD, p < .001). Primary Education vs. Secondary Education: Households led by heads with primary education spend significantly less than those led by heads with secondary education (mean difference = -514.34 MAD, p < .001). Primary Education vs. Higher Education: Households led by heads with primary education spend significantly less than those led by heads with higher education (mean difference = -1,746.63 MAD, p < .001). Primary Education vs. Other Education Levels: Households led by heads with primary education spend significantly less than those led by heads with other education levels (mean difference = -188.79 MAD, p = .015). Secondary Education vs. Higher Education: Households led by heads with secondary education spend significantly less than those led by heads with higher education (mean difference = -1,232.29 MAD, p < .001). Secondary Education vs. Other Education Levels: Households led by heads with secondary education spend significantly less than those led by heads with other education levels (mean difference = 325.55 MAD, p = .000). Higher Education vs. Other Education Levels: Households led by heads with higher education spend significantly more than those led by heads with other education levels (mean difference = 906.74 MAD, p < .001). Economic Conclusion The results of the ANOVA and Games-Howell post hoc tests reveal significant differences in annual healthcare expenditures based on the education level of the household head. Households led by heads with higher education spend significantly more on healthcare compared to those led by heads with lower education levels, particularly illiterate heads. These differences may be attributed to the higher income levels, better health awareness, and greater access to healthcare services typically associated with higher education. To address these disparities, public health policies should focus on improving healthcare access and education for households led by less educated heads. This could include implementing health literacy programs and providing financial assistance to support healthcare expenditures for these households. Additionally, targeted interventions aimed at raising awareness about preventive healthcare in lower education groups could help reduce the observed disparities. In conclusion, the education level of the household head is a significant determinant of healthcare expenditures in vulnerable households in Morocco. It is crucial to consider this factor when designing health policies and financial support programs to ensure equitable access to healthcare services across different education levels. General Conclusion This study provided an in-depth analysis of the socio-economic factors influencing the annual healthcare expenditures of older adult vulnerable household heads in Morocco, revealing several key findings. First, the use of data from the 2013/2014 National Household Consumption and Expenditure Survey, covering a large sample of 122,218 households, allowed for the generation of representative and reliable results. This wealth of data provided an overview of the determinants of healthcare expenditures across a diverse population, encompassing different regions and socio-economic contexts. Additionally, the study examined a variety of factors, including region, place of residence, gender, household size, profession of the household head, and education level, offering a comprehensive overview of the determinants of healthcare expenditures. By examining these multiple variables, the study was able to identify complex patterns of socio-economic influences on healthcare expenditures, enriching our understanding of the underlying dynamics. The use of robust statistical techniques such as ANOVA and Games-Howell post hoc tests identified significant differences and ensured the validity of the results. These rigorous methods helped guarantee that the conclusions drawn are solid and reliable, providing a foundation upon which policymakers can rely to formulate informed policies. However, this study also has certain limitations that should be considered. The data used are from the 2013/2014 survey, and economic conditions and health policies may have evolved since then, potentially affecting the current relevance of the results. The rapid evolution of economic contexts and public policies means that the conclusions drawn from this study may no longer accurately reflect the current situation. It is therefore crucial to regularly update the data to maintain the relevance and applicability of the results. While the study included several socio-economic factors, other potential variables influencing healthcare expenditures, such as the general health status of individuals or access to health insurance, were not considered. Including these variables could provide a more nuanced understanding of the determinants of healthcare expenditures. For example, a person's general health status may significantly influence their healthcare expenditures, and access to health insurance could mitigate the financial impacts of medical care. Furthermore, the study is based on a cross-sectional analysis, limiting the ability to establish causal relationships between the factors studied and healthcare expenditures. A cross-sectional analysis provides a snapshot of the relationships between variables at a given moment, but it does not allow for determining how these relationships evolve over time. To address this limitation, longitudinal studies would be necessary, allowing for the tracking of households over an extended period and identifying trends and changes in healthcare expenditures. To extend this research, several avenues can be explored. It would be beneficial to conduct a new survey with more recent data to assess the current impact of socio-economic factors on healthcare expenditures. Updating the data would capture recent changes in economic conditions and health policies, providing a more current picture of the determinants of healthcare expenditures. The implementation of longitudinal studies would allow for tracking households over an extended period and identifying trends and changes in healthcare expenditures over time. By following the same households over several years, researchers can observe how healthcare expenditures evolve in response to changes in socio-economic factors, public policies, and economic conditions. Including additional variables such as general health status, access to health insurance, and regional health policies would provide a more comprehensive understanding of the determinants of healthcare expenditures. These variables may play crucial roles in determining healthcare expenditures, and their inclusion could reveal important insights that were previously missing. A comparison with similar studies conducted in other developing countries could offer interesting perspectives and innovative solutions applicable to the Moroccan context. By comparing the results with those of other countries, researchers can identify common patterns and unique differences, guiding the development of more effective and tailored policies. Finally, the results of this study can guide policymakers in developing targeted public health strategies to reduce regional and socio-economic disparities and improve equitable access to healthcare for vulnerable older adult individuals. Policymakers can use these insights to develop specific interventions that address the needs of the most vulnerable groups, ensuring that all segments of the population have adequate access to healthcare. In summary, this study highlights significant disparities in healthcare expenditures among older adult vulnerable household heads in Morocco, according to various socio-economic factors. The results underscore the importance of adopting inclusive and adapted public health policies to ensure equitable access to healthcare and improve the quality of life for this population. The future perspectives suggest avenues for research and action that could contribute to better understanding and addressing the healthcare needs of older adult individuals in Morocco. By continuing this line of research, scholars can continue to inform public policies and promote better health for all. Declarations Author Contribution Déclaration de contribution des auteursSaid Loucifi first author , Abdelkader Salmi first author, Mohammed Saber Hassainate, second author, wrote the main manuscript text. All authors reviewed the manuscript References Arber, S., & Ginn, J. (1991). Gender and Later Life: A Sociological Analysis of Resources and Constraints. Sage Publications. Arber, S., & Ginn, J. (1991). The invisibility of age: gender and class in later life. The sociological review,, 39(2), 260-291. Baltes, P. B., & Baltes, M. M. (1990). Successful Aging: Perspectives from the Behavioral Sciences. Cambridge University Press. Balarajan, Y., Selvaraj, S., & Subramanian, S. V. (2011). "Health care and equity in India." The Lancet, 377(9764), 505-515. Banks, J., et al. (2018). "Common health predictors of early retirement: findings from the English Longitudinal Study of Ageing." Age and Ageing. Banks, J., et al. (2018). "Predictors of Early Retirement in Older Workers: Health and Financial Considerations." Work Aging and Retirement, 4(2), 125-143. Bartley, M., Ferrie, J., Montgomery, S., Marmot, M., & Wilkinson, R. (2006) . Social determinants of health. The Solid Facts, 1-32. BMC Public Health. (2019). "Analysis of healthcare expenditures by profession in Morocco: A comparative study." BMC Public Health. BMC Public Health. (2021). "Regional Disparities in Health Expenditures in India: An Analysis of Health Service Accessibility and Policy Implications." BMC Public Health. BMC International Health and Human Rights. (2020). "Health Expenditure Disparities in Rural and Urban Areas of Tanzania." BMC International Health and Human Rights. Blossfeld, H. P., Buchholz, S., & Kurz, K. (2011). "Adding Scars to Wrinkles? Long-run Effects of Late-Career Job Loss on Retirement Behavior and Personal Income." Work, Aging and Retirement. Blossfeld, H.P., et al. (2011). "The Impact of Late-Career Job Loss on Financial Well-being and Retirement Behavior." Work Aging and Retirement, 1(1), 100-117. Burch, K. A., Dugan, A. G., & Barnes-Farrell, J. L. (2019). "Understanding What Eldercare Means for Employees and Organizations: A Review and Recommendations for Future Research." Work, Aging and Retirement, 5(1), 44–72. Chatterji, S., Kowal, P., & Mathers, C. (2008). The Health of Aging Populations in Developing Countries. World Health Organization. Chatterji, P., Alegria, M., Lu, M., & Takeuchi , D. (2008 ). Psychiatric Disorders and Employment: New Evidence from the Collaborative Psychiatric Epidemiology Surveys (CPES). National Bureau of Economic Research Working Paper w14404. Cylus, J., & Papanicolas, I. (2015). Health System Performance Comparison: An Agenda for Policy Information and Research. European Observatory on Health Systems and Policies Series. Fisher, G., & Ryan, L. (2018). Overview of the health and retirement study and introduction to the special issue. Work, aging and retirement,, 4(1), 1-9. Fuchs, V. R. (2004). "Reflections on the Socio-Economic Correlates of Health." Journal of Health Economics, 23(4), 653-661. Gignac, M. A. M., Bowring, J., Shahidi, F. V., Kristman, V., Cameron, J. I., & Jetha, A. (2024). "Workplace Disclosure Decisions Among Older Workers Seeking to Retain Employment: A Qualitative Study of Factors Considered When Contemplating Whether to Disclose or Conceal Support Needs." Work, Aging and Retirement, 10(2), 174-187. Globalization and Health. (2021). "Gender Differences in Health Expenditures: The Role of Female Household Heads in Health Budget Allocation." Globalization and Health. Globalization and Health. (2021). "The Role of Education in Health Expenditures: A Study of Preventive Health Behaviors." Globalization and Health. Guralnik, J. M., Fried, L. P., & Salive, M. E. (1996). "Disability as a Public Health Outcome in the Aging Population." Annual Review of Public Health, 17, 25-46. Guralnik, J. (1996). Assessing the impact of comorbidity in the older population. Annals of epidemiology, 6(5), 376-380 Health Economics Review. (2021). "Household Size and Health Expenditures: An Econometric Analysis." Health Economics Review. Marmot, M., & Wilkinson, R. G. (2006). Social Determinants of Health. Oxford University Press. Möwisch, D., Brose, A., & Schmiedek, F. (2023). "Active Time Use and Well-Being in Older Adulthood: Results From a Day Reconstruction Method Study." Work, Aging and Retirement, 9(1), 7–18. Nair, M., & Webster, P. (2013). Health professionals' migration in emerging market economies: patterns, causes and possible solutions. Journal of public health, 35(1), 157-163. Nair, P., Rajan, S. I., & Srinivasan, K. (2013). "Health Expenditure of the Older adult in India: Evidence and Implications." Journal of Aging & Social Policy, 25(3), 164-178. Rowe, J. W., & Kahn, R. L. (1997). "Successful Aging." The Gerontologist, 37(4), 433-440. O’Donnell, O., Van Doorslaer, E., Wagstaff, A., & Lindelow, M. (2008). Analyzing Health Equity Using Household Survey Data. World Bank Publications. OECD. (2019). Health at a Glance 2019: OECD Indicators. OECD Publishing. Selvaraj, S., & Karan, A. K. (2012). "Why Publicly Financed Health Insurance Schemes Are Ineffective in Offering Financial Risk Protection." Economic and Political Weekly, 60-68. Smith, S., Newhouse, J. P., & Freeland, M. S. (2010). "Income, Insurance, and Technology: Why Does Health Spending Outpace Economic Growth?" Health Affairs, 28(5), 1276-1284. Wagstaff, A., & Van Doorslaer, E. (2003). "Catastrophe and Impoverishment in Paying for Health Care: With Applications to Vietnam 1993–1998." Health Economics, 12(11), 921-933. Work Aging and Retirement. (2018). "The Impact of Precarious Employment on Health Expenditures: Evidence from Vulnerable Households." Work Aging and Retirement. World Health Organization. (2024). "Are Health Services Financially Accessible? New Evidence on Financial Protection in France." WHO Report on Financial Protection in France. World Health Organization. (2024). "Are Health Services Affordable? New Evidence on Financial Protection in Belgium." WHO Report on Financial Protection in Belgium. Oxford University Press. (2019). "Health Care Spending in Low- and Middle-Income Countries: Global Challenges and Opportunities." Smith, P.C., & Mossialos, E. (2002). "Health Systems in Low- and Middle-Income Countries: An Assessment." Oxford University Press. Stansfeld, S., Marmot, M., & Wilkinson, R. (2006). Social support and social cohesion. Social determinants of health, 2, 148-71. Xu, K., Saksena, P., & Jowett, M. (2012). "The Determinants of Health Expenditure: A Country-Level Panel Data Analysis." World Health Report. Xu, X., Lazar, C., & Ruger, J. (2021 ). Micro-costing in health and medicine: a critical appraisal. Health economics review, 11, 1-8 Additional Declarations No competing interests reported. Cite Share Download PDF Status: Published Journal Publication published 11 May, 2025 Read the published version in Archives of Gerontology and Geriatrics Plus → Version 1 posted You are reading this latest preprint version Research Square lets you share your work early, gain feedback from the community, and start making changes to your manuscript prior to peer review in a journal. As a division of Research Square Company, we’re committed to making research communication faster, fairer, and more useful. We do this by developing innovative software and high quality services for the global research community. 