Gendered vulnerabilities to a neglected disease: A comparative investigation of the effect of women’s legal economic rights and social status on malaria rates

Abstract

Although seldom noted in scholarly accounts, malaria represents a leading cause of death and underdevelopment in poor nations. Enormous cross-national variation in malaria rates across its endemic zones suggests the importance of large-scale factors in explaining comparative disease trends. While the biological vulnerability of women and children to malaria is often acknowledged, the literature has yet to investigate how gender inequalities contribute to patterns of malaria prevalence. Utilizing structural equation modeling on a sample of 90 less-developed nations and engaging insights from gender stratification perspectives, we consider the influence of both legal economic status and social dimensions of women’s status on malaria rates. We find that women’s legal economic status has an indirect relationship on malaria rates by enhancing women’s social standing and strengthening general health provisions. The results suggest that addressing issues of gender inequality in poor nations is central to tackling this persistent pandemic.

Keywords

Development gender health malaria Sub-Saharan Africa

Introduction

Malaria is a devastating, parasitic disease that is responsible for about 1–3 million annual deaths worldwide (World Health Organization (WHO), 2013). This preventable and treatable affliction represents a leading threat to health, especially among women and young children, in many less-developed nations located in tropical and subtropical zones where the Anopheles mosquito is endemic (Breman et al., 2004, 2007; Lowassa et al., 2012; Naidoo et al., 2011; Perez-Escamilla et al., 2009; Sabin et al., 2010; Sachs and Malaney, 2002; WHO, 2003; Williams et al., 2009). In fact, there are about 300–500 million clinical cases of malaria annually, with the highest burden of disease and death occurring in Sub-Saharan Africa (Breman et al., 2004, 2007; Lowassa et al., 2012; Naidoo et al., 2011; Sabin et al., 2010; Sachs and Malaney, 2002; WHO, 2013; Williams et al., 2009). Despite the continued prevalence of malaria in certain areas of the world-system, public and scholarly attention on this disease is sparse. Thus, malaria represents what many refer to as a ‘forgotten’ or ‘neglected’ disease (e.g. Farmer, 2001; Packard, 2009).

While more-developed areas eradicated malaria decades ago, it remains a persistent and devastating cause of morbidity and mortality in poor regions (Sachs and Malaney, 2002; WHO, 2013). Even within poorer, endemic zones, there is considerable variability in malaria rates across nations. Thus, it is likely that vulnerabilities to malaria go beyond individual behaviors or characteristics and rather derive from larger-scale social and economic conditions, as some societies have been more successful in addressing this pandemic than others (e.g. Bates et al., 2004).

Biomedical research demonstrates that pregnant women and young children are most susceptible to contracting malaria (e.g. Packard, 2009; Sachs and Malaney, 2002; WHO, 2003, 2013). In fact, malaria kills an African child every minute (WHO, 2013). While some of the susceptibility of women and children to malaria may have biological underpinnings, social aspects of gender stratification also contribute to disparities in this disease. Gender stratification perspectives illustrate that women face unequal access to resources in comparison to men, including economic, health, and education resources that would greatly impact malaria rates. As women represent the principal caregivers of children and the elderly, their lack of access to health resources and education also adversely impacts other members of the household and community.

Within the comparative gender stratification literature, many studies focus on the influence of women’s status on various health outcomes, such as female life expectancy, HIV, or infant mortality. In these studies, women’s status is measured using either social indicators, such as female schooling and fertility rates, or economic rights indicators, such as legal provisions for women’s access to loans, land, and property (e.g. Austin and Noble, 2014; Brady et al., 2007; Breman et al., 2004; Burroway, 2012; Lowassa et al., 2012; Medalia and Chang, 2011; Sabin et al., 2010; Wickrama and Lorenz, 2002). The primary goal of this research is to consider these two dimensions of female empowerment together and examine how these elements potentially influence one another, as well as other factors, to reduce malaria rates in less-developed nations. In other words, rather than treating social and economic dimensions of female status as competing predictors, we predict that increased legal economic rights for women enhances their socio-health standing, directly influencing the malaria disease burden by reducing vulnerabilities for women and the children in their care. Furthermore, we expect that increased access to economic resources for women may benefit the health or social standing of the community at large, as women are more likely to invest their economic resources in areas such as schooling and health care. Thus, the effects of female empowerment on disease are potentially quite significant and operate in a number of complex and interrelated ways.

A growing area of recent comparative research recognizes the importance of using direct and indirect effects in predicting cross-national health outcomes (e.g. Austin and McKinney, 2012; Kick et al., 2011; Shen and Williamson, 1997; Wickrama and Lorenz, 2002). In order to properly assess the interrelationships between various dimensions of female empowerment and other key factors on malaria rates, we employ a structural equation modeling (SEM) technique. This method allows for the specification of indirect effects, as well as the construction of latent variables, to more appropriately assess the influence of multiple aspects of female empowerment.

While much of the disproportionate burden of malaria on women and children can be partially understood biologically (e.g. WHO, 2013), this alone does not account for global patterns in prevalence across nations. Social and economic inequalities may limit the ability of women to take prevention and treatment steps against diseases, such as malaria, for themselves and other members of the household or community in their care. The persistence and scale of this treatable illness suggest that current biomedical strategies are not enough to eradicate this pathogen. Clearly, a deeper understanding of the socio-structural causes of this disease is needed in order to address the continued malaria burden. A comparative, social-scientific approach to malaria requires further attention in both the scholarly literature and public policy.

Malaria: Characteristics of a neglected disease

Approximately 3 billion people who live in tropical and subtropical regions where Anopheles mosquitoes are endemic are at risk of acquiring malaria (WHO, 2013). Significantly, poverty is concentrated in the tropical zones of the world, the same geographical boundaries that frame malaria transmission (Packard, 2009; Sachs and Malaney, 2002). Those living in poverty face increased risk of acquiring malaria for many reasons. Impoverished people lack knowledge of disease-prevention techniques, have little access to modern ‘Western’ medicine, and face limited access to appropriate preventative strategies such as screened windows or bed nets. Also, many poor households lack adequate sanitation systems, increasing potential mosquito habitats (e.g. Bates et al., 2004; Lowassa et al., 2012; Packard, 2009; Sachs and Malaney, 2002; Williams et al., 2009).

