Open Access
Original Article, Biomed Biopharm Res., 2022; 19(1):42-57
doi: 10.19277/bbr.19.1.282; pdf version here [+] ; Portuguese html here [PT] 

 

Food insecurity in the households of the Algarve

Ezequiel Pinto 1,2*, Filipa Guerreiro 1,2,3, Artur Gregório 3, Maria Palma Mateus 1,2

1Universidade do Algarve, Campus de Gambelas, Edifício 1, Piso 1, 8005-139, Faro, Portugal; 2Escola Superior de Saúde - Universidade do Algarve, Campus de Gambelas, Edifício 1, Piso 1, 8005-139, Faro, Portugal; 3Associação In Loco, Av. da Liberdade 101, 8150-101 São Brás de Alportel, Portugal

* corresponding author: This email address is being protected from spambots. You need JavaScript enabled to view it.

 

Abstract

The National Health Directorate considered that an assessment of food insecurity was a priority for the Algarve region. This study reports the results of the Regional Observatory for Food Security, which aimed to identify socioec34ewonomic determinants, lifestyle risk factors, and prevalence of food insecurity in the Algarve.

We conducted a cross-sectional study in a random, stratified, sample of households in the Algarve, with data regarding sociodemographic, anthropometric, food insecurity, access to places where food is sold, and Mediterranean diet adherence variables, collected through a direct interview conducted to a representative of the household. Data analysis was conducted with the IBM-SPSS software, version 22.

Approximately 24% of households had mild food insecurity, 3% had moderate food insecurity, and 2% had severe food insecurity. Only 25% of participants had good adherence to the Mediterranean Diet. Unemployment in the household (rSpearman=0.116; p<0.05), smoking habits (rSpearman=0.193; p<0.05), and low adherence to Mediterranean Diet (χ2= 6.7; p=0.01) seem associated with greater food insecurity. An odds ratio analysis shows that having a higher education degree can be a protective factor for food insecurity (OR=0.78; 95%IC 0.66-0.92).

Although further studies are needed in order to assess in detail the determinants of food insecurity, this work can contribute to tailor food and nutrition interventions in the region.

 

Keywords: Algarve; Food Insecurity; Mediterranean Diet

Received: 30/03/2022; Accepted: 01/06/2022

 

Introduction

The Food and Agriculture Organisation (FAO) describes food security (FS) as a situation in which all people, at all times, have permanent physical, social, and economic access to sufficient, safe and nutritious food to meet their dietary needs and food preferences for an active and healthy life (1). This concept encompasses four distinct dimensions that must occur simultaneously: the availability of food, physical and economic access to food, the stability of food, and the use of food in the diet.

Food insecurity (FI), the opposite of FS, is defined as a situation in which a consumer's physical, social and economic access to nutritionally adequate food is scarce or non-existent. It is a global public health problem, gaining expression on political health agendas worldwide, highly prevalent in developing countries, but also a concern in countries with developed economies (2). It is associated with unhealthy eating habits, obesity, and non-communicable diseases (NCD), in addition to conditions of undernourishment and undernutrition (3,4).

According to FAO, in 2017, about 821 million people were affected by malnutrition or chronic food shortages, and the average prevalence of severe FI was 9.2% globally and 1.6% in the European Union (5). Non-communicable chronic diseases account for 38 million or 70% of deaths worldwide, with 28 million of all deaths occuring in low- and middle-income countries, and 82% of 16 million premature deaths (6).

The Mediterranean diet (MD) has been shown to be a healthy dietary pattern, associated with a lower risk for several NCD (7). It is characterized by a high intake of local and seasonal healthy plant foods, and a moderate intake of red meat and dairy (7,8).

MD is not only associated with nutritional and health benefits but also with economic and environmental benefits that can contribute to halving (at minimum) greenhouse gas emissions from the food system compared to current western diets (7–9).

Issues related to the sustainability of the diets, environmental impact of food systems, and climate changes are increasingly relevant, as they contribute to aggravating the vulnerability and food insecurity of populations (6,9). However, a decreasing trend in adherence to MD in Mediterranean populations has been observed (10).

In Portugal, the scientific literature on this subject is scarce. Between 2011 and 2014, the General Directorate of Health (DGS) coordinated a nationwide study - INFOFAMÍLIA study - which aimed to contribute to the improvement of the methodology to evaluate FI and to assess FI in a representative sample of households in mainland Portugal.