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Also discoverable on Platform About Our Team In Review Editorial Policies Advisory Board Help Center Resources Author Services Accessibility API Access RSS feed Manage Cookie Preferences © Research Square 2026 | ISSN 2693-5015 (online) Privacy Policy Terms of Service Do Not Sell My Personal Information {"props":{"pageProps":{"initialData":{"identity":"rs-5767434","acceptedTermsAndConditions":true,"allowDirectSubmit":true,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":398801353,"identity":"28f144a7-f0d6-4cbc-af73-0be0a92141e7","order_by":0,"name":"SAID LOUCIFI","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAA7ElEQVRIiWNgGAWjYFACxgaJB0CKjflg42OwADNzA2EtCSAtbInNxgwMBkAtjIS0MDCAtTCwJbBJg7UwENAi33648UZCxWF5PjbmtuqCij/R/O1ALT8qtuHUYnAmsdki4cxhwzY2xrbbM84Y5M44zNjA2HPmNm4tDIltEolthxPY5BvbbvO2GeQ2ALUwA7Xjdlj/Q6gWoC3FIC3zCWlhuJGI0MIM0rKBkBaDGw9BfkkH+aVZmueMce5GoJaD+Pwi35/+8MaHCmt5+Tb2h595KuRy550/fPDBjwo8DsMKDpCofhSMglEwCkYBGgAAbKpXMqGqZHcAAAAASUVORK5CYII=","orcid":"","institution":"Mohammed V University in Rabat","correspondingAuthor":true,"prefix":"","firstName":"SAID","middleName":"","lastName":"LOUCIFI","suffix":""},{"id":398801354,"identity":"646b259d-13b4-4a38-970f-e36c9635156d","order_by":1,"name":"Abdelkader Salmi","email":"","orcid":"","institution":"Mohammed V University in Rabat","correspondingAuthor":false,"prefix":"","firstName":"Abdelkader","middleName":"","lastName":"Salmi","suffix":""},{"id":398801357,"identity":"0d3dd981-c1af-423b-9949-3b80eaf91d6e","order_by":2,"name":"Mohammed Saber Hassainate","email":"","orcid":"","institution":"Mohammed V University in Rabat","correspondingAuthor":false,"prefix":"","firstName":"Mohammed","middleName":"Saber","lastName":"Hassainate","suffix":""}],"badges":[],"createdAt":"2025-01-05 11:08:09","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-5767434/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-5767434/v1","draftVersion":[],"editorialEvents":[{"content":"https://doi.org/10.1016/j.aggp.2025.100146","type":"published","date":"2025-05-12T00:00:00+00:00"}],"editorialNote":"","failedWorkflow":false,"files":[{"id":82732190,"identity":"e36f27ba-ef67-400d-a9c5-201124de756d","added_by":"auto","created_at":"2025-05-14 15:07:05","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":1838869,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-5767434/v1/65f8c6f6-b2e3-46b1-badd-ab5e66e1363e.pdf"}],"financialInterests":"No competing interests reported.","formattedTitle":"Impact of Socio-Economic Factors on Healthcare Expenditures of Vulnerable Elderly People in Morocco","fulltext":[{"header":"Introduction","content":"\u003cp\u003eIn Morocco, the situation of the elderly has become a major concern, particularly with the evolution of social protection. The current age pyramid reveals a dominance of youth and adults (15-59 years), but the proportion of those aged 60 and over is constantly increasing, rising from 12.2% in 2022 to a projected 23.2% in 2050 (HCP, 2023). This trend is explained by a rising life expectancy\u0026mdash;78 years for men and 82 years for women\u0026mdash;and the overall improvement in healthcare services.\u003c/p\u003e\n\u003cp\u003eThe general census of population and housing, conducted every ten years by the High Commission for Planning (HCP), allows for monitoring the evolution of different age groups. These data are essential to raise public awareness about adopting social policies in favor of seniors. According to the National Household Consumption and Expenditure Survey of 2013/2014, there were 135,387 male household heads compared to 31,792 female heads, highlighting a notable gender disparity.\u003c/p\u003e\n\u003cp\u003eThe central question of our research is to identify the socio-economic factors that influence the annual health expenditures of vulnerable elderly household heads in Morocco. The concept of \u0026quot;elderly\u0026quot; is complex, encompassing not only chronological age but also vulnerability linked to various socio-economic and health factors. The traditional definition based on retirement age is insufficient, as it does not account for all aspects of vulnerability that seniors may experience.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eContext and Rationale\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003ePopulation aging is a global phenomenon that presents unique challenges for healthcare systems, especially in developing countries like Morocco. The increasing number of vulnerable elderly individuals requires special attention to understand how socio-economic factors affect their health expenditures. Regional inequalities, differences between urban and rural areas, as well as professional and educational disparities, can exacerbate the vulnerability of this population.\u003c/p\u003e\n\u003cp\u003eFor instance, rural regions in Morocco often face limited access to healthcare services, which can increase costs for the elderly who must travel long distances to receive care (Benbrahim et al., 2015). Moreover, elderly individuals with low levels of education may be less informed about available health services, affecting their ability to access necessary care (Chatterji et al., 2008).\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eObjectives of the Study\u003c/strong\u003e\u003c/p\u003e\n\u003cul\u003e\n\u003cli\u003eIdentify the socio-economic factors influencing the annual health expenditures of vulnerable elderly household heads in Morocco.\u003c/li\u003e\n\u003cli\u003eAssess the impact of variables such as region, residence setting, household size, profession, and education level on these expenditures.\u003c/li\u003e\n\u003cli\u003eFormulate recommendations for adapted public health policies aimed at reducing health inequalities.\u003c/li\u003e\n\u003c/ul\u003e\n\u003cp\u003e\u003cstrong\u003eSignificance of the Study\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThis research makes a significant contribution to the existing literature by providing a detailed analysis of health expenditures among vulnerable elderly individuals in Morocco. It highlights regional and socio-economic disparities, offering a solid basis for developing more equitable public policies. Additionally, by employing rigorous statistical methods such as ANOVA and t-tests, the study ensures the reliability and validity of its conclusions.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eStructure of the Article\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe article is structured as follows:\u003c/p\u003e\n\u003cp\u003e1. Literature Review: Presentation of key concepts related to successful aging and existing models, as well as an analysis of previous studies on socio-economic factors affecting health expenditures.\u003c/p\u003e\n\u003cp\u003e2. Methodology: Description of the data used, the hypotheses formulated, and the statistical methods employed for analysis.\u003c/p\u003e\n\u003cp\u003e3. Results: Detailed presentation of statistical analyses and interpretation of the obtained results.\u003c/p\u003e\n\u003cp\u003e4. Discussion: Interpretation of the results in the context of public health policies and implications for reducing health inequalities.\u003c/p\u003e\n\u003cp\u003e5. Conclusion: Synthesis of the main findings, limitations of the study, and suggestions for future research.\u003c/p\u003e\n\u003cp\u003eIn summary, this study aims to provide an in-depth understanding of the factors influencing health expenditures among vulnerable elderly individuals in Morocco, to contribute to more effective and equitable health policies.\u003c/p\u003e"},{"header":"Theoretical background and hypotheses development ","content":"\u003cp\u003eIn recent years, the concept of \u0026quot;successful aging\u0026quot; has gained prominence in the field of gerontology. More and more people are reaching advanced ages without experiencing the declines once associated with aging. Introduced during the annual meeting of the Gerontological Society of America in 1986, this concept encompasses terms like \u0026quot;aging well\u0026quot; and \u0026quot;active aging.\u0026quot; It offers a positive perception of aging, emphasizing the possibility of preventing or delaying problems associated with old age.\u003c/p\u003e\n\u003cp\u003eSuccessful aging refers to a state of good physical and mental health, as well as active participation in society. Several conceptual models have been developed to explain this phenomenon, integrating biological, psychological, and social dimensions. For example, Rowe and Kahn\u0026apos;s (1997) three-dimensional model identifies three interconnected components of successful aging: the absence of disease, a high level of physical and cognitive functioning, and an active life in professional and social domains.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eSocio-Economic Factors and Health Expenditures\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eHealth expenditures among the elderly are influenced by a multitude of socio-economic factors, including income, education level, access to healthcare, and social support. Fuchs (2004) demonstrated that individuals with higher education and income levels tend to spend more on their health, resulting in better health outcomes and longevity. (Bartley, Ferrie, Montgomery, Marmot, \u0026amp; Wilkinson, 2006)emphasized the importance of social determinants of health, indicating that social inequalities significantly contribute to health disparities among the elderly.\u003c/p\u003e\n\u003cp\u003eSpecific studies, such as (Chatterji, Alegria, Lu, \u0026amp; Takeuchi , 2008), have shown that elderly individuals with higher education levels and stable income are more likely to invest in preventive care and have easier access to health services. This underscores the importance of education and financial stability in promoting healthy aging.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eVulnerability and Health of the Elderly\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe vulnerability of the elderly is a multidimensional concept encompassing physical health, mental health, and social well-being. It is often exacerbated by socio-economic factors such as poverty, social isolation, and limited access to healthcare services. (Guralnik, 1996) showed that elderly individuals living in socio-economically disadvantaged conditions are more likely to suffer from chronic diseases and functional limitations. Additionally, gender plays an important role in the vulnerability of the elderly, with women often more likely to live in poverty and suffer from chronic illnesses (Arber \u0026amp; Ginn, 1991).\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eChallenges in Developing Countries\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eIn developing countries, challenges are often more complex due to limited resources and inadequate health infrastructures. Nair et al. (2013) showed that in these countries, the elderly spend a significant portion of their income on healthcare, which can lead to considerable financial difficulties. It is therefore essential that public health programs make healthcare more affordable and accessible.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eThe Case of Morocco\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eIn Morocco, health expenditures among vulnerable elderly household heads vary considerably across regions. Benbrahim et al. (2015) revealed that urban regions generally benefit from better access to healthcare than rural regions, leading to significant disparities in health expenditures. These regional differences can be attributed to the availability of health services, the quality of medical infrastructures, and regional health policies.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eSynthesis of the Literature\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe existing literature underscores the importance of understanding regional and socio-economic disparities in health expenditures among the elderly. Public health policies must be adapted to meet the specific needs of regions and socio-economic groups to reduce health inequalities. This study aims to fill gaps by providing a detailed analysis of socio-economic factors influencing health expenditures among vulnerable elderly individuals in Morocco.\u003c/p\u003e"},{"header":"Methodology","content":"\u003cp\u003e\u003cstrong\u003eResearch Question\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eWhat are the socio-economic factors that influence the annual health expenditures of vulnerable elderly household heads in Morocco?\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eResearch Hypotheses\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eTo answer this question, we formulated the following hypotheses, based on existing literature and the Moroccan socio-economic context:\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eHypothesis H1: Annual health expenditures of vulnerable households differ by region.\u003c/strong\u003e\u003cbr\u003e\u0026nbsp;As a consequence, we hypothesize that regional disparities play a crucial role in access to health services and associated expenditures. Previous studies have shown that health infrastructure, availability of medical personnel, and regional health policies vary considerably from one region to another (Benbrahim et al., 2015). For example, a study in Tanzania found that rural households had relatively lower health expenditures but faced disproportionate costs due to limited access to quality health services, compared to urban households that have better access but higher overall costs (BMC International Health and Human Rights, 2020). Therefore, it is relevant to examine whether such regional disparities exist in Morocco and how they influence health expenditures\u0026nbsp;among vulnerable elderly individuals.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eHypothesis H2: Annual health expenditures of vulnerable households differ by residence setting (urban/rural).\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eIn light of this, we hypothesize that the residence setting is a determining factor in access to healthcare. Rural areas are often characterized by limited access to health infrastructures, a shortage of health professionals, and greater distances to reach care centers (Nair \u0026amp; Webster, 2013). This can lead to lower expenditures due to restricted access or, conversely, higher expenditures due to additional costs related to transportation and obtaining care (Benbrahim et al., 2015). Understanding the impact of residence setting is essential to identify potential inequalities in health expenditures among vulnerable elderly individuals in Morocco.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eHypothesis H3: Annual health expenditures of vulnerable households differ by the gender of the household head.\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThus, we posit that the gender of the household head can influence budget priorities and the allocation of resources to healthcare. Households headed by women tend to allocate a larger share of their budget to healthcare, partly due to responsibilities related to caring for children and elderly family members (Globalization and Health, 2021). Moreover, female household heads may face specific socio-economic challenges, such as lower incomes and limited access to resources, which can affect their health expenditures (Arber \u0026amp; Ginn, 1991). It is therefore relevant to examine whether the gender of the household head influences health expenditures within vulnerable households.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eHypothesis H4: Annual health expenditures of vulnerable households differ by household size.\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eAccordingly, we propose that household size is an important factor that can affect health expenditures. Studies show that larger households have lower per capita health expenditures due to the dilution effect of fixed costs but higher total costs due to the increased number of people needing care (Xu, Lazar, \u0026amp; Ruger, 2021). This can place additional financial pressure on vulnerable households, making it essential to examine the impact of household size on health expenditures.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eHypothesis H5: Annual health expenditures of vulnerable households differ by the profession of the household head.\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eIn light of this, we suggest that the profession of the household head is an indicator of socio-economic status and can influence available income, access to health insurance, and exposure to occupational risks. Households whose heads occupy precarious or low-income jobs allocate a larger share of their budget to healthcare due to the absence of social benefits such as health insurance (Fisher \u0026amp; Ryan, 2018). For example, high-risk professions like construction and agriculture are associated with higher health costs due to frequent occupational injuries and illnesses (Work, Aging and Retirement, 2018). It is therefore crucial to explore how the profession of the household head affects health expenditures.