Malaria has afflicted human societies for centuries, representing a leading cause of death globally and historically. It was once thought that malaria came from fetid marshes, hence the name ‘mal-aria’, which literally means ‘bad air’ (WHO, 2013). However, in 1880, scientists discovered the real cause of malaria: a one-cell parasite called plasmodium. Later they found that the parasite spreads from person to person through the bite of a female Anopheles mosquito that requires blood to nurture her eggs (Norris, 2004; WHO, 2013). Once inside the human host, the parasite undergoes a series of changes as part of its complex lifecycle. After 9–14 days in the human host, the parasite develops into a form that is able to infect another person when transmitted by a mosquito, thus spreading the parasite (Sachs and Malaney, 2002; WHO, 2013).

Malaria symptoms also appear about 9–14 days after the infectious mosquito bite. Typically, malaria produces fever, headache, vomiting, and other flu-like symptoms and can progress very quickly to death, especially among young infants. Malaria kills by infecting and destroying red blood cells (causing anemia) and by clogging the capillaries that carry blood to the brain or other vital organs (WHO, 2013). Thus, many people who survive an episode of severe malaria suffer from neurological disorders, including learning disabilities, reduced fine motor functions, executive thinking impairment, or other forms of brain damage (Pattanayak et al., 2006; Sachs and Malaney, 2002).

As previously mentioned, pregnant women, infants, and young children face a greater biological susceptibility to malaria. Socially, gender stratification and conditions of poverty only heighten the risks of acquiring malaria for these already vulnerable segments of the population (Fried et al., 1998; Gupta, 2004; Ogbodo et al., 2009; Packard, 2009; Sachs and Malaney, 2002; WHO, 2013). For example, various epidemiological studies illustrate that acquiring malaria during pregnancy leads to deleterious maternal and neonatal outcomes such as maternal anemia, preterm labor, maternal death, stillbirths, low birth weight, and high infant mortality rates (e.g. Breman et al., 2004, 2007; Fried et al., 1998; Lowassa et al., 2012; Ogbodo et al., 2009; Perez-Escamilla et al. 2009; Sabin et al., 2010; Sachs and Malaney, 2002; Shargie et al., 2010; Verhoeff et al., 1999; WHO, 2013; Williams et al., 2009). While biomedical explanations of malaria emphasize that the decreased immunity of pregnant women leads to these conditions, social inequalities of women also limit their access to health care and promote high fertility rates (e.g. Lowassa et al., 2012). Similarly, poverty and limited health care access more generally represent broader trends connected to structural inequalities across nations. Thus, we now turn to a discussion of comparative trends in health, followed by a key focus on dimensions of female empowerment.

Health determinants in less-developed nations

A growing body of comparative health literature examines trends in life expectancy, infant/child mortality, hunger, and HIV across nations (e.g. Austin and Noble, 2014; Austin and McKinney, 2012; Brady et al., 2007; Burroway, 2010, 2012; Shen and Williamson, 1997; Shircliff and Shandra, 2011; Wickrama and Lorenz, 2002). Despite modernization-related arguments highlighting the primacy of economic growth in improving human well-being outcomes (e.g. Firebaugh and Beck, 1994), this body of research emphasizes that noneconomic factors seem most relevant in predicting health outcomes, including measures of health service provision, education, sanitation, clean water, and fertility rates (or other measures of female empowerment) (e.g. Brady et al., 2007). Given the characteristics of malaria, these factors are keenly relevant to consider here as well.

Indeed, the literature on health inequalities points to health service provisions as key to explaining cross-national variation in disease and mortality indicators (e.g. Austin and Noble, 2014; Bates et al., 2004; Soares, 2007). Many studies document the lack of quality health services in less-developed regions; many clinics and hospitals in poorer nations are run by inexperienced or untrained staff, face shortages in medical supplies and medicines, or provide inadequate diagnostics (e.g. Bates et al., 2004; Breman et al., 2007; Farmer, 2001). For many impoverished families, the lack of a basic public health infrastructure, the expense of medications, and the difficulty of traveling long distances to a medical clinic or hospital represent major barriers to effective treatment for a wide range of diseases, including malaria (e.g. Bates et al., 2004; Breman et al., 2007; Farmer, 2001; Lowassa et al., 2012). In addition to health-care services, assessments also emphasize the importance of provisions for improved sanitation (e.g. Austin, 2013; Austin and McKinney, 2012; Shandra et al., 2011; Soares, 2007). As mosquitoes inhabit standing water, sanitation issues are keenly relevant to malaria; open sanitation pits and streams represent prime mosquito habitats and lead to increased transmission (Breman et al., 2004; Norris, 2004).

In addition to the role of health-care services and improved sanitation, numerous cross-national analyses emphasize the importance of education in improving physical well-being in less-developed nations (e.g. Austin and Noble, 2014; Brady et al., 2007; Burroway, 2010). Education tends to improve health through a variety of mechanisms. For example, schooling exposes pupils to information on disease vectors and transmission, leading to more successful prevention. Educated people also tend to have more hygienic behaviors that reduce infectious disease transmission. Education also can expose individuals to techniques of modern medicine and dispel harmful myths, again leading to more successful treatments (Bates et al., 2004; Heimer, 2007; Soares, 2007). Education is found to be especially important for women, and improving women’s access to education represents a robust predictor of a wide variety of health outcomes (e.g. Brady et al., 2007; Burroway, 2012; Wickrama and Lorenz, 2002). While educating women is important, we draw on gender stratification literature to examine additional dimensions of female empowerment that are central in explaining cross-national trends in the malaria pandemic.

Gender stratification in less-developed nations

Gender differences are socially constructed and maintained, including ideologies that perpetuate the notion that males and females have different capabilities and that men are inherently superior to women (Ridgeway and Smith-Lovin, 1999). This belief justifies the differential treatment of women and unequal provisions for women in societies based on gender, or gender inequality (Ridgeway and Smith-Lovin, 1999). Gender inequality, where women’s economic, social, and health status is marginalized relative to men’s, is especially pronounced in less-developed nations. Given these comparative patterns, gender stratification theory informs the relationship between women’s status and health in a number of cross-national studies (Austin and Noble, 2014; Breman et al., 2004; Burroway, 2012; Lowassa et al., 2012; Medalia and Chang, 2011; Sabin et al., 2010; WHO, 2008; Wickrama and Lorenz, 2002). Although gender inequality takes various forms in economic, political, and social realms, research centered on comparative patterns in health focus on the power or ability of women to access both economic and social resources (e.g. Holvoet, 2005; Mayoux, 2001; Parvin et al., 2005; Sherer et al., 2004; Taj et al., 2008; Tsai et al., 2011). We begin by considering connections between women’s legal economic rights and health.

