Sampler 4 Dealing with Depression Naturally Series

It is possible that public opposition may hinder any lithium mining operation in Ireland [ 34 ]. There is a need to measure baseline levels of lithium in the natural waters of the Carlow area prior to any mining operation. Natural waters refer to both surface SW and groundwater GW.

These specific transect lines did not represent any significant geological features but were rather a numerical split of the area taking into account existing road access. This sampling method was chosen to sample the whole county in a cost-efficient way. This method also allowed us to address the question: Carlow, showing locations of transect sampling lines.

The study was conducted over a period of seven months during , from March to September. Water samples were collected every two months four sampling events from a total of sampling sites, 80 of which were GW and 35 SW, giving a total of water samples Figure 3. GW samples were taken directly from household taps that were connected to private boreholes. SW samples were taken from streams and rivers along and adjacent to transect sampling lines.

All bottles were acid washed with dilute HNO 3 , then filled with the dilute acid and allowed to sit for approximately one day. Bottles were then rinsed several times with deionized water and placed in sealable bags to prevent any contamination. Parts per billion measurements require rigorous cleaning and sampling methods; because of this, only ultra-pure analytical grade acids were used in this study. Samples collected in the field were filled to the brim leaving very little air space at the top of the bottle. Physico-chemical measurements pH, Temperature and Conductivity were taken in the field prior to filtering or acidification.

Samples were filtered within 24 h of collection and then acidified. The samples were acidified to arrest any biological activity; dissolve any precipitates present; and discourage the adsorption of lithium onto the walls of the bottles. Trace metals are particularly prone to adsorption. Carlow, showing bedrock geology, transect sampling lines, areas of interest and approximate sampling point locations. Sample bottles were rinsed three times with the water being sampled. To avoid non-representative samples caused by surface films and the entrainment of river bottom deposits, a grab sampling method was used.

Samples were taken from the center of rivers below the surface of the water while the sampler stood downstream of the sampling point. In some cases, i. Only sites which had their own GW well system installed were selected for the study. Care was also taken to avoid any water softening system or other treatment between the GW source and the tap. Prior to sampling from taps, the water was left to flow for two minutes before a sample was taken. When collecting GW samples, the container was rinsed three times with the water to be sampled before taking the final sample. Samples were also taken as close as possible to the source of the supply to minimize any potential influence from the plumbing system.

All water samples and blanks underwent the same preparatory procedures. Firstly, samples were concentrated by a factor of 10 by evaporation mL samples were concentrated down to 20 mL before being analyzed. This ensured that the naturally low lithium levels in our samples would be measured at an order of magnitude greater than they appear naturally and therefore be more easily detected by our instrumentation. Further to this, samples were filtered using 0. These filters trap particles both on the surface of the filter and within the filter; consequently, the retention of small particles increased as the filter became more loaded.

Samples were filtered avoiding excessive pressure. Algal cells are known to concentrate trace metals, so rupture of an algal cell could cause inaccurately high results; rupture may also introduce natural chelating agents into the water. Ph Eur to the same percentage as collected samples i. Glassware was thoroughly soaked in dilute HNO 3 and rinsed several times with deionized water before use. No other metals in the analysis required an ionization suppressant. All reagents used were commercially available from Sigma Aldrich Ireland Ltd. Vale Road, Arklow, Wicklow, Ireland.

These instruments provided sufficient sensitivity, short analysis time and level of accuracy required for the study. Samples were concentrated by a factor of 10 prior to any instrumental analysis, therefore a concentration factor of 10 was applied to all data before reporting the result. Processed samples were drawn at random before being read on the AAS to minimize procedural bias.

After every 50 samples, the instrument was recalibrated using blank samples and working standards. Typical readings obtained from blank samples were 0. Speciation of the lithium and the other metals analyzed in the water was not a factor in this study; only total dissolved metal content was measured. Alkali metals are very susceptible to ionization. In an air-acetylene flame, lithium ionization is appreciable. To adjust the data to normality and allow the use of parametric tests, all data underwent a logarithmic transformation Figure 4.