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eHypothesis H6: Annual health expenditures of vulnerable households differ by the education level of the household head.\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe education level of the household head plays a determining role in health behaviors. Individuals with higher education levels better understand the importance of preventive care and use health services more effectively, which can influence health expenditures (Stansfeld, Marmot, \u0026amp; Wilkinson, 2006). A study showed that education is associated with increased use of health services and higher costs but also with better overall health (Globalization and Health, 2021). It is therefore pertinent to examine the impact of education level on health expenditures of vulnerable households.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eMethodological Framework\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eMethods and Measurement Tools\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThis research is based on data from the National Household Consumption and Expenditure Survey (ENCDM) conducted by the High Commission for Planning (HCP) in 2014. Data were collected through standardized questionnaires in Arabic and French, administered individually to household heads at their homes. The questionnaires included detailed information on health expenditures, socio-economic characteristics of the household and household head, and other variables relevant to the study.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eSample\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe study population includes household heads aged 60 and over, identified during the 2013/2014 ENCDM. The national sample includes 7,190,456 households, representing 33,579,281 people. For this study, we focused on vulnerable household heads aged 60 and over, which represents a sample of 122,219 households.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eStatistical Tests Used\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eTo test these hypotheses, we used appropriate statistical tests, conducted using IBM SPSS Statistics 28 software:\u003c/p\u003e\n\u003cp\u003eOne-way Analysis of Variance (ANOVA): Used to compare annual health expenditures between multiple groups (more than two), particularly according to the 12 regions, household size, profession of the household head, and education level. ANOVA allows us to determine whether there are significant differences between group means.\u003c/p\u003e\n\u003cp\u003eIndependent Samples t-tests: Used to compare annual health expenditures between two independent groups, notably according to residence setting (urban/rural) and the gender of the household head. This test evaluates whether the observed differences between the means of the two groups are statistically significant.\u003c/p\u003e\n\u003cp\u003eWe analyzed the relationships between health expenditures (in Moroccan dirhams) of this population and various variables, including:\u003c/p\u003e\n\u003cp\u003eGender of the Household Head: Male or female.\u003c/p\u003e\n\u003cp\u003eRegion: The 12 administrative regions of Morocco, allowing examination of regional disparities.\u003c/p\u003e\n\u003cp\u003eResidence Setting: Urban or rural.\u003c/p\u003e\n\u003cp\u003eProfessional Activity: Classification of the household head according to profession, enabling assessment of the impact of profession on health expenditures.\u003c/p\u003e\n\u003cp\u003eEducation Level: Educational attainment of the household head, to analyze the influence of education on health expenditures.\u003c/p\u003e\n\u003cp\u003eHousehold Size: Number of people living in the household, to study the effect of family composition on health expenditures.\u003c/p\u003e\n\u003cp\u003eThis approach allows us to thoroughly explore how these socio-economic variables influence the annual health expenditures of vulnerable elderly individuals in Morocco\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eTable 1.\u0026nbsp;\u003c/strong\u003eDescriptive Statistics of the Study Population\u003c/p\u003e\n\u003ctable border=\"1\" cellpadding=\"0\"\u003e\n \u003cthead\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003eResidence Area\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003eFrequency\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003ePercent\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003eValid Percent\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003eCumulative Percent\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/thead\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003eUrban\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e51,685\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e42.3%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e42.3%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e42.3%\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003eRural\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e70,534\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e57.7%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e57.7%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e100.0%\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003eTotal\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e122,219\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e100.0%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e100.0%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e100.0%\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n\u003c/table\u003e\n\u003cp\u003e\u0026nbsp;\u003c/p\u003e\n\u003ctable border=\"1\" cellpadding=\"0\"\u003e\n \u003cthead\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003eRegions\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003eFrequency\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003ePercent\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003eValid Percent\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003eCumulative Percent\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/thead\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003eTanger-T\u0026eacute;touan-Al Hoce\u0026iuml;ma\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e6,754\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e5.5%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e5.5%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e5.5%\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003eOriental\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e4,760\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e3.9%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e3.9%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e9.4%\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003eF\u0026egrave;s-Mekn\u0026egrave;s\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e20,364\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e16.7%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e16.7%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e26.1%\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003eRabat-Sal\u0026eacute;-K\u0026eacute;nitra\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e12,540\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e10.3%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e10.3%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e36.3%\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003eB\u0026eacute;ni Mellal-Kh\u0026eacute;nifra\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e14,526\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e11.9%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e11.9%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e48.2%\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003eCasablanca-Settat\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e22,988\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e18.8%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e18.8%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e67.0%\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003eMarrakech-Safi\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e15,278\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e12.5%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e12.5%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e79.5%\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003eDr\u0026acirc;a-Tafilalet\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e6,199\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e5.1%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e5.1%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e84.6%\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003eSouss-Massa\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e17,030\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e13.9%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e13.9%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e98.5%\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003eGuelmim-Oued Noun\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e1,749\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e1.4%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e1.4%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e100.0%\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003eDakhla-Oued Ed Dahab\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e31\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e0.0%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e0.0%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e100.0%\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003eTotal\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e122,219\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e100.0%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e100.0%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e100.0%\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n\u003c/table\u003e\n\u003cp\u003e\u0026nbsp;\u003c/p\u003e\n\u003ctable border=\"1\" cellpadding=\"0\"\u003e\n \u003cthead\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003eGender of Household Head\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003eFrequency\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003ePercent\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003eValid Percent\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003eCumulative Percent\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/thead\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003eMale\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e99,974\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e81.8%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e81.8%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e81.8%\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003eFemale\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e22,245\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e18.2%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e18.2%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e100.0%\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003eTotal\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e122,219\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e100.0%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e100.0%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e100.0%\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n\u003c/table\u003e\n\u003cp\u003e\u0026nbsp;\u003c/p\u003e\n\u003ctable border=\"1\" cellpadding=\"0\"\u003e\n \u003cthead\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003eMarital Status of Household Head\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003eFrequency\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003ePercent\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003eValid Percent\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003eCumulative Percent\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/thead\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003eMarried\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e99,093\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e81.1%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e81.1%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e81.1%\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003eWidowed\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e23,126\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e18.9%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e18.9%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e100.0%\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003eTotal\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e122,219\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e100.0%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e100.0%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e100.0%\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n\u003c/table\u003e\n\u003cp\u003e\u0026nbsp;\u003c/p\u003e\n\u003ctable border=\"1\" cellpadding=\"0\"\u003e\n \u003cthead\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003eSector of Usual Activity During the Last 12 Months\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003eFrequency\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003ePercent\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003eValid Percent\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003eCumulative Percent\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/thead\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003eAgriculture, Forestry, and Fishing\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e37,655\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e30.8%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e52.9%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e52.9%\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003eIndustry\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e5,663\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e4.6%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e8.0%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e60.9%\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003eConstruction and Public Works\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e6,021\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e4.9%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e8.5%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e69.3%\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003eCommerce\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e9,869\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e8.1%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e13.9%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e83.2%\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003eServices\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e9,947\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e8.1%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e14.0%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e97.2%\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003eNot Declared\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e1,997\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e1.6%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e2.8%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e100.0%\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003eTotal\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e71,152\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e58.2%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e100.0%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e100.0%\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003eMissing\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003eSystem\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e51,067\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e41.8%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003eTotal\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e122,219\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e100.0%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd\u003e\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n\u003c/table\u003e\n\u003cp\u003e\u0026nbsp;\u003c/p\u003e\n\u003ctable border=\"1\" cellpadding=\"0\"\u003e\n \u003cthead\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 205px;\"\u003e\n \u003cp\u003eDescriptive