Formal economic rights for women and influences on health

A growing body of scholarship acknowledges legal economic rights of women as a key dimension of empowerment that greatly impacts health and well-being (e.g. Agarwal, 1994, 1997; Ali et al., 2007; Burroway, 2012; Parvin et al., 2005). Legal restrictions for women legitimize discriminatory customs that profoundly affect women’s ability to access health resources and make decisions about their health and well-being (Agarwal, 1994; Burroway, 2012; Villarreal, 2006). Many less-developed nations have formal legal restrictions on the economic activities of women, thus reinforcing and facilitating gender and health inequalities (Agarwal, 1994; World Bank, 2013). Providing women with basic economic rights allows them the opportunity to gain control over their financial situation and have increased capacity to make decisions about how money is spent in the household. Not only do greater economic rights reduce risks for poverty, but they also give women the economic means to provide for themselves and their children, potentially improving the health standing of the entire household (Agarwal, 1994, 1997; Parvin et al., 2005).

Prior empirical assessment finds that women’s access to property, land, and loans significantly reduce both female HIV prevalence and total HIV prevalence in poor nations (e.g. Burroway, 2012). Similarly, access to ownership of property, land, and loans signify economic resources and sources of autonomy for women that may give women and households the economic means to pay for malaria treatment and prevention practices such as bed nets. Egalitarian economic rights for women perhaps trickle down to impact malaria in more indirect ways, such as by increasing the social and health status of women themselves, and by facilitating increased demand and provisions for public health resources in the broader community. A growing body of literature emphasizes that revenues earned by women are more often used to meet basic needs that improve quality of life, such as education fees, health care costs, clean water and sanitation services, and clothing for children, in comparison to earnings made by men (Agarwal, 1994, 1997; Taj et al., 2008). Many studies report specifically that loans targeted at women produce enhanced social and health conditions in less-developed nations (Holvoet, 2005; Mayoux, 2001; Parvin et al., 2005; Taj et al., 2008). Scholars link microfinance loan programs to outcomes, such as increasing food consumption, enhancing the capacity of women to engage in and manage financial activities, heightening female household bargaining power, and increasing contraceptive use (Parvin et al., 2005; Sherer et al., 2004; Taj et al., 2008; Tsai et al., 2011).

Research also demonstrates that in regions where women have formal access to economic resources, women are more likely to occupy influential positions within their communities. Women more often use their bargaining power to promote community development projects that serve to better the health and well-being of their community or region (e.g. Agarwal, 1994, 1997; Kristof and WuDunn, 2009; Schuler and Hashemi, 1994). A very recent WHO (2014) report highlights the work of women in Gujarat, India, in creating the Self-Employed Women’s Association (SEWA). They used their earnings to cumulatively invest in and create various services, including health clinics, childcare facilities, work security insurance, legal services, and housing that are available to members as well as nonmembers in the region. The SEWA women also started their own bank, increasing access to credit for women and avoiding the huge interest rates demanded by private loan agents. In addition, they collectively organized a health insurance program used to subsidize health care costs, which the women of the broader community identified as a major vulnerability to poverty (WHO, 2014).

Schuler and Hashemi (1994) also demonstrate that the community as a whole, including nonparticipants, benefits indirectly when women gain access to savings-and-loan schemes. Programs, such as the Grameen Bank, designed to empower women, significantly affect households and communities beyond those directly involved in the program by providing a broad range of services that facilitate changes in social norms about gender and equality. For example, even among nonmembers of Grameen Bank, they find that contraceptive use increased in villages where programs are implemented (Schuler and Hashemi, 1994).

These examples suggest that providing women with equal access to economic resources enhances developmental outcomes for the community as a whole, as women are more likely than men to push collectively for greater social and health outcomes in their communities and reinvest their earnings into areas that promote community development. This draws on broader development thinking, adopting a wider view or definition of development. For example, Sen (1999) argues that development cannot be measured solely by economic indicators but more appropriately includes social and quality-of-life dimensions, as the end goal of development is generally improvements in human well-being. Using a framework of ‘development as freedom’ (Sen, 1999), when women garner the same access to economic resources as men, we are likely to see an increase in development, particularly social development that addresses basic needs such as schooling, health care access, and sanitation. These ideas also suggest that in addition to improvements in the broader community, women’s legal economic rights facilitate improved socio-health status of women in particular. Indeed, while theorizations on gender stratification suggest a deep connection between women’s access to economic resources and social status, current comparative assessments tend to treat these sets of factors independently (e.g. Burroway, 2012; Wickrama and Lorenz, 2002). We now turn to a discussion of women’s socio-health status, with continued key emphasis on how formal economic rights and social conditions for women are linked.

Socio-health status of women and influences on health

The adoption of egalitarian economic rights likely increases participation in education and the utilization of health care for women in particular; women tend to use part of their earnings to invest in themselves and perhaps the well-being of their daughters specifically. Participation in education is particularly significant, as it greatly affects women’s knowledge of and access to various health resources. Educated women are more likely to have the power and ability to make decisions about their own health, reproductive, and well-being concerns (Austin and Noble, 2014; Burroway, 2012; Karlsen et al., 2011; Medalia and Chang, 2011; Wickrama and Lorenz, 2002). Indeed, a wide body of research finds that female access to education represents a key predictor of women’s health outcomes as well as general health measures such as HIV prevalence, life expectancy, and infant and child mortality (e.g. Austin and Noble, 2014; Brady et al., 2007; Burroway, 2012; Karlsen et al., 2011; Shen and Williamson, 1997; Shircliff and Shandra, 2011; Wickrama and Lorenz, 2002).

Women’s education and fertility are prominently intertwined. According to Wickrama and Lorenz (2002), schooling influences women’s health most predominantly by providing them with the opportunity to acquire health and reproductive information and develop problem-solving and decision-making skills, attitudes, behaviors, and aspirations that all contribute to reduced fertility, and, therefore, enhanced health outcomes. More educated women increasingly work outside of the home (e.g. Heimer, 2007), placing less emphasis on child rearing and reducing family size. As pregnancy is a risk factor for a variety of health concerns, including malaria, reductions in fertility are paramount in addressing gendered health inequalities.

Furthermore, equal economic rights for women also influences their access to health resources (e.g. Shen and Williamson, 1997; Wickrama and Lorenz, 2002). Access to health care is especially important during pregnancy and the month following childbirth for a number of reasons, including malaria prevention and management (Bates et al., 2004). Thus, women assisted by trained professionals, who are knowledgeable and equipped with the skills necessary in conducting and managing pregnancies and childbirths, greatly decrease women’s health issues and susceptibilities to infectious diseases for themselves and their vulnerable infants (Austin and Noble, 2014; WHO, 2008).