Between 48.5% and 50.7% of households were classified as being in a status of FI. Higher rates were identified in the regions of Algarve and Lisbon and Tagus Valley (11).

Following these results, the DGS considered the assessment and monitoring of FI in the Algarve region a priority, which led the IN LOCO Association to develop a pilot project in the region, aimed at assessing and monitoring the state of FI in the Algarve population, and at implementing a “Regional Observatory for Food Security”.

This project was funded by the DGS and ran between August of 2017 and August of 2018. This paper reports some of the data collected by the Regional Observatory for Food Security and aims to describe FI in the Algarve and analyse associated socio-economic and lifestyle determinants.

 

Materials and Methods

Population and sample

The Algarve is a coastal region in the south of Portugal, with around 467,500 permanent inhabitants, and incorporates 16 municipalities, subdivided into 67 counties. Based on demographic statistics for the region and following the methods proposed by Cohen (12), we estimated that a minimum of 199 households were needed to construct a representative sample of the prevalence of FI reported by the INFOFAMÍLIA study (11) for the region, considering a sample power of 80% and a statistical significance of 0.05. With information provided by the municipalities and demographic data for the region, we constructed a random sampling of households, stratified by categories of populational density. We set the minimum number of households (to be participate) from each county at 5 in order to assure representation. This would imply a minimum of 335 households to represent the region. We randomly selected and contacted 400 households. A total of 384 representatives of the household were willing to be a part of this study.

Data collection

A survey was conducted through direct interview of one representative for each household, a habitual resident (tourists and temporary residents were not considered as valid representatives of the household), aged 18 years old or older.

The interview script consisted of a structured questionnaire, based on the survey tool of the INFOFAMILIA study, and was composed of four sections: 1) sociodemographic and anthropometric characterization; 2) FI scale; 3) questions regarding alcohol intake and regarding conditions of accessing places where food is sold; and 4) adherence to the Mediterranean dietary pattern (MDP).

The FI scale used in the INFOFAMÍLIA study (11) and reproduced in this study was proposed in 2014 by Gregório et al. (13), based on the “Brazilian Scale of Food Insecurity” (14), originally developed by the United States Department of Agriculture (15). This scale is composed of 14 close-ended questions and provides a final score between 0 and 14 points, which allows the classification of FI in three levels: Mild FI (1 to 5 points in households with underaged elements or 1 to 3 points in households composed exclusively by adults) – Concern or Uncertainty as to future access to food or as to inadequate food quality resulting from strategies aiming at not compromising the quantity of food; Moderate FI (6 to 9 points in households with underaged elements or 4 to 5 points in households composed exclusively of adults) – Quantitative reduction of food among adults or disruption in the dietary patterns resulting from lack of food among adults and Severe FI (10 to 14 points in households with underaged elements or 6 to 8 points in households composed exclusively of adults) – Quantitative reduction of food among children or disruption in eating patterns resulting from the lack of food among children; famine (when someone goes an entire day without eating due to lack of money to purchase food).

The assessment of adherence to MDP was performed using a Portuguese version of the Mediterranean Diet Adherence Screener scale (MEDAS) used in the PREDIMED study (11). This scale presents 14 questions to which a score is attributed. Total score can vary between 0 and 14, and scores equal to or greater than 10 are considered indicative of good adherence to MDP (16). For use in Portugal, the items of the Brazilian version were adapted in their spelling and grammar by a panel of experts and pre-tested.

Self-reported weight and height were used to calculate the body mass index (BMI) of the participants, which was categorized in order to classify nutritional status according to the World Health Organization criteria: BMI < 18,50 kg/m2 represents “Low weight”, BMI between 18,50 and 24,99 kg/m2 represents “Normal weight”, BMI ≥ 25,00 kg/m2 represents “Overweight” and where BMI ≥ 30,00 kg/m2 represents “Obesity” (17).

This study was submitted to and approved by an institutional Ethics Committee and all ethical issues underlying this type of study were followed, in accordance with the principles established by the Declaration of Helsinki (18), as well as all appropriate rules of confidentiality and data protection. The study design, the procedures for anonymity and confidentiality, the informed consent, and all survey methodology was analyzed by the jury of the application process for funding. The application resulted in authorization for the study and funding, granted by the DGS.