Where significant differences were found, a post-hoc t -test was used to identify significant differences between sample means. A p -value of 0. Bonferroni corrections were used as appropriate. Histogram of SW and GW lithium concentration data. One sampling point was selected from which 20 water samples were taken: In total, 80 GW sites located along each of the five transect sampling lines were sampled four times giving a total of observations.

In total, 35 SW sites located along each of the five transect sampling lines were sampled four times from which a total of observations were made. A correlation analysis was carried out to assess whether there was any association between lithium and the physico-chemical variables pH, Conductivity and Temperature.

The mean lithium concentration in SW and GW appeared similar. There was no statistical difference between lithium levels in SW and GW. Bimonthly sampling took place during with four different sampling events March, May, July and September. One peculiar observation in the data can be seen in Figure 6. The variation in lithium levels in March and July appears similar as is the data in May and September. Boxplots of SW and GW lithium concentration data for each month. Initially, we suspected that this relationship could be explained by procedural bias. Before any analysis began, all SW and GW samples were mixed together.

During the analysis, samples were selected randomly from this mix. Selecting samples using this method should have effectively negated the possibility of any procedural bias. Rainfall data for the area at the time of sampling did not correlate with the measured lithium concentrations; the above pattern was not present March To mitigate any diluting influence, sampling only took place two to three days after significant precipitation. We also considered that agricultural activities could have been responsible for the pattern.

In the South-East, fertilizer application is prohibited from 15 September to 12 January [ 38 ]. In Ireland, farmers tend to apply fertilizer in the spring February to April. At any time during our sampling, fertilizer spreading may have taken place. It is impossible to know when farmers spread fertilizer during our sampling, but we cannot ignore the possibility that agricultural activities may be responsible for the pattern.

K fertilizers Nitrogen, Phosphorous and Potassium. There is also one landfill site located within Carlow Powerstown Landfill and Recycling Centre approximately 8 km South of Carlow town. This is almost exactly midway between transects 3 and 4 Figure 2 and is thus unlikely to directly impinge on our measurements. Data that we have collected for other alkali metals analyzed during the same time do not display the same pattern. We do not observe the same pattern in our K data Figure 7 and Figure 8. There is heterogeneity in the K and Na data but it is a different pattern than in the lithium data.

Boxplots of SW and GW potassium concentration data for each month. Boxplots of SW and GW sodium concentration data for each month. Nevertheless, the pattern of variation among months is different for the three elements, so we conclude that the heterogeneity is not due to procedural bias. We are currently unable to propose a mechanism to explain the observed pattern in the lithium data. Lithium enrichment is often expressed by the lithium to sodium ratio which is used as an indicator of residence time within an aquifer.

This contrasts with the comparisons among months. Boxplots of SW and GW lithium concentration data for each transect. The lithium bearing pegmatites are located along the Eastern border of the county; the Western border of the county is marked by the river Barrow valley. One of the questions we sought to answer was whether the levels of lithium are higher in the East than in the West Figure This hypothesis was based on the highly mobile nature of the lithium ion and the weathering over time of the lithium bearing pegmatites.

Dividing our data into West, Central and East data bins the lithium bearing pegmatites being in the East of the county and then conducting a t -test on the logarithmic transformed data—with the null hypothesis being that there was no difference between East and West lithium means—the following observations were made: Bedrock geology map of Ireland and county Carlow showing general location of lithium bearing pegmatites.

Image modified from [ 33 ]. The results are listed in Table 1.

Introduction

The Irish EPA carries out routine water quality analyses all over Ireland, the data from which are publicly available [ 32 ]. These metals were chosen because they represent a common set of metals associated with water quality and to determine whether there were any significant correlations between lithium and the other metals. When a correlation analysis was carried out to assess this, no significant correlations were observed.

It is worth noting that these figures are associated with studies investigating an association between lithium levels in drinking water and suicide rates in the local population. Some of these studies also claim a positive correlation among lithium levels, longevity and general mental health. The theory is that lithium in trace amounts enhances the connectivity among neurons and exposure over a lifetime enhances happiness [ 41 ]. However, data for Ireland, let alone Carlow, in relation to suicide are not currently available at sufficient geographical granularity to allow an investigation of potential associations between suicide rates and lithium levels in drinking water.