Statistics\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 7px;\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd style=\"width: 63px;\"\u003e\n \u003cp\u003eN\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 72px;\"\u003e\n \u003cp\u003eMinimum\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 74px;\"\u003e\n \u003cp\u003eMaximum\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 75px;\"\u003e\n \u003cp\u003eMean\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 91px;\"\u003e\n \u003cp\u003eStd. Deviation\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/thead\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 205px;\"\u003e\n \u003cp\u003eAge (in 5-year intervals) of Household Head\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 7px;\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd style=\"width: 63px;\"\u003e\n \u003cp\u003e122,219\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 72px;\"\u003e\n \u003cp\u003e13\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 74px;\"\u003e\n \u003cp\u003e16\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 75px;\"\u003e\n \u003cp\u003e14.19\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 91px;\"\u003e\n \u003cp\u003e1.136\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 205px;\"\u003e\n \u003cp\u003eAnnual Healthcare Expenditure per Household (in MAD)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 7px;\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd style=\"width: 63px;\"\u003e\n \u003cp\u003e122,219\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 72px;\"\u003e\n \u003cp\u003e24.00\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 74px;\"\u003e\n \u003cp\u003e30,650.00\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 75px;\"\u003e\n \u003cp\u003e2,807.6483\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 91px;\"\u003e\n \u003cp\u003e3,983.09708\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 205px;\"\u003e\n \u003cp\u003eHousehold Size\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 7px;\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd style=\"width: 63px;\"\u003e\n \u003cp\u003e122,219\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 72px;\"\u003e\n \u003cp\u003e1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 74px;\"\u003e\n \u003cp\u003e6\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 75px;\"\u003e\n \u003cp\u003e5.63\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 91px;\"\u003e\n \u003cp\u003e0.787\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 205px;\"\u003e\n \u003cp\u003eValid N (listwise)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 7px;\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd style=\"width: 63px;\"\u003e\n \u003cp\u003e122,219\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 72px;\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd style=\"width: 74px;\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd style=\"width: 75px;\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd style=\"width: 91px;\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n\u003c/table\u003e\n\u003cp\u003eSource: Prepared by the authors based on data from the HCP-ENCDM survey (2014).\u003c/p\u003e"},{"header":"Results","content":"\u003cp\u003eThis section presents the results of the statistical analyses conducted to test the hypotheses related to healthcare expenditures of vulnerable older adult household heads in Morocco. Each hypothesis is tested using appropriate statistical methods, and the results are interpreted accordingly.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eH1: The annual healthcare expenditures of vulnerable households differ across the 12 regions of Morocco\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThis section presents the results of the statistical analyses conducted to test the hypotheses related to healthcare expenditures of vulnerable older adult household heads in Morocco. The results include ANOVA tests followed by post hoc tests for multiple comparisons, taking into account the assumption of variance homogeneity.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eHypotheses:\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eH0: There is no significant difference in annual healthcare expenditures between regions.\u003c/p\u003e\n\u003cp\u003eH1: There is a significant difference in annual healthcare expenditures between regions.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eTable 2.\u0026nbsp;\u003c/strong\u003eTest of Homogeneity of Variances\u003c/p\u003e\n\u003ctable border=\"1\" cellpadding=\"0\"\u003e\n \u003cthead\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003eMethod\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003eLevene Statistic\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003edf1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003edf2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003eSig.\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/thead\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003eBased on Mean\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e1116.633\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e10\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e122,208\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e.000\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003eBased on Median\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e639.421\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e10\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e122,208\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e.000\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003eBased on Median and with adjusted df\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e639.421\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e10\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e72,026.502\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e.000\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003eBased on trimmed mean\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e787.721\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e10\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e122,208\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e.000\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n\u003c/table\u003e\n\u003cp\u003eSource: Prepared by the authors based on data from the HCP-ENCDM survey (2014).\u003c/p\u003e\n\u003cp\u003eThe hypothesis of homogeneity of variances is rejected (p \u0026lt; 0.05). Therefore, we use the Games-Howell post hoc test for multiple comparisons.\u003c/p\u003e\n\u003cp\u003eStatistical Interpretation Analysis of Variance (ANOVA)\u003c/p\u003e\n\u003cp\u003eAn analysis of variance (ANOVA) was conducted to examine differences in annual healthcare expenditures across the 12 regions of Morocco. The ANOVA results revealed a significant difference between regions (F(10, 122,208) = 540.973, p \u0026lt; .001). Descriptive statistics showed that average healthcare expenditures varied considerably from one region to another.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eTable 3.\u0026nbsp;\u003c/strong\u003eANOVA Results for Regional Differences in Healthcare Expenditures\u003c/p\u003e\n\u003ctable border=\"1\" cellpadding=\"0\"\u003e\n \u003cthead\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003eSource of Variation\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003eSum of Squares\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003edf\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003eMean Square\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003eF\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003eSig.\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/thead\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003eBetween Groups\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e82,194,312,609.081\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e10\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e8,219,431,260.908\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e540.973\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e.000\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003eWithin Groups\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e1,856,801,880,619.078\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e122,208\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e15,193,783.391\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd\u003e\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003eTotal\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e1,938,996,193,228.158\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e122,218\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd\u003e\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n\u003c/table\u003e\n\u003cp\u003eSource: Prepared by the authors based on data from the HCP-ENCDM survey (2014).\u003c/p\u003e\n\u003cp\u003eThese results indicate that annual healthcare expenditures vary significantly across regions in Morocco.\u003c/p\u003e\n\u003cp\u003eMultiple Comparisons: Games-Howell Test\u003c/p\u003e\n\u003cp\u003eTo identify the specific regions where healthcare expenditures differ, a Games-Howell post hoc test was conducted. The results show that several regions exhibit significant differences in annual healthcare expenditures.\u003c/p\u003e\n\u003cp\u003eStatistical Interpretation of the Post Hoc Test (Games-Howell)\u003c/p\u003e\n\u003cp\u003eTo analyze specific differences between regions in terms of annual healthcare expenditures, a Games-Howell post hoc test was performed. This test is particularly appropriate due to the unequal variances between groups. The results revealed several significant differences:\u003c/p\u003e\n\u003cp\u003eTanger-T\u0026eacute;touan-Al Hoce\u0026iuml;ma:\u003c/p\u003e\n\u003cp\u003eSignificantly higher expenditures than in the Oriental and Marrakech-Safi regions.\u003c/p\u003e\n\u003cp\u003eSignificantly lower expenditures than in the F\u0026egrave;s-Mekn\u0026egrave;s, Rabat-Sal\u0026eacute;-K\u0026eacute;nitra, B\u0026eacute;ni Mellal-Kh\u0026eacute;nifra, Dr\u0026acirc;a-Tafilalet, Souss-Massa, Guelmim-Oued Noun, and Dakhla-Oued Ed Dahab regions.\u003c/p\u003e\n\u003cp\u003eOriental:\u003c/p\u003e\n\u003cp\u003eSignificantly lower expenditures than in the F\u0026egrave;s-Mekn\u0026egrave;s, Dr\u0026acirc;a-Tafilalet, Souss-Massa, and Dakhla-Oued Ed Dahab regions.\u003c/p\u003e\n\u003cp\u003eSignificantly higher expenditures than in the Marrakech-Safi and Casablanca-Settat regions.\u003c/p\u003e\n\u003cp\u003eF\u0026egrave;s-Mekn\u0026egrave;s:\u003c/p\u003e\n\u003cp\u003eSignificantly higher expenditures than all other regions except Dakhla-Oued Ed Dahab.\u003c/p\u003e\n\u003cp\u003eSignificantly lower expenditures than Dakhla-Oued Ed Dahab.\u003c/p\u003e\n\u003cp\u003eRabat-Sal\u0026eacute;-K\u0026eacute;nitra:\u003c/p\u003e\n\u003cp\u003eSignificantly higher expenditures than Marrakech-Safi and Casablanca-Settat.\u003c/p\u003e\n\u003cp\u003eSignificantly lower expenditures than F\u0026egrave;s-Mekn\u0026egrave;s, Dr\u0026acirc;a-Tafilalet, Souss-Massa, and Dakhla-Oued Ed Dahab.\u003c/p\u003e\n\u003cp\u003eB\u0026eacute;ni Mellal-Kh\u0026eacute;nifra:\u003c/p\u003e\n\u003cp\u003eSignificantly higher expenditures than Casablanca-Settat, Marrakech-Safi, Guelmim-Oued Noun, and Dakhla-Oued Ed Dahab.\u003c/p\u003e\n\u003cp\u003eSignificantly lower expenditures than F\u0026egrave;s-Mekn\u0026egrave;s, Rabat-Sal\u0026eacute;-K\u0026eacute;nitra, Dr\u0026acirc;a-Tafilalet, and Souss-Massa.\u003c/p\u003e\n\u003cp\u003eCasablanca-Settat:\u003c/p\u003e\n\u003cp\u003eSignificantly lower expenditures than Oriental, Rabat-Sal\u0026eacute;-K\u0026eacute;nitra, B\u0026eacute;ni Mellal-Kh\u0026eacute;nifra, Dr\u0026acirc;a-Tafilalet, Souss-Massa, Guelmim-Oued Noun, and Dakhla-Oued Ed Dahab.\u003c/p\u003e\n\u003cp\u003eSimilar expenditures to Tanger-T\u0026eacute;touan-Al Hoce\u0026iuml;ma.\u003c/p\u003e\n\u003cp\u003eMarrakech-Safi:\u003c/p\u003e\n\u003cp\u003eSignificantly lower expenditures than all other regions except Casablanca-Settat.\u003c/p\u003e\n\u003cp\u003eDr\u0026acirc;a-Tafilalet:\u003c/p\u003e\n\u003cp\u003eSignificantly higher expenditures than all other regions except Souss-Massa and Dakhla-Oued Ed Dahab.\u003c/p\u003e\n\u003cp\u003eSouss-Massa:\u003c/p\u003e\n\u003cp\u003eSignificantly higher expenditures than all other regions except Dakhla-Oued Ed Dahab.\u003c/p\u003e\n\u003cp\u003eGuelmim-Oued Noun:\u003c/p\u003e\n\u003cp\u003eSignificantly lower expenditures than F\u0026egrave;s-Mekn\u0026egrave;s, Rabat-Sal\u0026eacute;-K\u0026eacute;nitra, B\u0026eacute;ni Mellal-Kh\u0026eacute;nifra, Dr\u0026acirc;a-Tafilalet, Souss-Massa, and Dakhla-Oued Ed Dahab.\u003c/p\u003e\n\u003cp\u003eSimilar expenditures to Casablanca-Settat and Tanger-T\u0026eacute;touan-Al Hoce\u0026iuml;ma.\u003c/p\u003e\n\u003cp\u003eDakhla-Oued Ed Dahab:\u003c/p\u003e\n\u003cp\u003eSignificantly higher expenditures than all other regions.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eSummary of Results\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe results of the post hoc test indicate significant differences in healthcare expenditures between several regions, highlighting substantial heterogeneity in the healthcare spending of vulnerable older adult households in Morocco. Regions such as Dakhla-Oued Ed Dahab, Souss-Massa, and Dr\u0026acirc;a-Tafilalet exhibit higher healthcare expenditures, while regions like Marrakech-Safi and Casablanca-Settat show lower expenditures. These disparities could be attributed to various factors, such as differences in access to healthcare services, medical infrastructure, and regional health policies.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eEconomic Conclusion of the Hypothesis\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe results of the ANOVA and Games-Howell post hoc tests reveal significant disparities in annual healthcare expenditures across different regions of Morocco, highlighting crucial economic implications for public health policies and resource allocation. Regions such as Dakhla-Oued Ed Dahab, Souss-Massa, and Dr\u0026acirc;a-Tafilalet\u0026nbsp;show significantly higher healthcare expenditures, which could indicate better availability and utilization of healthcare services in these areas but also potentially higher healthcare costs. Conversely, regions like Marrakech-Safi and Casablanca-Settat show lower expenditures, which could reflect limited access or lower utilization of healthcare services, signaling specific intervention needs.\u003c/p\u003e\n\u003cp\u003eThese marked differences underline the importance of adopting a regionally tailored approach in public health policies. Regions with lower expenditures could benefit from programs aimed at improving access to healthcare, enhancing medical infrastructure, and raising public awareness about the importance of preventive healthcare. Additionally, the findings suggest that public health resources need to be reevaluated and potentially reallocated to reduce regional disparities. For instance, regions with higher healthcare expenditures may require additional funding to maintain the quality of care, while regions with lower expenditures may need increased investments to improve access and availability of healthcare services.\u003c/p\u003e\n\u003cp\u003eThe variation in healthcare expenditures may also reflect broader economic disparities between regions. Regions with higher healthcare expenditures may correspond to areas with higher income levels, allowing for a greater allocation of funds for healthcare. Conversely, regions with lower expenditures may signal a population with limited purchasing power, necessitating economic assistance and subsidies to improve living conditions and access to healthcare.\u003c/p\u003e\n\u003cp\u003eGiven these findings, it is recommended to strengthen healthcare infrastructure in underserved regions, introduce targeted subsidies to reduce healthcare costs in economically disadvantaged areas, and implement awareness programs aimed at encouraging the use of healthcare services, particularly in rural and urban areas with lower expenditures. It is also crucial to continuously assess healthcare expenditures to adjust policies according to evolving regional needs, ensuring a flexible and effective response to the specific health challenges of each region.