These socio-health dimensions of empowerment (women’s access to education, women’s access to health care, reduced fertility rates) are likely to have a more proximate or direct impact on malaria rates, as these factors address both the biological and social conditions that facilitate malaria vulnerabilities for women, young infants, and children. Women’s legal economic rights are likely to underlie these female socio-health status measures, thus impacting health outcomes indirectly as women use their increased economic autonomy to enhance their access to social and health resources that reduce malaria transmission. In addition, these theorizations suggest that economic rights for women lead to expansions in general health provisions in the broader community.

While existing assessments examining other health outcomes, such as HIV, life expectancy, or infant mortality, highlight the importance of women’s status on improvements in aggregate health outcomes (e.g. Austin and Noble, 2014; Brady et al., 2007; Burroway, 2010, 2012; Shen and Williamson, 1997), these ideas have not yet been applied to the malaria pandemic. Moreover, as previously mentioned, much of this research treats women’s legal economic status, socio-health dimensions of women’s status (such as fertility rates), and general health provisions as competing predictors, rather than carefully drawing on prior ideas to theorize how these elements work through and with one another to shape the distribution of health inequalities across nations (e.g. Austin and Noble, 2014; Brady et al., 2007; Burroway, 2010, 2012). Deconstructing the relationship between women’s legal economic rights, general provisions for health, and women’s socio-health status can help provide further insights into how the adoption of gender-egalitarian economic policies leads to broad improvements in physical well-being. We specify our key hypotheses formally below.

Predictions

The arguments explored above suggest that improving various dimensions of women’s status plays an important role in reducing the malaria burden in low-income nations, where women and children are more likely to acquire the disease. In particular, it is likely that gender-egalitarian economic policies work indirectly to reduce malaria rates, as the increased economic autonomy of women often is directed toward enhancing health provisions in the community and improving access to these resources for women that have more proximate effects on disease transmission. In other words, economic rights for women lead to increased access to social and health resources, for both the broader community and for women in particular, resulting in reduced malaria prevalence.

We, therefore, hypothesize that nations with egalitarian legal economic rights (including formal access to land, loans, and property for women) have enhanced provisions for health (health-care providers, education, and sanitation), in comparison with nations who legally prevent women from accessing or utilizing economic resources. We also hypothesize that legal economic rights for women (formal access to land, loans, and property) increases women’s socio-health status (female access to medical care, education, and reduced fertility), where nations with more egalitarian policies tend to have enhanced socio-health standing of women. In so doing, we recognize female legal economic status and female socio-health status as two distinct components or dimensions of female empowerment, where enhanced economic rights facilitate increased socio-health standing for women across less-developed nations.

In turn, we also hypothesize that health provisions (health-care providers, education, and sanitation) and women’s socio-health status (access to schooling, health care, and reduced fertility for women) directly reduce malaria rates across nations. Taken together, we thus predict that there are important direct and indirect relationships among these sets of predictors. To clarify these predictions, we provide a basic path diagram of these central predictions in Figure 1.

Figure 1.

Hypothesized causal model predicting malaria rates.

In addition to the predictions outlined above, we also draw on prior cross-national health and malaria research to predict that economic development or gross domestic product (GDP) per capita helps to explain cross-national trends in malaria prevalence. However, it is likely that GDP works indirectly by impacting women’s legal economic status, socio-health status, or general provisions for health resources, as many studies document that GDP in itself is often not a significant or robust predictor of health outcomes but acknowledge that more affluent nations tend to have increased gender equality and better public health-care provisions (e.g. Austin and Noble, 2014; Brady et al., 2007; Burroway, 2010, 2012). Thus, it is likely that GDP per capita has only indirect effects on malaria prevalence across nations.¹ Nations located in tropical zones experience a higher malaria burden, and we thus hypothesize that nations located further from the equator will have lower rates of malaria. Comparative trends also indicate that Sub-Saharan African nations face a disproportionately high malaria disease burden (WHO, 2013). We also predict that Sub-Saharan African nations have higher rates of malaria prevalence; however, similar to arguments made for GDP above, we expect that much of this is due to the relatively low standing of women and poor provisions for public health infrastructure in this region (e.g. Heimer, 2007), making the effects of Sub-Saharan Africa residence on malaria also an indirect effect.

Methods

Sample

Our sample includes less-developed nations for which there is a consistent and measureable incidence of malaria for the year 2010. The World Malaria Report (WHO, 2013) provides measurable rates of malaria on 106 malaria endemic nations; 90 nations with sufficient data were retained in our sample.² For a complete list of the countries included in the analyses, see Table 1.³

Table 1.

Countries in the analysis (N = 90).

Country	Malaria prevalence per 100,000	Country	Malaria prevalence per 100,000
Afghanistan	201.82	Kyrgyz Republic	0.06
Algeria	0.03	Lao PDR	335.44
Angola	8819.19	Liberia	23088.25
Azerbaijan	0.55	Madagascar	977.37
Bangladesh	37.58	Malawi	2043.50
Belize	43.52	Malaysia	20.49
Benin	4779.36	Mali	1480.06
Bhutan	60.06	Mauritania	57.63
Bolivia	138.66	Mexico	1.08
Botswana	52.12	Morocco	0.01
Brazil	171.65	Mozambique	6509.31
Burkina Faso	4885.26	Namibia	24.35
Burundi	21036.37	Nepal	9.32
Cambodia	349.10	Nicaragua	11.96
Cameroon	0.01	Niger	3997.29
Cape Verde	3.63	Nigeria	347.92
Chad	1785.38	Pakistan	126.26
China	0.37	Panama	11.89
Colombia	254.13	Papua New Guinea	1369.97
Comoros	4972.85	Paraguay	0.42
Congo, Dem. Rep.	3665.20	Peru	100.34
Congo, Rep.	1757.35	Philippines	19.90
Costa Rica	2.45	Russian Federation	0.01
Cote d’Ivoire	317.80	Rwanda	6011.57
Djibouti	114.66	Sao Tome & Principe	1656.62
Dominican Republic	34.39	Senegal	1334.54
Ecuador	13.05	Sierra Leone	15918.57
El Salvador	0.39	Solomon Islands	7377.90
Eritrea	684.89	South Africa	7.75
Ethiopia	1396.27	Sri Lanka	3.06
Gabon	568.99	Sudan	2144.28
Gambia, The	6731.86	Suriname	326.32
Georgia	0.01	Swaziland	25.39
Ghana	4393.43	Tajikistan	1.61
Guatemala	51.32	Tanzania	0.09
Guinea	957.50	Thailand	46.99
Guinea-Bissau	3325.65	Timor-Leste	4213.30
Guyana	3039.79	Togo	10237.59
Haiti	842.10	Turkey	0.01
Honduras	127.39	Uganda	4730.52
India	130.65	Uzbekistan	0.01
Indonesia	95.81	Vanuatu	3408.71
Iran, Islamic Republic	2.50	Venezuela, RB	156.60
Iraq	0.01	Vietnam	20.15
Kenya	2217.90	Yemen, Republic	443.60

Analytic strategy

We employ SEM to assess the relationships between measures of women’s status and malaria prevalence in lower-income, malaria endemic nations using the statistical package AMOS.⁴ Our research design utilizes a time-ordered dependent variable, where the dependent variable is measured in time after the independent variables. This is a common strategy used in cross-sectional macro-comparative research in order to help adhere to conditions of causality, where causes must precede effects in time (e.g. Austin and McKinney, 2012; Burroway, 2010, 2012; Shircliff and Shandra, 2011). In this study, we measure malaria prevalence rates for the year 2010, and all independent variables are measured for the year 2009, since we would expect the influences on malaria to be fairly immediate.