If the lithium from the known pegmatite sources is being slowly eroded and making its way down into the Barrow valley, we should have measured an elevated amount of lithium in our samples. The passage of water through the pegmatites is low. They are situated at an altitude above the level of the local water table and are unsaturated. The pegmatites also occur within a poor aquifer.

The low concentrations found appear to be consistent with limited dissolution of the pegmatites. When a one-sample t -test assuming unequal variances was carried out on our data—the null hypothesis being that the population mean was 0. Neither recent nor historic prospecting have uncovered any cases of exposed pegmatite-bearing bedrock in the area. Pegmatites are very similar in composition to granite in that they both are susceptible to weathering. However, spodumene LiAl SiO 3 2 is an aluminosilicate mineral, thus the leaching from its lattice structure is very slow.

This fact, the lack of exposed bedrock outcrops in the area and the fact that the pegmatite deposits are unsaturated may account for the low levels of lithium in our samples. Some of the compounds that lithium forms in nature such as its fluoride, carbonate and phosphate compounds also have very low solubility in water [ 42 , 43 , 44 , 45 ]. Lithium occurs at very low levels in water, and this elusiveness means that it is also an inherently difficult element to quantify.

This research offers a snapshot of the lithium levels within the natural waters of the South East of Ireland. As with other studies, we have found low levels of dissolved lithium in natural waters but with significant heterogeneity through the year. This emphasizes the importance of repeated sampling to establish a true measure of lithium at a given site. From our work, we suggest that the following mean lithium values be used to establish baseline concentrations of lithium levels in the natural waters of the region: These data establish a reference concentration for lithium in the natural waters of the area prior to any mining activity.

The study may also be useful for other purposes: Our analysis indicates that, undisturbed, the lithium-rich pegmatites of the Blackstairs have negligible effect on lithium concentration in local watershed. Although evidence supports the contention that psychiatric nosology is shaped by sociocultural and political-economic context, e. We chose these disorders because the average age of onset relative to other mood and anxiety disorders is later after age Kessler and Berglund et al.

Canino and Bravo et al. Diagnoses were further validated using the SFv2, a mental disability score, in controlled linear regressions Grant and Stinson et al. We categorised classes as owners, managers, supervisors, and workers. The 90 th percentile was chosen because it clearly separates capitalists from small employers and the petty bourgeoisie, but is still obtainable by workers, managers, and supervisors in a variety of occupations.

A sensitivity analysis using a different income cutoff is described below. A sensitivity analysis with no education proxy is discussed below. Workers comprise respondents who identified their occupation as private household; other services; farming, forestry, and fishing; operators, fabricators, and labourers; transportation and material moving; or handlers, equipment cleaners, and labourers. Sensitivity analyses using additional occupations to represent workers are discussed below. We tabulated the prevalence of any lifetime and month depression and anxiety by class categories, first restricting to the private sector and then including all sectors.

We also tabulated depression and anxiety by income and education in the full sample. We constructed bivariate and adjusted logistic regression models one for each of lifetime and month depression and anxiety as the outcomes to determine the odds of disorder across classes, in the private sector and all sectors. Adjusted models include sex, age, ancestry group, and metropolitan statistical area. The study design oversampled harder to reach groups, thus sample weights were incorporated to generate estimates that are nationally representative of the demographics of the United States based on the census.

Further, design weights were incorporated to account for the stratified complex sampling strategy. Standard errors were estimated using Taylor Series Linearization. We performed four sensitivity analyses on our class measures. We wanted to ensure that our results were not contingent on our education proxy for managers and supervisors, our income cut-off for owners, or our choice of worker occupations. In our first sensitivity analysis, we collapsed the manager and supervisor categories by removing the education proxy and removed the income cut-off for owners.

