We expand on this prior work on infectious disease by testing whether this distinction might also be important for explaining common mental illness – a fundamental aspect of health that has been extensively demonstrated to show significant associations with poverty. The picture from decades of research from high-income countries is clear: worse socioeconomic status consistently predicts worse mental health outcomes, especially common mental disorders like anxiety and depression . The associations do not solely mean that conditions of poverty drives common mental disorders , but may also often feed each other syndemically in a “vicious cycle” . In higher income countries the onset, deterioration or relapse of mental illness in turn tends to increase economic risk and undermine wealth . The uncertainty of living with material poverty in itself is proposed to be stressful in ways that can trigger or heighten mental distress . This is explained in part by both poverty and female gender intersecting with many other related vulnerabilities – like under nutrition, low education, poor access to health services, chronic physical illness, gender-based violence and discrimination,vertical agriculture stigma/discrimination, or other forms of low social capital – that can heighten risks further .
In contrast, emerging research in low- and middle-income countries paints a more complex picture. Specifically, measures of material poverty, such as financial stress, food insecurity, income, and consumption expenditures, have shown surprisingly mixed associations with mental health in LMIC contexts . Of these, food insecurity tends to demonstrate the more robust associations ; income and expenditure less so . A number of reasons have been proposed for these inconsistent findings, including measurement issues and the argument that the everyday contexts and stressors of poverty are fundamentally different between higher and lower income countries in ways that matter for mental health .A commonly applied measure of wealth/poverty in research in LMICs is the Demographic and Health Survey wealth index. This indicator is mainly based on household assets that can be purchased in the cash economy . Using a statistical reduction technique, household items , quality of housing construction , and access to services are scaled into a single one dimensional index. This asset-based indicator has become the key variable used in LMICs to assess economic gradients in education , nutrition , physical health , mortality , and mental health . However, this uni-dimensional index really only captures household poverty through livelihoods associated with the cash economy . Importantly, too, these cash-economic goods or services are more easily accessible in urban areas; thus, they often depict rural settings as largely poor or deprived .
In countries or regions where agriculture plays a dominant role in many household economies, agricultural assets should fundamentally shape experiences of poverty. Most notably, availability of crops and animals for household consumption provides food security. Agricultural assets are also means of production and can contribute to the household income . Importantly, too, agricultural assets need not be held solely by, or provide benefit to, rural households. Peri-urban and even urban households owning even just a few animals or small plots of cultivatable land can produce small but valuable amounts of consumable or sellable food . For these reasons, agricultural wealth could provide a straightforward buffer against nutrition-related disease at the very least . Beyond such effects on nutrition and wealth, agricultural assets might also enhance social capital and status to provide further buffering effects for mental health. For example, in a Tanzanian community in which cattle ownership is prestigious, lack of ownership was found to predict mental distress: being without cattle meant one really could not belong in a society that viewed themselves as defined by their pastoralism and relationship to cows . In a study of livestock and animal assets in DRC, Glass et al. computed a total livestock asset score for rural women, finding that animal ownership had a moderating effect on depression symptoms. They proposed that ownership provided means to produce cash that could pay school fees, purchase land, and get materials to build/repair homes, but it was also potentially buffering via the social indexing of women’s productivity and status .
Similarly, cultivatable land ownership does not just reflect material wealth but also in some contexts lends the owner considerable power, status, and prestige . In spite of the potential for agricultural assets to buffer health risks, few empirical studies have considered these alternative dimensions of wealth in assessing the relationship between poverty and well being in low-income countries . Based on these multiple proposed mechanisms by which agricultural wealth might buffer vulnerabilities, we should also expect that greater household agricultural wealth could have a protective effect in relation to mental well being .Thus, in this study we consider how lack of agricultural assets – as a specific dimension of poverty – is associated with common mental disorder symptoms in Haiti. Our basic proposition is that household agricultural wealth will promote mental wellbeing – or buffer against depression and anxiety symptoms – with an effect evident beyond other commonly measured forms of material wealth, such as cash-economy wealth and food security. We analyze novel data from Haiti, considering how these relate within geographically randomly selected samples from three very different, but all highly vulnerable, communities. These contrast with each other in degree of rurality and direct access to and dependence on agricultural assets – a fully urban neighborhood, a fully rural zone, and a mid-sized town with a rural hinterland. Due largely to a complex history of foreign intervention, Haiti is the poorest nation in the Western hemisphere and one of the most economically unequal in the world, with high national dependence on the agricultural sector . Much of the rural farming is done on small plots by smallholder farmers, but making a living with small-scale farming is increasingly difficult given poor quality and lack of land, complex legal issues around proving land ownership, and vulnerabilities to natural hazards . These peyizan often balance multiple informal occupations; moreover, they can inhabit peri-urban and suburban zones, though most live in rural areas . The study communities reflect three particularly vulnerable sites within Haiti,vertical farming aeroponics all with high levels of food insecurity and significant material poverty . However, they differ substantially in agricultural wealth.