SEM can be viewed as a more general and flexible framework for modeling relationships between variables than multiple regression (e.g. Bollen, 1989), and there are several benefits in using SEM to model malaria prevalence. SEM allows for testing the dimensionality of the concept of women’s status, the first step of our analytic strategy. Measurement equations are used to obtain the amount of shared and unique variance among the indicators of each dimension of women’s status. Furthermore, SEM also includes a structural model where careful consideration of the potential interconnections or indirect pathways among indicators is taken into account. This is especially relevant in our application here, as we predict a number of indirect relationships, including our key hypothesis that women’s socio-health status serves to mediate the effects of women’s legal economic rights on malaria prevalence.

Another key benefit of the SEM framework is the ability to obtain separate measures for direct, indirect, and total effects for predictor variables of interest on malaria prevalence. This allows us to interrogate more closely not only the effects of our predictors, but also evaluate potential pathways leading to malaria prevalence beyond the measures of direct effects that are shown in typical ordinary least squares (OLS) regressions. Finally, we can utilize new tests and measures of model fit, over-and-above F-tests, t-tests, and confidence intervals, and R² values, to evaluate models and judge how closely our hypothesized model fits the data at hand.⁵

Dependent variable

The key dependent variable in our analysis is the malaria prevalence rate, measured for the year 2010. The malaria prevalence variable was constructed using data on the confirmed number of malaria cases (WHO, 2013) and total population level from the World Bank (2013). These confirmed malaria cases are reported to the WHO from national malaria control programs. The number of malaria cases for each nation is weighted by its total population and then multiplied by 100,000 to form the prevalence rate. It is common to find a variable that is left-bounded at zero to have a nonnormal distribution. As shown in Table 1, the malaria prevalence rate is implicitly bounded at zero, and there are large differences in the malaria prevalence from country to country. To correct for this nonnormality and adhere to the regression assumptions, we log-transformed malaria prevalence, as is common in macro-comparative research of highly skewed health outcomes (e.g. Austin and Noble, 2013; Burroway, 2010, 2012). However, as a result of this log-transformation, the interpretation of the relationships between our outcome and its predictors must be taken into account. When interpreting this dependent variable, changes in the independent variable result in percentage changes in malaria prevalence.⁶

Key independent variables

To explore the relationships between women’s legal economic status, socio-health status, and malaria prevalence, we include six key independent variables that measure women’s status. We hypothesize that there are two underlying latent factors of women’s status represented by these six key variables, women’s legal economic status and women’s socio-health status. We follow prior researchers (e.g. Burroway, 2012) by measuring women’s legal economic status with three key variables: women’s legal access to land, legal access to property other than land, and legal access to credit or loans. These variables were collected from the 2009 Gender, Institutions and Development Database (GID-DB) from the Organisation for Economic Co-operation and Development (OECD). Access to land captures women’s access to the ownership of agricultural land. Access to property refers to women’s ability to own property other than land, especially immovable property, and access to enter into property contracts. Access to loans refers to women’s ability to acquire loans and credit. These three variables quantify women’s legal and effective access to ownership of three types of economic resources. These access variables are coded in three ordered categories, where higher values equate to more access: legal equality of women and men (1), legal equality of women compared to men but discriminatory practices still exist and represent impediments to access for women (.5), and the existence of some legal restrictions or de facto discriminatory practices that serve as impediments to access (0).

To capture women’s socio-health status, we use three key variables: female-to-male schooling ratio, fertility rate, and the percentage of births attended by skilled health staff. Female to male secondary school enrollment ratio, measured using gross enrollment estimates, calculates the ratio of total enrollment of females in secondary-level education to the gross enrollment of males in secondary education. This measure appropriately captures inequality in access to education of women versus men. Many studies demonstrate that improving access to education among women is one of the most important predictors of general health measures (e.g. Austin and Noble, 2014; Brady et al., 2007). Similarly, the fertility rate holds strong correlations to health (Wickrama and Lorenz, 2002). Fertility captures key aspects of female empowerment, as more empowered women are able to gain control of their reproductive rights and reduce fertility. Fertility rates also represent an important control variable in this analysis, as pregnant women and young children have biological vulnerabilities to malaria (e.g. Lowassa et al., 2012). The fertility rate is an estimate of the number of children an average woman would have if current age-specific fertility rates remained constant during her reproductive years. In this analysis, we reverse code this variable as to construct a measure of low fertility rate. Recoding this measure allows higher values to be associated with increased status of women. Percent of births attended represents the percentage of the total deliveries under the supervision and care of skilled health staff. This includes guidance to pregnant women at all stages of pregnancy, including pre-labor, labor, and the postpartum period. Not only is there direct relevance of malaria prevention with increased medical care surrounding birth, but this measure also uniquely represents a gender-specific variable that captures women’s access to medical care more generally (e.g. Austin and Noble, 2014). We obtained each of these variables from the World Bank’s (2013) World Development Indicators database for the year 2009.

Additional independent variables

To account for the influence of economic development, we include GDP per capita, the total annual output of a country’s economy divided by its population, measured in current international dollars for the year 2009 (World Bank, 2013). More specifically, GDP per capita is the total market value of all final goods and services produced in a country in a given year, equal to total consumer, investment, and government spending, divided by the midyear population. It is converted into current international dollars using purchasing power parity (PPP) rates, providing a standard measure allowing for comparisons of real price levels between countries. We performed a log-transformation of this variable to reduce the influence of extreme outliers.