In our next two sensitivity analyses, we utilised our original class categories but systematically added occupations to the worker category. Therefore, in our second sensitivity analysis, we constructed an 8-occupation worker category that included sales; administrative support including clerical; private household; other services; farming, forestry, and fishing; operators, fabricators, and labourers; transportation and material moving; and handlers, equipment cleaners, and labourers.

In our third sensitivity analysis, we constructed a occupation worker category that included professional specialty; technical and related support; sales; administrative support, including clerical; private household; protective services; other services; farming, forestry, and fishing; precision production, craft, and repair; operators, fabricators, and labourers; transportation and material moving; and handlers, equipment cleaners, and labourers.

Table 1 presents prevalence estimates of lifetime and month depression and anxiety by class and sector. With few exceptions and as hypothesised, contradictory class locations display the highest prevalence of depression and anxiety relative to non-contradictory class locations.

In the private sector and all sectors, supervisors have the highest prevalence of all disorder categories. For lifetime disorders, managers have the next highest prevalence, while owners had similar or lower prevalence than workers. For month disorders, the pattern is less consistent for depression but consistent for anxiety. Workers identified their occupation as private household; farming, forestry, and fishing; operators, fabricators, and labourers; transportation and material moving; or handlers, equipment cleaners, and labourers.

Odds ratios from bivariate logistic regression models show modest to strong effects of contradictory class location in the hypothesised direction Table 2. For example, in the private sector, relative to workers, supervisors had higher odds of lifetime depression OR: Compared to workers, managers had the next highest odds of lifetime depression 1. The pattern was identical, though the odds ratios slightly smaller, when the sample comprised all sectors. For the private sector, when owners form the reference group data not shown in table , supervisors have the highest odds of lifetime depression 1.

Bivariate and adjusted odds of depression and anxiety among managers, supervisors, and owners relative to workers, private and all sectors. Adjusted models control for age, sex, ancestry group, and metropolitan statistical area. Adjustment for age, sex, ancestry group, and metropolitan statistical area did not change the pattern of findings comparing supervisors to workers for any outcomes except month depression; however, the magnitude of the effect of contradictory class location was weaker.

The estimates for managers relative to workers were also reduced or null in some cases. Comparing supervisors to owners data not shown in table , supervisors had higher odds of lifetime depression 1. There was no effect for month depression 0. Managers and workers had similar odds of all disorders relative to owners. Sex and ancestry group were the primary drivers of the reduced adjusted effects. Table 3 shows the distribution of depression and anxiety across traditional measures of SES: As expected, income displays a negative linear relationship with lifetime and month depression and anxiety, with some exceptions in the highest income category.

Educational attainment shows a nonlinear prevalence pattern that varies by disorder.

In general, individuals who do not complete an educational milestone e. Prevalence of depression and anxiety across traditional measures of socioeconomic status. Depression includes major depression. Anxiety includes generalised anxiety and panic disorder. In the United States, high school includes grades 9— GED, or General Educational Development, is a series of tests demonstrating skills equivalent to completion of high school.

We also hypothesised that the addition of occupations to the worker category would dilute but not nullify our findings, given the likely misclassification it would introduce. Reclassifying workers to include 8 occupations showed reduced, but significant, effects of contradictory class location. Supervisors had higher odds of lifetime depression, lifetime anxiety, and month anxiety relative to workers Appendix Table B. Managers had the next highest odds relative to owners for lifetime disorders data not shown. The effect of contradictory class location was further diminished, but not eliminated, after reclassifying the worker category to include 12 occupations.

Odds ratios show that relative to workers, supervisors maintained higher odds of lifetime depression, lifetime anxiety, and month anxiety Appendix Table B.

Relative to owners, managers and workers showed similar odds of all disorders. Finally, using our original class categories but reducing the income cut-off for owners did not alter the pattern of our findings Appendix Table B.

Relative to workers, supervisors maintained the highest odds of all disorders, followed by managers. Relative to owners, supervisors had the highest odds of all disorders, followed by managers, who had the next highest odds for all disorders except month depression. Using a relational measure of class, we found that individuals who occupy more contradictory class locations have higher prevalence and odds of depression and anxiety than individuals in less contradictory class locations.