Statistical analysis

The data collected in this study were used to build a database in Microsoft Office Excel 2007® and Statistical Package for the Social Sciences (SPSS), version 22®.

Descriptive statistical procedures were used, and mean values and standard deviations were calculated for quantitative variables; absolute and relative frequencies were determined for each category of nominal and ordinal variables, considering the total number of valid answers. Data were also summarized using tables.

The Kolmogorov-Smirnov test was used to verify adherence to the Normal distribution for the variables used in statistical inference tests and, according to the results of the test for adherence to the Normal distribution, the Student’s t-test was used for comparison of two groups in variables with a distribution considered Normal and the Mann-Whitney test in variables with another distribution. The associations between variables with Normal distribution were studied through Pearson's correlation coefficient and Spearman's correlation coefficient was used as its non-parametric equivalent.

The chi-square test was used to analyse associations between qualitative variables and the unadjusted association between the variables under study, and AI status was studied by calculating the odds ratio (OR) with 95% confidence interval (95%CI), estimated through univariate logistic regression. A significance of 0.05 was considered as an indicator of statistically significant differences or associations in all the tests conducted.

 

Results

The final sample was composed of 384 participants, 24% (n=91) male and 76% (n=293) female, aged between 18 and 97 years (M=56.4; SD=17.76). The most common formal education level of the participants was the first level of basic education, corresponding to up to four years of schooling (31.3%; n=120). Most of the households (38.2%; n=146) were composed of only two people and more than half of the households (55.7%; n=214) did not include individuals over 65 years old.

Using BMI as an indicator of nutritional status, 40.6% of participants were overweight and 16.8% of participants were classified as obese.

Most participants reported that they usually buy food for the household (90.9%), cook meals (85.4%) and use olive oil to do so (97.7%).

Approximately 29% of participants (n=113) had some degree of FI and 2.1% (n=8) had severe FI. The results of the items of the FS scale and the prevalence of FI are presented in Table 1.

FI appeared to be associated with a decrease in the consumption of essential foods (X2=64.2; p<0.001), the purchase of medicines (X2=36.6; p<0.001), the number of visits to the doctor (X2=31.7; p<0.001) and the number of meals taken outside the home (X2=48.8; p<0.001). Also, 16.6% (n=45) of respondents with FS reported having decreased the number of meals away from home in the three months prior to the interview time.

When analysing the differences in FI according to socio-demographic and anthropometric characteristics (Table 2), a statistically significant positive correlation was observed between the FI scale and the number of unemployed members of the household (rSpearman=0.116; p<0.05) and between the FI scale and the number of smokers in the household (rSpearman=0.193; p<0.05). Thus, unemployment in the household and the existence of smoking habits seem to be associated with higher FI.

The odds-ratio (OR) for FI was 2.22 (95% CI between 1.39 and 3.55) in smokers, when compared with that of non-smokers (Table 3).

The level of education, more specifically having a completed a higher education degree, seems to be a moderate protective factor for FI. Participants with higher education have an OR of 0.78 (95% CI between 0.66 and 0.92) for FI when compared to participants who have only completed primary education.

Curiously, there was also a favorable OR for FS in individuals who consume alcoholic beverages compared to non-consumers (OR=0.51; 95%CI between 0.32 and 0.79).

Only 24.7% of the participants achieved a score of 10 or more points in the PREDIMED scale and, thus, have a good adherence to MDP (Table 4).

Even though overall adherence to MDP is low, participants with some degree of FI have lower adherence (p=0.01) and lower median and mean values on the PREDIMED scale score (U=11474.5; p<0.001) (Table 5).

 

Discussion

Research on FI has been conducted using different methodological approaches with regard to the sampling method, data collection tools, study populations, different socio-economic and political contexts, or FI evaluation tools.

Despite the existence of different estimates, FI prevalence is high in countries with developed economies, such as Australia and Japan (21.7% of households, and 15.7%, respectively, in 2012); Canada (7.7%, in 2007/8), and across the European Union (8.7% when 27 countries are included); and the US (15% of the population) (2).