\u003c/p\u003e\n\u003cp\u003eIn conclusion, the regional disparities in healthcare expenditures of vulnerable older adult household heads in Morocco require particular attention to ensure equitable resource distribution and improved access to healthcare for all. These results should guide policymakers in developing effective strategies to reduce health inequalities and promote overall well-being on a national scale. The ultimate goal is to create a healthcare system that not only meets the immediate needs of different regions but also proactively adapts to emerging challenges, ensuring equitable and quality healthcare access for every Moroccan citizen.\u003c/p\u003e\n\u003cp\u003eH2: The annual healthcare expenditures of vulnerable households differ by place of residence (rural vs. urban)\u003c/p\u003e\n\u003cp\u003eStatistical Interpretation (Hypothesis H2)\u003c/p\u003e\n\u003cp\u003eHypothesis H2: The annual healthcare expenditures of vulnerable households differ by place of residence (rural vs. urban).\u003c/p\u003e\n\u003cp\u003eHypotheses:\u003c/p\u003e\n\u003cp\u003eH0: There is no significant difference in annual healthcare expenditures between rural and urban residences.\u003c/p\u003e\n\u003cp\u003eH1: There is a significant difference in annual healthcare expenditures between rural and urban residences.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eTable 4.\u0026nbsp;\u003c/strong\u003eDescriptive Statistics\u003c/p\u003e\n\u003ctable border=\"1\" cellpadding=\"0\"\u003e\n \u003cthead\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003eResidence Area\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003eN\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003eMean\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003eStandard Deviation\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003eStandard Error of the Mean\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/thead\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003eUrban\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e51,685\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e2,907.4273\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e4,636.55762\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e20.39452\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003eRural\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e70,534\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e2,734.5334\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e3,424.21961\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e12.89325\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n\u003c/table\u003e\n\u003cp\u003eSource: Prepared by the authors based on data from the HCP-ENCDM survey (2014).\u003c/p\u003e\n\u003cp\u003eThe descriptive statistics show that households in urban areas spend an average of 2,907.43 MAD per year on medical care, while those in rural areas spend an average of 2,734.53 MAD per year.\u003c/p\u003e\n\u003cp\u003eIndependent Samples t-Test\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eTable 5.\u0026nbsp;\u003c/strong\u003eTest for Equality of Variances\u003c/p\u003e\n\u003ctable border=\"1\" cellpadding=\"0\"\u003e\n \u003cthead\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003eLevene\u0026apos;s Test for Equality of Variances\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003eF\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003eSig.\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/thead\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003eEqual variances assumed\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e356.140\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e.000\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n\u003c/table\u003e\n\u003cp\u003eSource: Prepared by the authors based on data from the HCP-ENCDM survey (2014).\u003c/p\u003e\n\u003cp\u003eThe hypothesis of homogeneity of variances is rejected (p \u0026lt; .001), indicating that the variances of the two groups are not equal.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eTable 6.\u0026nbsp;\u003c/strong\u003et-Test for Equality of Means\u003c/p\u003e\n\u003ctable border=\"1\" cellpadding=\"0\"\u003e\n \u003cthead\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003et\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003edf\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003eSig. (2-tailed)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003eMean Difference\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003eStandard Error Difference\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e95% CI Lower\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e95% CI Upper\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/thead\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003eEqual variances assumed\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e7.498\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e122,217\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e.000\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e172.89387\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e23.05742\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e127.70174\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003eEqual variances not assumed\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e7.166\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e90,642.938\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e.000\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e172.89387\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e24.12824\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e125.60277\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n\u003c/table\u003e\n\u003cp\u003eSource: Prepared by the authors based on data from the HCP-ENCDM survey (2014).\u003c/p\u003e\n\u003cp\u003eThe t-test shows a significant difference in healthcare expenditures between urban and rural areas (t(122,217) = 7.498, p \u0026lt; .001 for equal variances; t(90,642.938) = 7.166, p \u0026lt; .001 for unequal variances). The mean difference in annual expenditures between the two groups is 172.89 MAD, with a 95% confidence interval ranging from 127.70 to 218.09 MAD.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eTable 7.\u0026nbsp;\u003c/strong\u003eEffect Size\u003c/p\u003e\n\u003ctable border=\"1\" cellpadding=\"0\"\u003e\n \u003cthead\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003eEffect Size\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003eEstimate\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e95% CI Lower\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e95% CI Upper\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/thead\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003eCohen\u0026apos;s d\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e.043\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e.032\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e.055\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003eHedges\u0026apos; correction\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e.043\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e.032\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e.055\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003eGlass\u0026apos;s delta\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e.050\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e.039\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e.062\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n\u003c/table\u003e\n\u003cp\u003eSource: Prepared by the authors based on data from the HCP-ENCDM survey (2014).\u003c/p\u003e\n\u003cp\u003eThe effect size, measured by Cohen\u0026apos;s d, is 0.043, indicating a small difference between the two groups.\u003c/p\u003e\n\u003cp\u003eEconomic Conclusion\u003c/p\u003e\n\u003cp\u003eThe t-test results show a statistically significant difference in annual healthcare expenditures between urban and rural households, with urban households spending an average of 172.89 MAD more than rural households on medical care. Although the effect size is small, this difference may have important economic implications. Urban households appear to benefit from better access to healthcare services, resulting in higher expenditures, while rural households, despite spending less, may face limited access to quality healthcare services.\u003c/p\u003e\n\u003cp\u003eTo address this situation, it is essential to improve healthcare infrastructure in rural areas to reduce this expenditure gap and ensure equitable access to healthcare for all citizens. Targeted subsidies for rural households could also be considered to offset the additional costs associated with limited access and the quality of healthcare services. Additionally, the implementation of awareness and health education programs in rural areas could encourage greater use of preventive healthcare services, ultimately contributing to balancing expenditures between urban and rural areas.\u003c/p\u003e\n\u003cp\u003eIn summary, the observed differences in healthcare expenditures between urban and rural areas highlight the need for an integrated policy approach aimed at improving access to healthcare and reducing regional health disparities in Morocco.\u003c/p\u003e\n\u003cp\u003eH3: The annual healthcare expenditures of vulnerable households differ by gender\u003c/p\u003e\n\u003cp\u003eStatistical Interpretation (Hypothesis H3)\u003c/p\u003e\n\u003cp\u003eHypothesis H3: The annual healthcare expenditures of vulnerable households differ by the gender of the household head.\u003c/p\u003e\n\u003cp\u003eHypotheses:\u003c/p\u003e\n\u003cp\u003eH0: There is no significant difference in annual healthcare expenditures between male and female household heads.\u003c/p\u003e\n\u003cp\u003eH1: There is a significant difference in annual healthcare expenditures between male and female household heads.\u003c/p\u003e\n\u003cp\u003e\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eTable 8.\u0026nbsp;\u003c/strong\u003eDescriptive Statistics\u003c/p\u003e\n\u003ctable border=\"1\" cellpadding=\"0\"\u003e\n \u003cthead\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003eGender of Household Head\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003eN\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003eMean\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003eStandard Deviation\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003eStandard Error of the Mean\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/thead\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003eMale\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e99,974\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e2,799.2360\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e3,769.88046\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e11.92296\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003eFemale\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e22,245\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e2,845.4550\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e4,826.33663\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e32.35947\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n\u003c/table\u003e\n\u003cp\u003eSource: Prepared by the authors based on data from the HCP-ENCDM survey (2014).\u003c/p\u003e\n\u003cp\u003eThe descriptive statistics show that households headed by men spend an average of 2,799.24 MAD per year on medical care, while those headed by women spend an average of 2,845.46 MAD per year.\u003c/p\u003e\n\u003cp\u003eIndependent Samples t-Test\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eTable 9.\u0026nbsp;\u003c/strong\u003eTest for Equality of Variances\u003c/p\u003e\n\u003ctable border=\"1\" cellpadding=\"0\"\u003e\n \u003cthead\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003eLevene\u0026apos;s Test for Equality of Variances\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003eF\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003eSig.\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/thead\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003eEqual variances assumed\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e508.870\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e.000\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n\u003c/table\u003e\n\u003cp\u003eSource: Prepared by the authors based on data from the HCP-ENCDM survey (2014).\u003c/p\u003e\n\u003cp\u003eThe hypothesis of homogeneity of variances is rejected (p \u0026lt; .001), indicating that the variances of the two groups are not equal.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eTable 10.\u0026nbsp;\u003c/strong\u003et-Test for Equality of Means\u003c/p\u003e\n\u003ctable border=\"1\" cellpadding=\"0\" width=\"629\"\u003e\n \u003cthead\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003et\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003edf\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003eSig. (2-tailed)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003eMean Difference\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003eStandard Error Difference\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e95% CI Lower\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e95% CI Upper\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/thead\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003eEqual variances assumed\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e-1.565\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e122,217\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e.118\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e-46.21901\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e29.52759\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e-104.09257\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003eEqual variances not assumed\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e-1.340\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e28,576.378\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e.180\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e-46.21901\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e34.48612\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e-113.81340\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n\u003c/table\u003e\n\u003cp\u003eSource: Prepared by the authors based on data from the HCP-ENCDM survey (2014).\u003c/p\u003e\n\u003cp\u003eThe t-test shows that the difference in healthcare expenditures between households headed by men and women is not statistically significant (t(122,217) = -1.565, p = .118 for equal variances; t(28,576.378) = -1.340, p = .180 for unequal variances). The mean difference in annual expenditures between the two groups is -46.22 MAD, with a 95% confidence interval ranging from -104.09 to 11.65 MAD for equal variances and from -113.81 to 21.38 MAD for unequal variances.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eTable 11:\u0026nbsp;\u003c/strong\u003eEffect Size\u003c/p\u003e\n\u003ctable border=\"1\" cellpadding=\"0\"\u003e\n \u003cthead\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003eEffect Size\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003eEstimate\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e95% CI Lower\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e95% CI Upper\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/thead\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003eCohen\u0026apos;s d\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e-.012\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e-.026\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e.003\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003eHedges\u0026apos; correction\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e-.012\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e-.026\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e.003\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003eGlass\u0026apos;s delta\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e-.010\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e-.024\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e.005\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n\u003c/table\u003e\n\u003cp\u003eSource: Prepared by the authors based on data from the HCP-ENCDM survey (2014).