In addition to economic development, general measures of public health and education provisions (that are not gender-specific) represent important factors in the analysis. We include number of health-care providers, for the year 2009. This represents the number of trained doctors, nurses, and midwives per 1000 people and includes both generalist and specialist medical personnel (World Bank, 2013). To assess the influence of education, we include secondary school enrollment. This measure represents a gross enrollment ratio that calculates the ratio of total enrollment, regardless of age, to the population age group that officially corresponds to secondary-level education for the year 2009. Access to sanitation is an additional public health measure that has key relevance for malaria, since open and untreated sanitation pits can represent prime mosquito habitats (e.g. Bates et al., 2004). We thus include percent of the population with improved access to sanitation, pertaining to disposal facilities that can effectively prevent human, animal, and insect contact with excreta, such as piped sanitation systems or properly constructed latrines (World Bank, 2013).

Many explanations also focus on geographical or environmental factors in contributing to cross-national disparities in malaria rates (e.g. Norris, 2004). We include a measure of latitude to measure proximity to tropical zones. Measures of latitude were obtained from the CIA World Factbook (2010), where we estimate the absolute value to capture distance from the equator. We include a regional indicator for Sub-Saharan Africa, as many assessments document the increased malaria burden in this region. This measure represents a dummy variable, where countries coded with a ‘1’ indicate location in Sub-Saharan Africa and those with a ‘0’ indicate that a country is located in a different region of the world.

Results

Table 2 displays the correlation matrix of all of the variables used in the analyses. The magnitude of the relationships among the variables demonstrates that many of the predictor variables are highly correlated. This further warrants the use of the SEM analytical technique given its superior handling of intercorrelated independent variables through the creation of latent constructs and direct and indirect pathways that circumvents the tendency to bias coefficient estimates (e.g. Bollen, 1989; Byrne, 2009).

Table 2.

Correlation matrix, means, and standard deviations.

		(1)	(2)	(3)	(4)	(5)	(6)	(7)	(8)	(9)	(10)	(11)	(12)	(13)	(14)
(1)	Malaria prevalence (ln)	1.00
(2)	Loan access	−.45	1.00
(3)	Land access	−.50	.57	1.00
(4)	Property access	−.38	.58	.59	1.00
(5)	Female/male schooling	−.46	.56	.45	.45	1.00
(6)	Low fertility	−.67	.59	.57	.51	.75	1.00
(7)	% Births attended	−.57	.54	.54	.40	.61	.66	1.00
(8)	Health providers	−.63	.48	.54	.42	.41	.63	.59	1.00
(9)	Secondary sch.	−.69	.57	.54	.44	.73	.81	.76	.72	1.00
(10)	Sanitation	−.66	.57	.52	.44	.64	.71	.71	.65	.81	1.00
(11)	Democracy	−.01	.16	.11	.22	.50	.24	.24	−.05	.32	.13	1.00
(12)	GDP p/c (ln)	−.61	.48	.46	.44	.69	.75	.70	.57	.76	.71	.29	1.00
(13)	Sub-Saharan Africa	.53	−.53	−.52	−.39	−.44	−.67	−.40	−.51	−.62	−.65	−.07	−.53	1.00
(14)	Latitude	−.61	.26	.26	.25	.06	.37	.21	.67	.38	.37	−.22	.26	−.36	1.00
	Mean	4.9	0.7	0.6	0.7	0.9	96.3	68.1	0.8	59.8	53.0	3.9	7.9	0.4	16.2
	SD	3.2	0.3	0.3	0.3	0.1	1.4	24.5	1.0	25.1	28.1	1.5	0.9	0.5	11.7

GDP: gross domestic product.

A preliminary step in our empirical assessment of the complete SEM was determining if women’s legal economic status and women’s socio-health status represent distinguishable components. Building upon prior scholarship in gender stratification, we predict that legal economic status and socio-health status do represent different aspects of female empowerment. Legal provisions represent institutional characteristics that may or may not influence gendered social inequalities in access to education, health care, and improving reproductive conditions of women (e.g. Burroway, 2012). We measure women’s legal economic status with a set of variables that indicate whether the country permits formal access to land, access to loans, and access to property for women. Socio-health status is represented by the female-to-male secondary schooling ratio, low fertility rate, and the percentage of births attended by a skilled medical professional. To test this hypothesis, we initially construct a confirmatory factor analysis (CFA) with two distinct factors representing women’s legal economic status and socio-health status and analyze the overall and component measures of fit. We compared this to a model where all six indicators loaded on a single factor of women’s status. By empirical standards, we find evidence at both the component and overall model levels to support our substantive hypothesis that the two-factor model is superior.⁷ This also fits with our substantive interpretations. Therefore, we build on the two-factor model of women’s status to include the other indicators in the structural equation model.

In similar fashion, we find that the number of health-care providers, secondary school enrollment, and access to improved sanitation represent a distinct factor capturing public health conditions or provisions. We initially included these variables as separate indicators but found that model fit was greatly enhanced by modeling them together as a distinct component.⁸ This is theoretically and substantially grounded, as many studies find that these social predictors or social gradients of health-related to education, health care, and sanitation represent a bundle of components that work together to improve health, with distinct effects in comparison to economic indicators, such as GDP per capita (e.g. Austin and Noble, 2014; Brady et al., 2007).

Before we report the results of the SEM model, it is requisite to examine the overall model fit statistics that assess the fit of our model to the data provided. In accordance with standards typical for this empirical tradition, the chi-square test statistic is nonsignificant (p = .152; 65.71 with 55 degrees of freedom (df));⁹ the values of the Incremental Fit Index (.985), Tucker–Lewis Index (.974), and the Confirmatory Fit Index (0.984) all exceed .90; the root mean squared error of approximation (RMSEA) value (.047) is below the threshold of .05; and the Bayesian Information Criterion (BIC) is −182, where a large negative value indicates that the hypothesized model has superior model fit than the fully saturated model.¹⁰ Together, these fit indices demonstrate that the model presented has excellent fit to the data and permits interpretation of the pathway coefficients, which are all statistically significant at the .05 level.¹¹

The results of the SEM are presented in Figure 2. We tested all theoretically and substantially informed paths as predicted, and then eliminated all nonsignificant interrelationships, as is requisite in this tradition (Byrne, 2009). Each remaining pathway coefficient is statistically significant and represents the standardized regression coefficient. Reporting the standardized regression coefficients allows for comparison of the relative size of the effects of independent variables on a dependent variable, where larger numbers indicate a stronger effect in comparison to the other predictors.¹² Consistent with our predictions, women’s socio-health status has the strongest direct influence on malaria rates (−.57), where increased equality in access to education for women, the percent of births attended by skilled health staff, and lower fertility rates are associated with lower rates of malaria prevalence across nations. Latitude has a slightly weaker direct influence on cross-national malaria rates (−.43), where distance from the equator is associated with decreases in the malaria burden.

Figure 2.

SEM predicting malaria prevalence displaying standardized regression coefficients.