In Portugal, the literature shows a trend for FI increase. The first FI study in Portugal, conducted in 2003, reported a national prevalence of 8.1% (19). Another nationwide study, the Fourth National Health Survey (2005-2006), reported a prevalence of 16.5% (20), and the INFOFAMÍLIA study (11), conducted during a period of great economic and social instability (2011 to 2014), reported a mean prevalence between 48.5% and 50.7%, with a prevalence in the Algarve of 59.5%, from which 15.1% corresponded to severe FI.

More recently, the National Food and Physical Activity Survey 2015-2016, showed a prevalence of 10.1% of FI in Portuguese households, of which 2.6% would correspond to moderate or severe FI. This national survey also showed significant differences between regions, with higher prevalence in the Autonomous Region of the Azores (13.4%) and the Autonomous Region of Madeira (13.2%), and lower prevalence in the Centre (8.5%) and Algarve (5.8%) regions (21). Data from the Portuguese Epidemiology of Chronic Diseases Cohort Study (EpiDoC) for the same period, collected using the Brazilian Scale of Food Insecurity, show a prevalence of FI in Portugal of 19.3% in a total of 5,653 participants, with food insecure households presenting low adherence to DM. Diabetes, rheumatic disease, depression symptoms, lower health-related quality of life, and a higher disability were independently associated with food insecurity (22).

Our data show a prevalence of 29.4% for FI in the households of the Algarve (24.2% for mild FI, 3.1% for moderate FI and 2.1% for severe FI). Due to methodological similarities with the INFOFAMILIA study (11), we consider it more appropriate to compare the current results with those of that national study, allowing us to infer that there may have been a decrease in prevalence, although there are results that prompt further research and intervention. In the INFOFAMILIA study, even after adjustment for socio-economic variables, the Algarve had a high prevalence of FI (59.5%, of which 15.1% corresponded to severe FI) and living in the Algarve was considered a risk factor for all levels of FI except for mild FI (11).

In this study, the economic, cultural, and social context of the Algarve households also proved to be important. Positive associations were found between the degree of FI and the number of unemployed in the household, the number of smokers and lower education level. These three risk factors for FI are identified in the literature and show a complex interrelationship in addition to their association with FI.

Unemployment is associated, as indicated in the literature (11,23–26), with a higher risk of FI as a result of low economic availability to buy food. In this study, the prevalence of unemployed participants (6.3%) is slightly below the unemployment rate registered by the National Institute of Statistics (32) for the Algarve region (7.7%). We also found that in 34.9% of the households the income is the result of only one element, in line with the results of the INFOFAMÍLIA study (11), where the contribution of only one element to the household income was 35.8%.

The decrease in purchasing power is identified in the literature as one of the causal factors of the association between smoking habits and AI, due to smokers diverting economic resources from food to the purchase of tobacco (27), or, antagonistically, to the increased anxiety caused by the FI situation, which will result in increased smoking behaviors (28–31).

Similarly, to what is described in the literature (23,27), this work identified the level of education, also related to income, as a risk factor for FI. The proportion of participants in this study who completed only the first level of basic education (31.3%; 95% CI between 26.8% and 36%) is higher than the national average (22.4%), indicated by the National Institute of Statistics for the same period (32), which may result in a confounding effect, which should be clarified in future studies.

The same confounding bias may explain the favourable OR for FS in individuals who consume alcoholic beverages compared to non-consumers (OR=0.51; 95% CI between 0.32 and 0.79). These results are contrary to what is expected and indicated by the literature, but it is thought that they may be caused by the low consumption of alcoholic beverages among participants presenting risk factors for FI (11,23,33).

Adherence to the MDP is, in general, low, and this low adherence is associated with FI. It is necessary to clarify whether this association is due to the association between low adherence to MDP and a lower socioeconomic status, documented in the literature (34), or to an as yet unclear role of MDP in FI.

This study has several limitations that must be identified and addressed. We conducted a cross-sectional study, which limits the establishment of causal and temporal inferences about the association between the variables. In addition, although a random sample of households was constructed in each municipality of the Algarve, data on education for the sample were dissimilar from those reported by the National Institute of Statistics. We are unable to analyze our data further, in order to discuss if there is a confounder. Additionally, weight and height were self-reported, which can suggest a bias in data collection.

These limitations indicate that the interpretation and generalization of our results should be made with caution. Nevertheless, given that this work is the first to analyze FI in detail only in the Algarve region, this paper can add value for the planning of specific interventions adapted to this population.