\u003c/p\u003e\n\u003cp\u003eThe effect size, measured by Cohen\u0026apos;s d, is -0.012, indicating a negligible difference between the two groups.\u003c/p\u003e\n\u003cp\u003eEconomic Conclusion\u003c/p\u003e\n\u003cp\u003eThe t-test results reveal that there is no statistically significant difference in annual healthcare expenditures between male and female household heads. Although female household heads have slightly higher average expenditures, this difference is not statistically significant. This suggests that the gender of the household head does not have a substantial impact on healthcare expenditures, which may indicate relative equality in access to healthcare services for male and female household heads.\u003c/p\u003e\n\u003cp\u003eConsequently, health policies can legitimately continue to focus on other factors that influence healthcare expenditures, such as residence area and socio-economic status. However, it remains crucial to maintain inclusive and non-discriminatory health awareness and education programs to ensure that all household heads, regardless of gender, have equitable access to the necessary information and services. Additionally, financial support policies should remain fair and accessible to all household heads, ensuring that female household heads do not encounter additional barriers in accessing healthcare.\u003c/p\u003e\n\u003cp\u003eIn conclusion, these results show relative equity in healthcare expenditures between male and female household heads, which is encouraging for initiatives aimed at promoting gender equality in healthcare access in Morocco.\u003c/p\u003e\n\u003cp\u003eH4: The annual healthcare expenditures of vulnerable households differ by household size\u003c/p\u003e\n\u003cp\u003eStatistical Interpretation (Hypothesis H4)\u003c/p\u003e\n\u003cp\u003eHypothesis H4: The annual healthcare expenditures of vulnerable households differ by household size.\u003c/p\u003e\n\u003cp\u003eHypotheses:\u003c/p\u003e\n\u003cp\u003eH0: There is no significant difference in annual healthcare expenditures by household size.\u003c/p\u003e\n\u003cp\u003eH1: There is a significant difference in annual healthcare expenditures by household size.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eTable 12:\u0026nbsp;\u003c/strong\u003eTest of Homogeneity of Variances\u003c/p\u003e\n\u003ctable border=\"1\" cellpadding=\"0\"\u003e\n \u003cthead\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003eTest of Homogeneity of Variances\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003eLevene Statistic\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003edf1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003edf2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003eSig.\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/thead\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003eHealthcare Expenditure per Household (in MAD)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd\u003e\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003eBased on Mean\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e556.479\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e5\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e122,213\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e.000\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003eBased on Median\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e337.538\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e5\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e122,213\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e.000\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003eBased on Median and with adjusted df\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e337.538\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e5\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e105,692.827\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e.000\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003eBased on trimmed mean\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e393.173\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e5\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e122,213\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e.000\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n\u003c/table\u003e\n\u003cp\u003eSource: Prepared by the authors based on data from the HCP-ENCDM survey (2014).\u003c/p\u003e\n\u003cp\u003eThe hypothesis of homogeneity of variances is rejected (p \u0026lt; .001), indicating that the variances between groups are not equal.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eTable 13.\u0026nbsp;\u003c/strong\u003eANOVA \u003cstrong\u003e\u0026nbsp;\u0026nbsp;\u003c/strong\u003eResults for difference healthcare expenditures \u0026nbsp;by household size\u0026nbsp;\u003c/p\u003e\n\u003ctable border=\"1\" cellpadding=\"0\"\u003e\n \u003cthead\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003eSource of Variation\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003eSum of Squares\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003edf\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003eMean Square\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003eF\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003eSig.\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/thead\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003eBetween Groups\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e27,204,444,285.084\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e5\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e5,440,888,857.017\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e347.814\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e.000\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003eWithin Groups\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e1,911,791,748,943.075\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e122,213\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e15,643,112.835\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd\u003e\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003eTotal\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e1,938,996,193,228.158\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e122,218\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd\u003e\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n\u003c/table\u003e\n\u003cp\u003eSource: Prepared by the authors based on data from the HCP-ENCDM survey (2014).\u003c/p\u003e\n\u003cp\u003eThe ANOVA shows a significant difference between household sizes (F(5, 122,213) = 347.814, p \u0026lt; .001).\u003c/p\u003e\n\u003cp\u003eMultiple Comparisons: Games-Howell Test\u003c/p\u003e\n\u003cp\u003eThe results of the Games-Howell post hoc test reveal several significant differences between household sizes:\u003c/p\u003e\n\u003cp\u003e1 person vs. 2 persons:\u003c/p\u003e\n\u003cp\u003eHouseholds with 1 person spend significantly less than those with 2 persons (mean difference = -1,899.00 MAD, p \u0026lt; .001).\u003c/p\u003e\n\u003cp\u003e1 person vs. 3 persons:\u003c/p\u003e\n\u003cp\u003eHouseholds with 1 person spend significantly less than those with 3 persons (mean difference = -1,368.20 MAD, p \u0026lt; .001).\u003c/p\u003e\n\u003cp\u003e1 person vs. 4 persons:\u003c/p\u003e\n\u003cp\u003eHouseholds with 1 person spend significantly less than those with 4 persons (mean difference = -1,853.77 MAD, p \u0026lt; .001).\u003c/p\u003e\n\u003cp\u003e1 person vs. 5 persons:\u003c/p\u003e\n\u003cp\u003eHouseholds with 1 person spend significantly less than those with 5 persons (mean difference = -1,479.80 MAD, p \u0026lt; .001).\u003c/p\u003e\n\u003cp\u003e1 person vs. 6 persons and more:\u003c/p\u003e\n\u003cp\u003eHouseholds with 1 person spend significantly less than those with 6 persons and more (mean difference = -2,666.75 MAD, p \u0026lt; .001).\u003c/p\u003e\n\u003cp\u003e2 persons vs. 3 persons:\u003c/p\u003e\n\u003cp\u003eHouseholds with 2 persons spend significantly more than those with 3 persons (mean difference = 530.80 MAD, p \u0026lt; .001).\u003c/p\u003e\n\u003cp\u003e2 persons vs. 4 persons:\u003c/p\u003e\n\u003cp\u003eNo significant difference between households with 2 and 4 persons.\u003c/p\u003e\n\u003cp\u003e2 persons vs. 5 persons:\u003c/p\u003e\n\u003cp\u003eHouseholds with 2 persons spend significantly less than those with 5 persons (mean difference = 419.20 MAD, p \u0026lt; .001).\u003c/p\u003e\n\u003cp\u003e2 persons vs. 6 persons and more:\u003c/p\u003e\n\u003cp\u003eHouseholds with 2 persons spend significantly less than those with 6 persons and more (mean difference = -767.75 MAD, p \u0026lt; .001).\u003c/p\u003e\n\u003cp\u003e3 persons vs. 4 persons:\u003c/p\u003e\n\u003cp\u003eHouseholds with 3 persons spend significantly less than those with 4 persons (mean difference = -485.57 MAD, p \u0026lt; .001).\u003c/p\u003e\n\u003cp\u003e3 persons vs. 5 persons:\u003c/p\u003e\n\u003cp\u003eNo significant difference between households with 3 and 5 persons.\u003c/p\u003e\n\u003cp\u003e3 persons vs. 6 persons and more:\u003c/p\u003e\n\u003cp\u003eHouseholds with 3 persons spend significantly less than those with 6 persons and more (mean difference = -1,298.56 MAD, p \u0026lt; .001).\u003c/p\u003e\n\u003cp\u003e4 persons vs. 5 persons:\u003c/p\u003e\n\u003cp\u003eHouseholds with 4 persons spend significantly more than those with 5 persons (mean difference = 373.96 MAD, p \u0026lt; .001).\u003c/p\u003e\n\u003cp\u003e4 persons vs. 6 persons and more:\u003c/p\u003e\n\u003cp\u003eHouseholds with 4 persons spend significantly less than those with 6 persons and more (mean difference = -812.99 MAD, p \u0026lt; .001).\u003c/p\u003e\n\u003cp\u003e5 persons vs. 6 persons and more:\u003c/p\u003e\n\u003cp\u003eHouseholds with 5 persons spend significantly less than those with 6 persons and more (mean difference = -1,186.95 MAD, p \u0026lt; .001).\u003c/p\u003e\n\u003cp\u003eEconomic Conclusion\u003c/p\u003e\n\u003cp\u003eThe results of the ANOVA and Games-Howell post hoc tests reveal significant differences in annual healthcare expenditures based on household size, showing that larger households tend to spend more on healthcare compared to smaller households. This suggests that these households, due to their size, may require greater attention in terms of support policies and access to healthcare services.\u003c/p\u003e\n\u003cp\u003eTherefore, it is crucial that financial support programs are specifically targeted towards larger households to offset the additional costs associated with healthcare. Additionally, public health policies must integrate the dimension of household size when developing interventions and budget allocations. For larger households, preventive healthcare programs and community health services could be particularly beneficial, helping to reduce per capita costs.\u003c/p\u003e\n\u003cp\u003eIt is also important that health education and awareness campaigns are tailored to the needs of households of different sizes to ensure effective and equitable use of healthcare services. In summary, the observed differences in healthcare expenditures by household size highlight the importance of adapting health policies and financial interventions to meet the specific needs of larger households, ensuring equitable access and affordable healthcare for all.\u003c/p\u003e\n\u003cp\u003eH5: The annual healthcare expenditures of vulnerable households differ by the profession of the household head\u003c/p\u003e\n\u003cp\u003eStatistical Interpretation (Hypothesis H5)\u003c/p\u003e\n\u003cp\u003eHypothesis H5: The annual healthcare expenditures of vulnerable households differ by the profession of the household head.\u003c/p\u003e\n\u003cp\u003eHypotheses:\u003c/p\u003e\n\u003cp\u003eH0: There is no significant difference in annual healthcare expenditures by the profession of the household head.\u003c/p\u003e\n\u003cp\u003eH1: There is a significant difference in annual healthcare expenditures by the profession of the household head.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eTable 14.\u0026nbsp;\u003c/strong\u003eTest of Homogeneity of Variances\u003c/p\u003e\n\u003ctable border=\"1\" cellpadding=\"0\"\u003e\n \u003cthead\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003eTest of Homogeneity of Variances\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003eLevene Statistic\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003edf1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003edf2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003eSig.\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/thead\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003eHealthcare Expenditure per Household (in MAD)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd\u003e\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003eBased on Mean\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e285.481\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e5\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e122,213\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e.000\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003eBased on Median\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e244.569\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e5\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e122,213\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e.000\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003eBased on Median and with adjusted df\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e244.569\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e5\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e95,672.471\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e.000\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003eBased on trimmed mean\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e256.641\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e5\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e122,213\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e.000\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n\u003c/table\u003e\n\u003cp\u003eSource: Prepared by the authors based on data from the HCP-ENCDM survey (2014).\u003c/p\u003e\n\u003cp\u003eThe hypothesis of homogeneity of variances is rejected (p \u0026lt; .001), indicating that the variances between groups are not equal.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eTable 15.\u0026nbsp;\u003c/strong\u003eANOVA Results of annual healthcare expenditures of vulnerable households by the profession\u003c/p\u003e\n\u003ctable border=\"1\" cellpadding=\"0\"\u003e\n \u003cthead\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003eSource of Variation\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003eSum of Squares\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003edf\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003eMean Square\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003eF\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003eSig.\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/thead\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003eBetween Groups\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e1,734,828,276.784\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e5\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e346,965,655.357\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e20.371\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e.000\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003eWithin Groups\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e1,937,261,364,951.374\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e122,213\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e15,847,868.930\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd\u003e\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003eTotal\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e1,938,996,193,228.158\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e122,218\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd\u003e\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n\u003c/table\u003e\n\u003cp\u003eSource: Prepared by the authors based on data from the HCP-ENCDM survey (2014).\u003c/p\u003e\n\u003cp\u003eThe ANOVA shows a significant difference between the professions of the household heads (F(5, 122,213) = 20.371, p \u0026lt; .001).