Also consistent with our predictions, we find that women’s legal economic status, indeed, impacts malaria rates indirectly. The results demonstrate that legal economic rights for women tend to improve women’s socio-health status (.18), which in turn directly reduces rates of malaria. In addition, we find that women’s legal economic rights are associated with improved public health provisions (.30). This confirms other findings conducted at the case-study level that emphasize that increased economic autonomy of women leads to greater bargaining and investment in health resources (including education) that benefit the entire community. Thus, these findings provide support for our key hypotheses concerning the importance of women’s legal economic status in leading to increased public health provisions and improved socio-health status for women, representing an important indirect predictor of cross-national malaria rates.

The results indicate that public health provisions (health-care providers, education, and sanitation) also greatly enhance female socio-health status (.63), suggesting that nations with increased public health provisions tend to have more women able to access and benefit from those resources. Somewhat surprisingly, the results indicate that public health provisions (health-care providers, education, and sanitation) only have negative influences on malaria rates indirectly through advancing female socio-health status.¹³ In essence, this finding demonstrates that improvements in public health only serve to impact malaria rates in so far as women are able to access and utilize these provisions. This finding certainly demonstrates the importance of increasing the social standing of women in addressing the malaria pandemic.

The results show that GDP per capita has many indirect influences on malaria by enhancing women’s legal economic status (.39), women’s socio-health status (.24), and public health resources (.51). Sub-Saharan African nations tend to have less egalitarian legal provisions of women’s economic rights (−.41), as well as reduced provisions of public health resources in comparison to other nations (−.23). In these ways, economic development and location in Sub-Saharan Africa have important indirect influences on malaria rates. We also find that latitude is associated with number of health-care providers (.46), where there are fewer trained health workers in nations closer to the equator.

Although the effects of many relevant predictors are indirect, these remain notable, especially given the relative size or magnitude of some of the indirect parameters displayed in Figure 2. Comparing the relative size of direct, indirect, and total effects (the combination of indirect and direct effects) of indicators on malaria rates further illustrates the relevance of certain factors in explaining cross-national variation in malaria rates. These results are presented in Table 3. Comparing the size standardized regression coefficients in Table 3, we find that women’s socio-health status has the largest total influence on malaria rates (−.57), accounted for entirely through its direct effect on malaria rates. Latitude has the second largest total effect on malaria rates (−.43). GDP per capita has the third largest total effect on malaria rates (−.41), accounted for by its indirect relationships to other predictors in the model. Health resources have the next largest total effect (−.36), followed by women’s legal economic status (−.21), and Sub-Saharan Africa (.17). Although many variables operate indirectly, the results presented in Table 3 suggest that the overall or total influence of some of these indirect indicators is still quite robust, such as GDP per capita and public health resources. While the total effect of women’s legal economic status on malaria prevalence rates across less-developed nations is relatively modest, it does represent an important predictor, and has strong influence on other key predictors in the model (e.g. health provisions). The ability to appropriately model and assess indirect, direct, and total effects leads to more appropriate testing and synthesis of theory and substantive evidence, and helps illuminate relevant interrelationships that would go undetected in traditional direct effects analyses.

Table 3.

Direct, indirect, and total effects of the predictors of malaria prevalence.

Predictor	Malaria prevalence (ln)
	Direct	Indirect	Total
GDP per capita (ln)	–	−.406***	−.406***
	–	.058	.058
	–	(−1.326)	(−1.326)
Sub-Saharan Africa	–	.170***	.170***
	–	.041	.041
	–	(1.045)	(1.045)
Latitude	−.425***	–	−.425***
	.067	–	.067
	(−.111)	–	(−.111)
Women’s legal economical rights	–	−.211***	−.211***
	–	.062	.062
	–	(−2.751)	(−2.751)
Women’s socio-health status	−.574***	–	−.574***
	.064	–	.064
	(−13.558)	–	(−13.558)
Health provisions	–	−.364***	−.364***
	–	.091	.091
	–	(−.049)	(−.049)

Standardized coefficients flagged for statistical significance; standard errors reported in italics; unstandardized coefficients reported in parentheses.

***

p < .001; **p < .01; *p < .05 (one-tailed directional tests).

Conclusion

Gender disparities and issues of infectious disease represent two persistent features of global inequality. The findings demonstrate that issues of gender stratification and disease are deeply connected to one another; women’s legal economic status and socio-health status represent two dimensions of female empowerment that are crucial in explaining cross-national variation in malaria prevalence. The connection between these dimensions of women’s status and malaria includes a complex set of relationships, where the impact of legal economic rights for women on malaria prevalence is mediated by women’s socio-health status and general public health provisions. The socio-health standing of women also completely mediates the influence of general public health provisions on malaria, demonstrating the relative importance of women’s socio-health status in curbing this infectious disease. The results strongly suggest that improvements in public health conditions only reduce malaria rates in so far as women are able to access those resources. This is undoubtedly due to the fact that women are the principal caregivers of children, and children and pregnant women epidemiologically are most vulnerable to this infection. Thus, it is increasing women’s participation in education, access to health care, and reduced fertility that has the biggest direct impact on cross-national patterns in this pathogen.

To the best of our knowledge, this represents one of the first studies to carefully scrutinize the relationship between malaria and women’s status, as well as initiate examination of both legal economic and socio-health facets of gender inequality. While prior research took substantive focus on either one aspect of female empowerment or the other, or has set up dimensions of women’s legal economic and socio-health status as competing predictors in direct-effects analyses (e.g. Austin and Noble, 2014; Brady et al., 2007; Burroway, 2012), we rigorously examine the interrelationships between various dimensions of empowerment using a more integrative modeling approach. Doing so illuminates a number of important mediating or indirect relationships that would remain undetected using more traditional methods.

Overall, we find that women’s legal economic rights have an indirect, negative relationship on malaria rates. Egalitarian economic rights for women increase their socio-health standing, but also strengthen general provisions for public health resources. This confirms prior case-study and substantive research that finds that women tend to use their economic autonomy to invest in and promote community needs, such as provisions for health-care providers, schools, and sanitation infrastructure (e.g. Agarwal, 1994, 1997; Kristof and WuDunn, 2009). In addition, the strong relationship between public health provisions and women’s socio-health status indicates that increased provisions for education and health services help to facilitate women’s access to those resources. However, this relationship is not deterministic, and the fact that women’s socio-health status completely mediates the influence of public health provisions on malaria still speaks to the overall importance of women’s socio-health status in mitigating this deadly infection.