These findings confirm results from prior studies that used measures of class based on the same neo-Marxian theory employed here Muntaner and Eaton et al. Regarding stress theory, our findings suggest that class relations may structure exposure to depressogenic and anxiogenic occupations, and that stresses from workplace domination and exploitation may extend beyond those associated with the relative material disadvantage that is a consequence of domination and exploitation. While social stress theory does not preclude a diversity of stressors and stress reactions that have different effects on physical and mental health, our findings imply that the social distribution of some labour-related stressors may not fit a stratificationist framework that views said distribution linearly, along a social gradient e.

Contradictory class locations may entail greater exposure to exogenous stressors consistent with the job strain model.

Lithium in the Natural Waters of the South East of Ireland

Thus, it may not only be the social position that one occupies, but how one came to occupy and ascribe meaning to it, that has implications for depression and anxiety. Research has consistently shown that external attributions are protective against low self-esteem and internalising disorders such as depression Abramson and Metalsky et al. The notion that the subjective meaning of social location is important for mental health is also suggested by research on subjective social status SSS.

Using data from the Whitehall II study of British, white-collar civil servants, researchers found that SSS was a strong predictor of depression after controlling for traditional measures of SES such as income, occupational grade, and education Singh-Manoux and Adler et al. Our findings provide evidence for the effects of social structure e.

Lithium in the Natural Waters of the South East of Ireland

Lynch and Smith et al. Furthermore, our findings are consistent with recommendations for redistributive policies and workplace democracy stemming from studies that tested hypotheses about other psychosocial mechanisms e. That said, a relational class perspective is consistent with evidence that interventions at the level of the workplace environment are minimally effective, because meaningfully changing conditions such as worker autonomy requires changes to the very structure of wage labour, i. The present study is limited by its reliance on proxies for capital assets, skill, expertise, and authority that define contradictory class locations in relation to production.

To our knowledge, no large, representative population data exist in the United States in which such hypotheses can be tested directly. In addition, we were unable to test directly whether workplace exposures e. If class relations structure access to occupations with varying degrees of direction, control, and planning, this may explain the increased odds of depression and anxiety for managers and supervisors.

We hope the present study underscores the importance of further pursuing this line of inquiry.

1. Introduction

Nonetheless, our proxies support the hypothesis that individuals in contradictory class locations are at greater risk for depression and anxiety than individuals in non-contradictory locations. This is because 1 despite a weakening of effect, findings remained in same direction; and 2 the reduction in the magnitude of the effect of contradictory class locations conforms to our theoretical framework. In other words, we added nondifferential misclassification to our categorisation that we would expect to bias our results toward the null.

This is because, despite income cut-offs, the owner category likely included the singularly self-employed and small employers who may face work-related stresses more similar to managers and supervisors than the traditional capitalist class. This may also explain why owners sometimes had higher prevalence and odds of disorder than workers. We also analysed NESARC data cross-sectionally, thereby limiting causal inference about the temporality of the association between class location and disorders. Regarding comparative methods in the measurement of socioeconomic position in social and psychiatric epidemiology, our findings illustrate the need—as articulated by others Krieger and Williams et al.

This is not to say that traditional measures of SES such as income and education are not important and meaningful constructs for certain questions, but rather to suggest that understanding how socioeconomic position drives health outcomes is more complex than any single measure can convey. More precise measures of class, via occupational skills, expertise, and authority already exist from the U.

Other constructs may be more difficult to routinely measure: Such measures would provide objective, quantitative counterparts to psychosocial measures discussed above and represent an under-explored line of inquiry into the relationship between class and mental health.

The findings of the present study allude to broader social problems that are the result of the political-economic arrangements of post-industrial capitalism in the developed world, characterised by deregulation, privatisation, capital mobility, the dismantling of trade unions and other working-class institutions, the withdrawal of the state from social provision, and domestically, the replacement of the manufacturing sector with the service sector Harvey The health consequences of these trends have been reviewed extensively elsewhere e.