\u003c/p\u003e\n\u003cp\u003eMultiple Comparisons: Games-Howell Test\u003c/p\u003e\n\u003cp\u003eThe results of the Games-Howell post hoc test reveal several significant differences between the professions of the household heads:\u003c/p\u003e\n\u003cp\u003eAgriculture, Forestry, and Fishing vs. Industry:\u003c/p\u003e\n\u003cp\u003eHouseholds led by heads in the Agriculture, Forestry, and Fishing sector spend significantly less than those in the Industry sector (mean difference = -1,245.63 MAD, p \u0026lt; .001).\u003c/p\u003e\n\u003cp\u003eAgriculture, Forestry, and Fishing vs. Commerce:\u003c/p\u003e\n\u003cp\u003eHouseholds led by heads in the Agriculture, Forestry, and Fishing sector spend significantly less than those in the Commerce sector (mean difference = -838.52 MAD, p \u0026lt; .001).\u003c/p\u003e\n\u003cp\u003eAgriculture, Forestry, and Fishing vs. Services:\u003c/p\u003e\n\u003cp\u003eHouseholds led by heads in the Agriculture, Forestry, and Fishing sector spend significantly less than those in the Services sector (mean difference = -776.97 MAD, p \u0026lt; .001).\u003c/p\u003e\n\u003cp\u003eIndustry vs. Construction and Public Works:\u003c/p\u003e\n\u003cp\u003eHouseholds led by heads in the Industry sector spend significantly more than those in the Construction and Public Works sector (mean difference = 352.20 MAD, p = .009).\u003c/p\u003e\n\u003cp\u003eConstruction and Public Works vs. Commerce:\u003c/p\u003e\n\u003cp\u003eHouseholds led by heads in the Construction and Public Works sector spend significantly less than those in the Commerce sector (mean difference = -486.32 MAD, p \u0026lt; .001).\u003c/p\u003e\n\u003cp\u003eConstruction and Public Works vs. Services:\u003c/p\u003e\n\u003cp\u003eHouseholds led by heads in the Construction and Public Works sector spend significantly less than those in the Services sector (mean difference = -424.77 MAD, p \u0026lt; .001).\u003c/p\u003e\n\u003cp\u003eCommerce vs. Services:\u003c/p\u003e\n\u003cp\u003eNo significant difference between households led by heads in the Commerce sector and those in the Services sector.\u003c/p\u003e\n\u003cp\u003eEconomic Conclusion\u003c/p\u003e\n\u003cp\u003eThe results of the ANOVA and Games-Howell post hoc tests reveal significant differences in annual healthcare expenditures based on the profession of the household head. Households led by heads in the Agriculture, Forestry, and Fishing sector spend significantly less on healthcare compared to those in other sectors, particularly the Industry, Commerce, and Services sectors. These differences may be due to varying income levels, access to healthcare, and health insurance coverage associated with different professions.\u003c/p\u003e\n\u003cp\u003eIt is essential for policymakers to consider these disparities when designing healthcare and financial support programs. Targeted interventions should be implemented for households in sectors with lower healthcare expenditures, such as Agriculture, Forestry, and Fishing, to improve access to healthcare and reduce financial barriers. Additionally, expanding health insurance coverage and providing subsidies for healthcare in these sectors could help mitigate the observed disparities.\u003c/p\u003e\n\u003cp\u003eIn conclusion, the profession of the household head plays a significant role in determining healthcare expenditures in vulnerable households in Morocco. To ensure equitable access to healthcare, it is crucial to address the unique challenges faced by households in different sectors and to tailor public health policies and financial support accordingly.\u003c/p\u003e\n\u003cp\u003eH6: The annual healthcare expenditures of vulnerable households differ by the education level of the household head\u003c/p\u003e\n\u003cp\u003eStatistical Interpretation (Hypothesis H6)\u003c/p\u003e\n\u003cp\u003eHypothesis H6: The annual healthcare expenditures of vulnerable households differ by the education level of the household head.\u003c/p\u003e\n\u003cp\u003eHypotheses:\u003c/p\u003e\n\u003cp\u003eH0: There is no significant difference in annual healthcare expenditures by the education level of the household head.\u003c/p\u003e\n\u003cp\u003eH1: There is a significant difference in annual healthcare expenditures by the education level of the household head.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eTable 16.\u0026nbsp;\u003c/strong\u003eTest of Homogeneity of Variances\u003c/p\u003e\n\u003ctable border=\"1\" cellpadding=\"0\"\u003e\n \u003cthead\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003eTest of Homogeneity of Variances\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003eLevene Statistic\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003edf1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003edf2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003eSig.\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/thead\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003eHealthcare Expenditure per Household (in MAD)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd\u003e\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003eBased on Mean\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e251.106\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e4\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e122,214\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e.000\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003eBased on Median\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e174.493\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e4\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e122,214\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e.000\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003eBased on Median and with adjusted df\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e174.493\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e4\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e93,631.315\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e.000\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003eBased on trimmed mean\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e204.824\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e4\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e122,214\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e.000\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n\u003c/table\u003e\n\u003cp\u003eSource: Prepared by the authors based on data from the HCP-ENCDM survey (2014).\u003c/p\u003e\n\u003cp\u003eThe hypothesis of homogeneity of variances is rejected (p \u0026lt; .001), indicating that the variances between groups are not equal.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eTable 17.\u0026nbsp;\u003c/strong\u003eANOVA Results of The annual healthcare expenditures of vulnerable households by the education level\u0026nbsp;\u003c/p\u003e\n\u003ctable border=\"1\" cellpadding=\"0\"\u003e\n \u003cthead\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003eSource of Variation\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003eSum of Squares\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003edf\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003eMean Square\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003eF\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003eSig.\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/thead\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003eBetween Groups\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e3,421,664,447.907\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e4\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e855,416,111.977\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e50.760\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e.000\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003eWithin Groups\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e1,935,574,528,780.251\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e122,214\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e15,834,129.468\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd\u003e\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003eTotal\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e1,938,996,193,228.158\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e122,218\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd\u003e\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n\u003c/table\u003e\n\u003cp\u003eSource: Prepared by the authors based on data from the HCP-ENCDM survey (2014).\u003c/p\u003e\n\u003cp\u003eThe ANOVA shows a significant difference between the education levels of the household heads (F(4, 122,214) = 50.760, p \u0026lt; .001).\u003c/p\u003e\n\u003cp\u003eMultiple Comparisons: Games-Howell Test\u003c/p\u003e\n\u003cp\u003eThe results of the Games-Howell post hoc test reveal several significant differences between the education levels of the household heads:\u003c/p\u003e\n\u003cp\u003eIlliterate vs. Primary Education:\u003c/p\u003e\n\u003cp\u003eHouseholds led by illiterate heads spend significantly less than those led by heads with primary education (mean difference = -602.31 MAD, p \u0026lt; .001).\u003c/p\u003e\n\u003cp\u003eIlliterate vs. Secondary Education:\u003c/p\u003e\n\u003cp\u003eHouseholds led by illiterate heads spend significantly less than those led by heads with secondary education (mean difference = -1,116.65 MAD, p \u0026lt; .001).\u003c/p\u003e\n\u003cp\u003eIlliterate vs. Higher Education:\u003c/p\u003e\n\u003cp\u003eHouseholds led by illiterate heads spend significantly less than those led by heads with higher education (mean difference = -2,348.94 MAD, p \u0026lt; .001).\u003c/p\u003e\n\u003cp\u003eIlliterate vs. Other Education Levels:\u003c/p\u003e\n\u003cp\u003eHouseholds led by illiterate heads spend significantly less than those led by heads with other education levels (mean difference = -413.52 MAD, p \u0026lt; .001).\u003c/p\u003e\n\u003cp\u003ePrimary Education vs. Secondary Education:\u003c/p\u003e\n\u003cp\u003eHouseholds led by heads with primary education spend significantly less than those led by heads with secondary education (mean difference = -514.34 MAD, p \u0026lt; .001).\u003c/p\u003e\n\u003cp\u003ePrimary Education vs. Higher Education:\u003c/p\u003e\n\u003cp\u003eHouseholds led by heads with primary education spend significantly less than those led by heads with higher education (mean difference = -1,746.63 MAD, p \u0026lt; .001).\u003c/p\u003e\n\u003cp\u003ePrimary Education vs. Other Education Levels:\u003c/p\u003e\n\u003cp\u003eHouseholds led by heads with primary education spend significantly less than those led by heads with other education levels (mean difference = -188.79 MAD, p = .015).\u003c/p\u003e\n\u003cp\u003eSecondary Education vs. Higher Education:\u003c/p\u003e\n\u003cp\u003eHouseholds led by heads with secondary education spend significantly less than those led by heads with higher education (mean difference = -1,232.29 MAD, p \u0026lt; .001).\u003c/p\u003e\n\u003cp\u003eSecondary Education vs. Other Education Levels:\u003c/p\u003e\n\u003cp\u003eHouseholds led by heads with secondary education spend significantly less than those led by heads with other education levels (mean difference = 325.55 MAD, p = .000).\u003c/p\u003e\n\u003cp\u003eHigher Education vs. Other Education Levels:\u003c/p\u003e\n\u003cp\u003eHouseholds led by heads with higher education spend significantly more than those led by heads with other education levels (mean difference = 906.74 MAD, p \u0026lt; .001).\u003c/p\u003e\n\u003cp\u003eEconomic Conclusion\u003c/p\u003e\n\u003cp\u003eThe results of the ANOVA and Games-Howell post hoc tests reveal significant differences in annual healthcare expenditures based on the education level of the household head. Households led by heads with higher education spend significantly more on healthcare compared to those led by heads with lower education levels, particularly illiterate heads. These differences may be attributed to the higher income levels, better health awareness, and greater access to healthcare services typically associated with higher education.\u003c/p\u003e\n\u003cp\u003eTo address these disparities, public health policies should focus on improving healthcare access and education for households led by less educated heads. This could include implementing health literacy programs and providing financial assistance to support healthcare expenditures for these households. Additionally, targeted interventions aimed at raising awareness about preventive healthcare in lower education groups could help reduce the observed disparities.\u003c/p\u003e\n\u003cp\u003eIn conclusion, the education level of the household head is a significant determinant of healthcare expenditures in vulnerable households in Morocco. It is crucial to consider this factor when designing health policies and financial support programs to ensure equitable access to healthcare services across different education levels.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eGeneral Conclusion\u003c/p\u003e\n\u003cp\u003eThis study provided an in-depth analysis of the socio-economic factors influencing the annual healthcare expenditures of older adult vulnerable household heads in Morocco, revealing several key findings. First, the use of data from the 2013/2014 National Household Consumption and Expenditure Survey, covering a large sample of 122,218 households, allowed for the generation of representative and reliable results. This wealth of data provided an overview of the determinants of healthcare expenditures across a diverse population, encompassing different regions and socio-economic contexts.\u003c/p\u003e\n\u003cp\u003eAdditionally, the study examined a variety of factors, including region, place of residence, gender, household size, profession of the household head, and education level, offering a comprehensive overview of the determinants of healthcare expenditures. By examining these multiple variables, the study was able to identify complex patterns of socio-economic influences on healthcare expenditures, enriching our understanding of the underlying dynamics.\u003c/p\u003e\n\u003cp\u003eThe use of robust statistical techniques such as ANOVA and Games-Howell post hoc tests identified significant differences and ensured the validity of the results. These rigorous methods\u0026nbsp;helped guarantee that the conclusions drawn are solid and reliable, providing a foundation upon which policymakers can rely to formulate informed policies.\u003c/p\u003e\n\u003cp\u003eHowever, this study also has certain limitations that should be considered. The data used are from the 2013/2014 survey, and economic conditions and health policies may have evolved since then, potentially affecting the current relevance of the results. The rapid evolution of economic contexts and public policies means that the conclusions drawn from this study may no longer accurately reflect the current situation. It is therefore crucial to regularly update the data to maintain the relevance and applicability of the results.\u003c/p\u003e\n\u003cp\u003eWhile the study included several socio-economic factors, other potential variables influencing healthcare expenditures, such as the general health status of individuals or access to health insurance, were not considered. Including these variables could provide a more nuanced understanding of the determinants of healthcare expenditures. For example, a person\u0026apos;s general health status may significantly influence their healthcare expenditures, and access to health insurance could mitigate the financial impacts of medical care.\u003c/p\u003e\n\u003cp\u003eFurthermore, the study is based on a cross-sectional analysis, limiting the ability to establish causal relationships between the factors studied and healthcare expenditures. A cross-sectional analysis provides a snapshot of the relationships between variables at a given moment, but it does not allow for determining how these relationships evolve over time. To address this limitation, longitudinal studies would be necessary, allowing for the tracking of households over an extended period and identifying trends and changes in healthcare expenditures.