Although the environmental determinants of malaria are often emphasized (e.g. Bates et al., 2004; Norris, 2004; Sachs and Malaney, 2002; WHO, 2013; Williams et al., 2009), we find that the social environment (with regard to women’s standing) is in fact a stronger predictor of cross-national malaria rates than geographical location. In some ways, this finding illustrates that through improvements in gender equality, poor nations can potentially overcome some of their natural or environmental predispositions to this disease, and likely other tropical illnesses as well. Indeed, while we focus exclusively on malaria here, our results add to the growing body of literature that demonstrates the importance of women’s status for improvements in broad health measures (e.g. Austin and Noble, 2014; Brady et al., 2007; Medalia and Chang, 2011). It is probable that female empowerment contributes to patterns in other infectious diseases that primarily afflict children, such as dengue fever, dehydration, and pneumonia that have not yet been explored in the scholarly literature.

Our findings also reveal that the influence of economic development on malaria is important but indirect; economic development only reduces disease in so far as those resources are channeled to addressing women’s legal economic standing, public health provisions, and women’s socio-health status. This finding fits with emerging themes in comparative research that find social predictors to be more relevant than economic predictors in explaining cross-national health trends using direct effects approaches (e.g. Austin and Noble, 2014; Brady et al., 2007). This research demonstrates that economic development is not irrelevant or unimportant, but rather it operates indirectly, by enhancing social and political conditions which then reduce health disparities. In addition, we find that there is nothing inherent about Sub-Saharan African nations that leads to higher rates of malaria in this region. Rather, our findings suggest that the disproportionally high burden of malaria in Sub-Saharan Africa can be explained by issues of gender inequality and lower provisions of public health services in this region relative to others. The direct effects of GDP per capita, general health provisions, and Sub-Saharan Africa are often considered without hesitation in cross-national research on health outcomes; the results of this research indicate that future studies should be cautious when making claims of direct influence for these types of factors on health, unless the potential effects of pertinent mediating factors are properly taken into account.

We also found some unique indirect effects of latitude, where nations located nearer to tropical zones tend to have fewer health personnel. In addition to issues of poverty, tropical nations often are politically unstable and more dependent on natural resource extraction. These processes may contribute to a lack of formal health training as well as the ‘brain drain’, where the most educated and skilled people leave their home country for better opportunities elsewhere. Understanding the true mechanisms that contribute to this finding involving health-care professionals and latitude represents an area of needed further research.

This study contributes to a growing body of literature that emphasizes the importance of female empowerment in reducing infectious disease rates in less-developed nations (e.g. Austin and Noble, 2014; Brady et al., 2007; Burroway, 2012; Heimer, 2007; Shircliff and Shandra, 2011). Indeed, the degree to which dimensions of female empowerment predict total malaria prevalence is quite robust, given that the malaria prevalence outcome variable is not gender-specific. This finding signifies the importance of improving social conditions of women in less-developed nations and illustrates that improved health of women translates into improved health for the broader population. As women globally represent the primary caretakers of children, households, and even the broader community, enhancing women’s status translates into improvements in a potentially wide range of outcomes that are central to successful development.

It is important to acknowledge the limitations of this research. Data on gender-specific malaria rates, for example, female malaria prevalence, are not available; thus we were unable to utilize such a measure in the present analysis. However, use of the total prevalence measure here does illustrate the importance of female empowerment in improving general health measures cross-nationally, and our findings would have likely been even more robust with the availability of more nuanced malaria data. There is also a lack of cross-national data on women’s actual economic resources; while investigating the legal economic provisions of women begins to consider the influence of women’s economic standing, it would be valuable to assess if legal economic provisions facilitate improved access to income for women. Additional shortcomings of the research concern issues of data quality; cross-national estimates are never perfect, but we utilize the data from reputable sources (e.g. WHO, World Bank), which represent the best estimates available for the indicators we seek to study. Data on malaria rates may also be underreported, due to a lack of official diagnosis or persistent inequalities that prevent people, especially women, from seeking diagnosis and treatment in less-developed nations (Bates et al., 2004; Farmer, 2001).

New developments in the area of a malaria vaccine provide some optimism about combating this devastating disease. However, we must remain cautious about the potentials of medical breakthroughs. Social inequalities largely determine access to medical resources, including vaccines. The barriers identified here leading to gaps in successful malaria prevention and treatment represent the same inequalities that would prevent certain groups from accessing a new malaria vaccine. Thus, addressing issues of female empowerment in developing nations is still essential to reducing malaria rates and improving health. Biological susceptibilities cause pregnant women and children to be more prone to acquiring malaria; but the social and legal economic foundations of gender inequality also contribute to these trends. Gender stratification is most pronounced in poor nations, and among the most disadvantaged within these nations. Undoubtedly, addressing women’s social and legal economic status should be a dominant feature of global health and development policy in order to properly address this persistent plague.

Footnotes

Appendix 1

Table 4.

Regression estimates for SEM equations predicting malaria prevalence (ln).

Regression path	Coefficient	SE	Standardized coefficient
GDP per capita (ln) → Women’s legal economic rights	.097***	.030	.389
GDP per capita (ln) → Women’s socio-health status	.034*	.015	.243
GDP per capita (ln) → Health provisions	12.301***	2.231	.505
Sub-Saharan Africa → Women’s legal economical rights	−.194***	.056	−.411
Sub-Saharan Africa → Health provisions	−10.471**	4.088	−.229
latitude → health providers	.039***	.007	.459
Latitude → Malaria prevalence (ln)	−.111***	.018	−.425
Women’s legal economic rights → Women’s socio-health status	.099*	.062	.178
Women’s legal economic rights → Health provisions	28.897**	11.728	.297
Health provisions → Women’s socio-health status	.004***	.001	.634
Women’s socio-health status → Malaria prevalence (ln)	−13.558***	2.196	−.574
Women’s legal economic rights → Loan access	1.000^#	–	.785
Women’s legal economic rights → Land access	.99***	.153	.766
Women’s legal economic rights → Property access	.957***	.158	.712
Women’s socio-health status → Female/male schooling	1.000^#	–	.770
Women’s socio-health status → Low fertility	9.767***	1.226	.885
Women’s socio-health status → % Births attended	149.454***	21.974	.788
Health provisions → Secondary schooling	1.000^#	–	.931
Health provisions → Health providers	.025***	.004	.561
Health provisions → Improved sanitation	1.070***	.094	.868

GDP: gross domestic product; SE: standard error of estimation.

***

p < .001; **p < .01; *p < .05; #not applicable.

Acknowledgements

We would like to thank Bradly Fawcett and the anonymous reviewers at IJCS for their help, advice, and contributions to this research.

Funding

This research received no specific grant from any funding agency in the public, commercial, or not-for-profit sectors.

Notes

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