Martissant is a fully urban, densely-populated district of the City of Port-au-Prince where a minority of households surveyed own cultivatable land or animals . Ouanaminthe is a market border town with rural hinterland located across the Massacre River from the Dominican Republic, exhibiting a mix of subsistence and cash economy households ; Cornillon is a fully rural community in the West department with much higher rates of household cultivatable land and animal ownership . Additionally, both Ouanaminthe and Cornillon are municipalities, called Commune in Haiti, and have their own local administrative authority, an elected three-member mayoral council; while Martissant is a municipal district administrated by the City of Port-au-Prince . We surveyed 4055 households . Household sampling was powered so that each site would be able to detect an effect size of 0.15. The survey used a two-stage cluster sampling approach to select households. In the first stage, using the smallest census territorial entity called Dissemination Areas , all three sites under study were divided into clusters determined by the level of access to core services and central markets located in the main town or village. The level of access was measured based on two criteria, having an all season road and the distance from each DA to reach those core services. Four clusters were generated: accessibility very difficult, accessibility difficult, accessible, and very accessible. On the basis of probability proportional to size, a random sample of DAs was selected in each cluster for a total of 157 of 389 DAs in all three sites. Then, 25-26 households within each selected DA were selected in randomly generated sequence, while also allowing for over-selection of female household heads if needed to meet a 45% goal . The questionnaire was administrated in-person to the head of the selected households . Table 1 summarizes variables included in our analyses. We assessed mental well-being with locally adapted and/or validated depression and anxiety inventories. The Zanmi Lasante Depression Symptom Inventory assesses a combination of culturally adapted items from standard depression screeners and local idioms of distress . The ZLDSI was completed among a sample of 105 patients who also underwent diagnostic assessment by Haitian psychologists and social workers. Results were used to clinically validate the tool and identify cut-off scores for depression. The ZLDSI contains 13 symptom items, which respondents’ rate using a Likert scale from not at all to almost every day , based on frequency they occurred within the last 15 days. These were summed to provide total scores ranging from 0 to 39.
The Beck Anxiety Inventory was culturally adapted in a previous study in rural Haiti . Bilingual individuals provided initial translations of items, which were then discussed in focus groups. Participants commented on comprehensibility, acceptability, and relevance of each item, as well as recommending alternate wording. The Kreyòl BAI assesses experience of 20 anxiety symptom over the previous two weeks . Each question is scored from not at all to severe , yielding a possible range from 0 to 60. Our estimations of household wealth used a multidimensional approach . We included a wide range of household assets, household construction materials, access to basic services, and agricultural assets. Questions included vehicles and consumer goods; wall, roof and floor material; electrical access, sources of drinking water, toilet type; and ownership of livestock and land. All wealth related items were dummy coded . Those with more than two categories were recoded as a series of dummy variables. Count variables such number of livestock were ranged into categorical brackets before coding as dummy series . To derive wealth dimensions that are comparable with nationally representative surveys, we matched asset variables from the current survey to the Haiti Demographic and Health Survey and applied multiple correspondence analysis to the Haiti DHS household-by-variable matrix . These analyses identified two reliable dimensions of wealth/poverty, which accounted for 77.13% of the total dataset inertia. The first one, with 63.9% of the explained total, is strongly associated with variables such as having at least a TV, a radio, electricity, a cooker, internet services, or a bank account. We refer to this as our “cash economy wealth” measure. The second dimension is highly and clearly related to agricultural and subsistence assets, such as owning poultry or a boat , and we refer to this as our “agricultural wealth” measure. A third dimension solely related to latrine ownership and was discarded. Cronbach’s alpha showed good internal consistency for the two wealth dimensions: cash economy and agricultural . The first dimension was also highly correlated with the standard DHS wealth factor score produced using Principal Components Analysis , but the second dimension was not . This observed difference suggests that the agricultural dimension of wealth provides a distinctive means to characterize households in relation to each other. Then, using the DHS data, we estimated linear regressions predicting each of the two wealth dimensions from all asset variables in the DHS data that were also available in the current survey. This was facilitated by initial survey design aimed at maximizing overlap with DHS wealth index items, alongside additional wealth questions. Finally, we used those regression coefficients from the DHS data to estimate the two wealth dimensions for the current dataset based on each household’s assets. We also included food insecurity, water insecurity, income, financial stress, and household socio-economic status as key covariates likely highly correlated with household assets. A global analysis of over 145 countries shows household food insecurity is consistently associated with poor mental health in a dose-response pattern . While there is less direct evidence, household water scarcity also shows an association with anxiety and depression symptoms, with women most affected . To take this into account in our modeling, we applied the Household Food Insecurity Access Scale to assess household food insecurity . The HFIAS asks how often during the past two weeks was there: no food to eat of any kind in your house because of lack of resources to get food, any household member went to sleep at night hungry because there was not enough food, and if any household member spent a whole day and night without eating anything at all because there was not enough food.