\u003c/p\u003e\n\u003cp\u003eTo extend this research, several avenues can be explored. It would be beneficial to conduct a new survey with more recent data to assess the current impact of socio-economic factors on healthcare expenditures. Updating the data would capture recent changes in economic conditions and health policies, providing a more current picture of the determinants of healthcare expenditures.\u003c/p\u003e\n\u003cp\u003eThe implementation of longitudinal studies would allow for tracking households over an extended period and identifying trends and changes in healthcare expenditures over time. By following the same households over several years, researchers can observe how healthcare expenditures evolve in response to changes in socio-economic factors, public policies, and economic conditions.\u003c/p\u003e\n\u003cp\u003eIncluding additional variables such as general health status, access to health insurance, and regional health policies would provide a more comprehensive understanding of the determinants of healthcare expenditures. These variables may play crucial roles in determining healthcare expenditures, and their inclusion could reveal important insights that were previously missing.\u003c/p\u003e\n\u003cp\u003eA comparison with similar studies conducted in other developing countries could offer interesting perspectives and innovative solutions applicable to the Moroccan context. By comparing the results with those of other countries, researchers can identify common patterns and unique differences, guiding the development of more effective and tailored policies.\u003c/p\u003e\n\u003cp\u003eFinally, the results of this study can guide policymakers in developing targeted public health strategies to reduce regional and socio-economic disparities and improve equitable access to healthcare for vulnerable older adult individuals. Policymakers can use these insights to develop specific interventions that address the needs of the most vulnerable groups, ensuring that all segments of the population have adequate access to healthcare.\u003c/p\u003e\n\u003cp\u003eIn summary, this study highlights significant disparities in healthcare expenditures among older adult vulnerable household heads in Morocco, according to various socio-economic factors. The results underscore the importance of adopting inclusive and adapted public health policies to ensure equitable access to healthcare and improve the quality of life for this population. The future perspectives suggest avenues for research and action that could contribute to better understanding and addressing the healthcare needs of older adult individuals in Morocco. By continuing this line of research, scholars can continue to inform public policies and promote better health for all.\u0026nbsp;\u003c/p\u003e"},{"header":"Declarations","content":"\u003ch2\u003eAuthor Contribution\u003c/h2\u003e\u003cp\u003eD\u0026eacute;claration de contribution des auteursSaid Loucifi first author , Abdelkader Salmi first author, Mohammed Saber Hassainate, second author, wrote the main manuscript text. All authors reviewed the manuscript\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\n \u003cli\u003e\u003cstrong\u003eArber, S., \u0026amp; Ginn, J. (1991).\u003c/strong\u003e Gender and Later Life: A Sociological Analysis of Resources and Constraints. Sage Publications.\u003c/li\u003e\n \u003cli\u003e\u003cstrong\u003eArber, S., \u0026amp; Ginn, J. (1991).\u003c/strong\u003e\u003cem\u003e\u0026nbsp;\u003c/em\u003eThe invisibility of age: gender and class in later life. The sociological review,, 39(2), 260-291.\u003c/li\u003e\n \u003cli\u003e\u003cstrong\u003eBaltes, P. B., \u0026amp; Baltes, M. M. (1990).\u003c/strong\u003e Successful Aging: Perspectives from the Behavioral Sciences. Cambridge University Press.\u003c/li\u003e\n \u003cli\u003e\u003cstrong\u003eBalarajan, Y., Selvaraj, S., \u0026amp; Subramanian, S. V. (2011).\u003c/strong\u003e \u0026quot;Health care and equity in India.\u0026quot; The Lancet, 377(9764), 505-515.\u003c/li\u003e\n \u003cli\u003e\u003cstrong\u003eBanks, J., et al. (2018).\u003c/strong\u003e \u0026quot;Common health predictors of early retirement: findings from the English Longitudinal Study of Ageing.\u0026quot; Age and Ageing.\u003c/li\u003e\n \u003cli\u003e\u003cstrong\u003eBanks, J., et al. (2018).\u003c/strong\u003e \u0026quot;Predictors of Early Retirement in Older Workers: Health and Financial Considerations.\u0026quot; Work Aging and Retirement, 4(2), 125-143.\u003c/li\u003e\n \u003cli\u003e\u003cstrong\u003eBartley, M., Ferrie, J., Montgomery, S., Marmot, M., \u0026amp; Wilkinson, R. (2006)\u003c/strong\u003e\u003cem\u003e.\u0026nbsp;\u003c/em\u003eSocial determinants of health. The Solid Facts, 1-32.\u003c/li\u003e\n \u003cli\u003e\u003cstrong\u003eBMC Public Health. (2019).\u003c/strong\u003e \u0026quot;Analysis of healthcare expenditures by profession in Morocco: A comparative study.\u0026quot; BMC Public Health.\u003c/li\u003e\n \u003cli\u003e\u003cstrong\u003eBMC Public Health. (2021).\u003c/strong\u003e \u0026quot;Regional Disparities in Health Expenditures in India: An Analysis of Health Service Accessibility and Policy Implications.\u0026quot; BMC Public Health.\u003c/li\u003e\n \u003cli\u003e\u003cstrong\u003eBMC International Health and Human Rights. (2020).\u003c/strong\u003e \u0026quot;Health Expenditure Disparities in Rural and Urban Areas of Tanzania.\u0026quot; \u003cem\u003eBMC International Health and Human Rights.\u003c/em\u003e\u003c/li\u003e\n \u003cli\u003e\u003cstrong\u003eBlossfeld, H. P., Buchholz, S., \u0026amp; Kurz, K. (2011).\u003c/strong\u003e \u0026quot;Adding Scars to Wrinkles? Long-run Effects of Late-Career Job Loss on Retirement Behavior and Personal Income.\u0026quot; \u003cem\u003eWork, Aging and Retirement.\u003c/em\u003e\u003c/li\u003e\n \u003cli\u003e\u003cstrong\u003eBlossfeld, H.P., et al. (2011).\u003c/strong\u003e \u0026quot;The Impact of Late-Career Job Loss on Financial Well-being and Retirement Behavior.\u0026quot; \u003cem\u003eWork Aging and Retirement,\u003c/em\u003e 1(1), 100-117.\u003c/li\u003e\n \u003cli\u003e\u003cstrong\u003eBurch, K. A., Dugan, A. G., \u0026amp; Barnes-Farrell, J. L. (2019).\u003c/strong\u003e \u0026quot;Understanding What Eldercare Means for Employees and Organizations: A Review and Recommendations for Future Research.\u0026quot; \u003cem\u003eWork, Aging and Retirement,\u003c/em\u003e 5(1), 44\u0026ndash;72.\u003c/li\u003e\n \u003cli\u003e\u003cstrong\u003eChatterji, S., Kowal, P., \u0026amp; Mathers, C. (2008).\u003c/strong\u003e The Health of Aging Populations in Developing Countries. World Health Organization.\u003c/li\u003e\n \u003cli\u003e\u003cstrong\u003eChatterji, P., Alegria, M., Lu, M., \u0026amp; Takeuchi , D. (2008\u003c/strong\u003e). Psychiatric Disorders and Employment: New Evidence from the Collaborative Psychiatric Epidemiology Surveys (CPES). National Bureau of Economic Research Working Paper w14404.\u003c/li\u003e\n\u003c/ol\u003e\n\u003col start=\"16\"\u003e\n \u003cli\u003e\u003cstrong\u003eCylus, J., \u0026amp; Papanicolas, I. (2015).\u003c/strong\u003e Health System Performance Comparison: An Agenda for Policy Information and Research. European Observatory on Health Systems and Policies Series.\u003c/li\u003e\n\u003c/ol\u003e\n\u003col start=\"17\"\u003e\n \u003cli\u003e\u003cstrong\u003eFisher, G., \u0026amp; Ryan, L. (2018).\u003c/strong\u003e\u003cem\u003e\u0026nbsp;\u003c/em\u003eOverview of the health and retirement study and introduction to the special issue. Work, aging and retirement,, 4(1), 1-9.\u003c/li\u003e\n \u003cli\u003e\u003cstrong\u003eFuchs, V. R. (2004).\u003c/strong\u003e \u0026quot;Reflections on the Socio-Economic Correlates of Health.\u0026quot; Journal of Health Economics, 23(4), 653-661.\u003c/li\u003e\n \u003cli\u003e\u003cstrong\u003eGignac, M. A. M., Bowring, J., Shahidi, F. V., Kristman, V., Cameron, J. I., \u0026amp; Jetha, A. (2024).\u003c/strong\u003e \u0026quot;Workplace Disclosure Decisions Among Older Workers Seeking to Retain Employment: A Qualitative Study of Factors Considered When Contemplating Whether to Disclose or Conceal Support Needs.\u0026quot; Work, Aging and Retirement, 10(2), 174-187.\u003c/li\u003e\n \u003cli\u003e\u003cstrong\u003eGlobalization and Health. (2021).\u003c/strong\u003e \u0026quot;Gender Differences in Health Expenditures: The Role of Female Household Heads in Health Budget Allocation.\u0026quot; Globalization and Health.\u003c/li\u003e\n \u003cli\u003e\u003cstrong\u003eGlobalization and Health. (2021).\u003c/strong\u003e \u0026quot;The Role of Education in Health Expenditures: A Study of Preventive Health Behaviors.\u0026quot; Globalization and Health.\u003c/li\u003e\n \u003cli\u003e\u003cstrong\u003eGuralnik, J. M., Fried, L. P., \u0026amp; Salive, M. E. (1996).\u003c/strong\u003e \u0026quot;Disability as a Public Health Outcome in the Aging Population.\u0026quot; Annual Review of Public Health, 17, 25-46.\u003c/li\u003e\n \u003cli\u003e\u003cstrong\u003eGuralnik, J. (1996).\u0026nbsp;\u003c/strong\u003eAssessing the impact of comorbidity in the older population. Annals of epidemiology, 6(5), 376-380\u003c/li\u003e\n \u003cli\u003e\u003cstrong\u003eHealth Economics Review. (2021).\u003c/strong\u003e \u0026quot;Household Size and Health Expenditures: An Econometric Analysis.\u0026quot; \u003cem\u003eHealth Economics Review.\u003c/em\u003e\u003c/li\u003e\n \u003cli\u003e\u003cstrong\u003eMarmot, M., \u0026amp; Wilkinson, R. G. (2006).\u003c/strong\u003e \u003cem\u003eSocial Determinants of Health.\u003c/em\u003e Oxford University Press.\u003c/li\u003e\n \u003cli\u003e\u003cstrong\u003eM\u0026ouml;wisch, D., Brose, A., \u0026amp; Schmiedek, F. (2023).\u003c/strong\u003e \u0026quot;Active Time Use and Well-Being in Older Adulthood: Results From a Day Reconstruction Method Study.\u0026quot; Work, Aging and Retirement, 9(1), 7\u0026ndash;18.\u003c/li\u003e\n \u003cli\u003e\u003cstrong\u003eNair, M., \u0026amp; Webster, P. (2013).\u003c/strong\u003e Health professionals\u0026apos; migration in emerging market economies: patterns, causes and possible solutions. Journal of public health, 35(1), 157-163.\u003c/li\u003e\n \u003cli\u003e\u003cstrong\u003eNair, P., Rajan, S. I., \u0026amp; Srinivasan, K. (2013).\u003c/strong\u003e \u0026quot;Health Expenditure of the Older adult in India: Evidence and Implications.\u0026quot; \u003cem\u003eJournal of Aging \u0026amp; Social Policy,\u003c/em\u003e 25(3), 164-178.\u003c/li\u003e\n \u003cli\u003e\u003cstrong\u003eRowe, J. W., \u0026amp; Kahn, R. L. (1997).\u003c/strong\u003e \u0026quot;Successful Aging.\u0026quot; \u003cem\u003eThe Gerontologist,\u003c/em\u003e 37(4), 433-440.\u003c/li\u003e\n \u003cli\u003e\u003cstrong\u003eO\u0026rsquo;Donnell, O., Van Doorslaer, E., Wagstaff, A., \u0026amp; Lindelow, M. (2008).\u003c/strong\u003e Analyzing Health Equity Using Household Survey Data. World Bank Publications.\u003c/li\u003e\n \u003cli\u003e\u003cstrong\u003eOECD. (2019).\u003c/strong\u003e Health at a Glance 2019: OECD Indicators. OECD Publishing.\u003c/li\u003e\n \u003cli\u003e\u003cstrong\u003eSelvaraj, S., \u0026amp; Karan, A. K. (2012).\u003c/strong\u003e \u0026quot;Why Publicly Financed Health Insurance Schemes Are Ineffective in Offering Financial Risk Protection.\u0026quot; Economic and Political Weekly, 60-68.\u003c/li\u003e\n \u003cli\u003e\u003cstrong\u003eSmith, S., Newhouse, J. P., \u0026amp; Freeland, M. S. (2010).\u003c/strong\u003e \u0026quot;Income, Insurance, and Technology: Why Does Health Spending Outpace Economic Growth?\u0026quot; Health Affairs, 28(5), 1276-1284.\u003c/li\u003e\n \u003cli\u003e\u003cstrong\u003eWagstaff, A., \u0026amp; Van Doorslaer, E. (2003).\u003c/strong\u003e \u0026quot;Catastrophe and Impoverishment in Paying for Health Care: With Applications to Vietnam 1993\u0026ndash;1998.\u0026quot; Health Economics, 12(11), 921-933.\u003c/li\u003e\n \u003cli\u003e\u003cstrong\u003eWork Aging and Retirement. (2018).\u003c/strong\u003e \u0026quot;The Impact of Precarious Employment on Health Expenditures: Evidence from Vulnerable Households.\u0026quot; Work Aging and Retirement.\u003c/li\u003e\n \u003cli\u003e\u003cstrong\u003eWorld Health Organization. (2024).\u003c/strong\u003e \u0026quot;Are Health Services Financially Accessible? New Evidence on Financial Protection in France.\u0026quot; WHO Report on Financial Protection in France.\u003c/li\u003e\n \u003cli\u003e\u003cstrong\u003eWorld Health Organization. (2024).\u003c/strong\u003e \u0026quot;Are Health Services Affordable? New Evidence on Financial Protection in Belgium.\u0026quot; WHO Report on Financial Protection in Belgium.\u003c/li\u003e\n \u003cli\u003e\u003cstrong\u003eOxford University Press. (2019).\u003c/strong\u003e \u0026quot;Health Care Spending in Low- and Middle-Income Countries: Global Challenges and Opportunities.\u0026quot;\u003c/li\u003e\n \u003cli\u003e\u003cstrong\u003eSmith, P.C., \u0026amp; Mossialos, E. (2002).\u003c/strong\u003e \u0026quot;Health Systems in Low- and Middle-Income Countries: An Assessment.\u0026quot; Oxford University Press.\u003c/li\u003e\n \u003cli\u003e\u003cstrong\u003eStansfeld, S., Marmot, M., \u0026amp; Wilkinson, R. (2006).\u0026nbsp;\u003c/strong\u003eSocial support and social cohesion. Social determinants of health, 2, 148-71.\u003c/li\u003e\n \u003cli\u003e\u003cstrong\u003eXu, K., Saksena, P., \u0026amp; Jowett, M. (2012).\u003c/strong\u003e \u0026quot;The Determinants of Health Expenditure: A Country-Level Panel Data Analysis.\u0026quot; World Health Report.\u003c/li\u003e\n \u003cli\u003e\u003cstrong\u003eXu, X., Lazar, C., \u0026amp; Ruger, J. (2021\u003c/strong\u003e). Micro-costing in health and medicine: a critical appraisal. Health economics review, 11, 1-8\u003c/li\u003e\n\u003c/ol\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":false,"highlight":"","institution":"","isAcceptedByJournal":true,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true},"keywords":"elderly, vulnerable individuals, health expenditures, ANOVA, t-tests","lastPublishedDoi":"10.21203/rs.3.rs-5767434/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-5767434/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003eIn 1960, Morocco had 835,000 people aged 60 and over. This number reached 4.9\u0026nbsp;million in 2023 and is projected to rise to 10.1\u0026nbsp;million by 2050. The National Household Consumption and Expenditure Survey of 2013/2014 highlighted a notable disparity between male (135,387) and female (31,792) household heads. This article examines the impact of socio-economic factors on the annual health expenditures of vulnerable elderly individuals in Morocco, as well as their standard of living. Data used in this study come from the HCP-ENCDM survey (2014). We employed analysis of variance (ANOVA), t-tests, and multiple comparisons (Games-Howell) to identify variables influencing the increase in health expenditures among the elderly. The results indicate that health expenditures vary significantly based on regions, residence settings (urban/rural), household size, and the profession of the household head, while no significant difference was observed concerning gender. These findings underscore the importance of public health policies tailored to regional and socio-economic needs to reduce health inequalities.\u003c/p\u003e","manuscriptTitle":"Impact of Socio-Economic Factors on Healthcare Expenditures of Vulnerable Elderly People in Morocco","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2025-01-09 08:43:15","doi":"10.21203/rs.3.rs-5767434/v1","editorialEvents":[{"type":"communityComments","content":0}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"f11310b2-80bd-4df2-ab07-2a2b3ac06471","owner":[],"postedDate":"January 9th, 2025","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"published-in-journal","subjectAreas":[],"tags":[],"updatedAt":"2025-05-14T15:06:55+00:00","versionOfRecord":{"articleIdentity":"rs-5767434","link":"https://doi.org/10.1016/j.aggp.2025.100146","journal":{"identity":"archives-of-gerontology-and-geriatrics-plus","isVorOnly":true,"title":"Archives of Gerontology and Geriatrics Plus"},"publishedOn":"2025-05-12 00:00:00","publishedOnDateReadable":"May 12th, 2025"},"versionCreatedAt":"2025-01-09 08:43:15","video":"","vorDoi":"10.1016/j.aggp.2025.100146","vorDoiUrl":"https://doi.org/10.1016/j.aggp.2025.100146","workflowStages":[]},"version":"v1","identity":"rs-5767434","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-5767434","identity":"rs-5767434","version":["v1"]},"buildId":"8U1c8b4HqxoKbykW_rLl7","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}