As we have not tested hypotheses about such forces directly, the purpose of the present discussion is to emphasise the need for population health research to explicitly acknowledge the political-economic context in which quantitative measures of socioeconomic inequality are situated and engage openly with the theoretical framework that informs whatever operationalisation is chosen. We documented how the political-economic arrangements that give rise to SES may affect depression and anxiety via relational class mechanisms in a nonlinear, non-gradational fashion.

Our findings suggest that class processes such as domination and exploitation warrant explicit attention in social and psychiatric epidemiology. The authors thank Dr. Sharon Schwartz for invaluable comments on an earlier draft of this paper. Prevalence of lifetime and current depression and anxiety across three class locations, no education proxy. Sensitivity analyses for the odds of depression and anxiety among managers, supervisors, and owners relative to workers, all sectors.

National Center for Biotechnology Information , U. Author manuscript; available in PMC Nov 1. Prins , 1 Lisa M. Bates , 1 Katherine M. Keyes , 1 and Carles Muntaner 2. Author information Copyright and License information Disclaimer. The publisher's final edited version of this article is available at Sociol Health Illn. See other articles in PMC that cite the published article.

Abstract Despite a well-established social gradient for many mental disorders, evidence suggests that individuals near the middle of the social hierarchy suffer higher rates of depression and anxiety than those at the top or bottom. Social class, epidemiology, mental health and illness, social determinants of health. Introduction Social disadvantage is associated with higher risk of most adverse mental health outcomes Dohrenwend and Dohrenwend ; Dohrenwend ; Faris and Dunham ; Hollingshead and Redlich ; Muntaner and Ng et al.

Relational Theories of Class Stratificationist conceptualisations of socioeconomic disadvantage provide the theoretical foundations for traditional measures of SES. Sequelae of Class Relations? Contradictory Class Locations and Occupational Control Presumably, as one moves down an organisational hierarchy relative to owners, the more one encounters stress and adversity due to exploitation, alienation, and exposure to poor working conditions. Analysis We tabulated the prevalence of any lifetime and month depression and anxiety by class categories, first restricting to the private sector and then including all sectors.

Sensitivity analysis We performed four sensitivity analyses on our class measures. Results Table 1 presents prevalence estimates of lifetime and month depression and anxiety by class and sector. Table 1 Prevalence of lifetime and current depression and anxiety across class locations. Open in a separate window. Table 2 Bivariate and adjusted odds of depression and anxiety among managers, supervisors, and owners relative to workers, private and all sectors.

Table 3 Prevalence of depression and anxiety across traditional measures of socioeconomic status. Discussion Using a relational measure of class, we found that individuals who occupy more contradictory class locations have higher prevalence and odds of depression and anxiety than individuals in less contradictory class locations. Acknowledgements The authors thank Dr. Worker category includes private household; farming, forestry, and fishing; operators, fabricators, and labourers; transportation and material moving; and handlers, equipment cleaners, and labourers. Owners comprise respondents who identified as self-employed.

Worker category includes sales; administrative support, including clerical; private household; other services; farming, forestry, and fishing; operators, fabricators, and labourers; transportation and material moving; and handlers, equipment cleaners, and labourers. Worker category includes professional specialty; technical and related support; sales; administrative support, including clerical; private household; protective services; other services; farming, forestry, and fishing; precision production, craft, and repair; operators, fabricators, and labourers; transportation and material moving; and handlers, equipment cleaners, and labourers.

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The Medicalization of Mental Disorder; pp. Davis K, Moore WE. Some Principles of Stratification. Socioeconomic status SES and psychiatric disorders Are the issues still compelling? Social Psychiatry and Psychiatric Epidemiology. Socioeconomic status and psychiatric disorders: Social status and psychological disorder: The Division of Labor in Society. Simon and Schuster; Socioeconomic status and depressive syndrome: Journal of Health and Social Behavior. Socioeconomic differences in cardiometabolic factors: American Journal of Epidemiology. Mental Disorders in Urban Areas: An Ecological Study of Schizophrenia and other Psychoses.

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University of Chicago Press; Indicators of socioeconomic position part 1 Journal of epidemiology and community health.