 

Conclusion

This was the first study on FI conducted only within the Algarve region, and we found a 29.4% prevalence. Households with more unemployed elements, smokers, and where adherence to the MDP is lower have a higher prevalence of FI. Additionally, it can be concluded that most of the population follows eating patterns different from the one promoted by the Mediterranean Diet.

The quantification and characterization of FI, as well as its determinants, is crucial for the development of policies to promote FS and healthy eating habits. Thus, we conclude that the results of this study can contribute to the planning of interventions in the Algarve region, which presents sociodemographic and economic characteristics that constitute an important challenge in the adequacy of health education strategies. Our results will allow that intervention projects that are planned or that are already underway can be tailored to the specific populational characteristics and improve their effectiveness.

 

Authors Contributions Statement

EP, AG and MPM, were responsible for study design, implementation, and data analysis. All authors share equal responsibility in drafting, editing, reviewing and final writing.

 

Funding

This study was supported by funding from the General Directorate of Health.

 

Acknowledgements

The authors would like to express their thanks to the General Directorate of Health, represented during this research by Prof. Dr. Pedro Graça and Prof. Dr. Maria João Gregório; to the regional partnership for their support; to the Prato Certo team (Ana Poeta, Arlete Rodrigues, Catarina Vasconcelos) for their dedication; and to the team of nutritionists who carried out the fieldwork.

 

Conflict of Interests

The authors declare there are no financial and/or personal relationships that could present a potential conflict of interests.

 

References

  1. Napoli, M. (2011). Towards a Food Insecurity Multidimensional Index (FIMI). Dissertação de Mestrado, Universidade de Roma.
  2. Pollard, C. M., & Booth, S. (2019). Food Insecurity and Hunger in Rich Countries—It Is Time for Action against Inequality. International Journal of Environmental Research and Public Health 2019, Vol. 16, Page 1804, 16(10), 1804. https://doi.org/10.3390/IJERPH16101804
  3. Brown, A. G. M., Esposito, L. E., Fisher, R. A., Nicastro, H. L., Tabor, D. C., & Walker, J. R. (2019). Food insecurity and obesity: research gaps, opportunities, and challenges. Translational Behavioral Medicine, 9(5), 980–987. https://doi.org/10.1093/TBM/IBZ1171
  4. Keenan, G. S., Christiansen, P., & Hardman, C. A. (2021). Household Food Insecurity, Diet Quality, and Obesity: An Explanatory Model. Obesity, 29(1), 143–149. https://doi.org/10.1002/OBY.23033/
  5. FAO, IFAD, UNICEF, WFP, & WHO. (2018). The State of Food Security and Nutrition in the World 2018. Building climate resilience for food security and nutrition. (FAO).
  6. Ayala, A., & Meier, B. M. (2017). A human rights approach to the health implications of food and nutrition insecurity.Public Health Reviews, 38(1), 1–22. https://doi.org/10.1186/S40985-017-0056-5/METRICS
  7. Fernandez, M. L., Raheem, D., Ramos, F., Carrascosa, C., Saraiva, A., & Raposo, A. (2021).Highlights of Current Dietary Guidelines in Five Continents. International Journal of Environmental Research and Public Health 2021, Vol. 18, Page 2814, 18(6), 2814. https://doi.org/10.3390/IJERPH18062814
  8. Willett, W. (2021). Mediterranean Dietary Pyramid. International Journal of Environmental Research and Public Health, 18(9), 4568. https://doi.org/10.3390/IJERPH18094568
  9. Serra-Majem, L., Tomaino, L., Dernini, S., Berry, E. M., Lairon, D., de la Cruz, J. N., Bach-Faig, A., Donini, L. M., Medina, F. X., Belahsen, R., Piscopo, S., Capone, R., Aranceta-Bartrina, J., Vecchia, C. la, & Trichopoulou, A. (2020).Updating the Mediterranean Diet Pyramid towards Sustainability: Focus on Environmental Concerns. International Journal of Environmental Research and Public Health, 17(23), 1–20. https://doi.org/10.3390/IJERPH17238758
  10. Obeid, C. A., Gubbels, J. S., Jaalouk, D., Kremers, S. P. J., & Oenema, A. (2022). Adherence to the Mediterranean diet among adults in Mediterranean countries: a systematic literature review.European Journal of Nutrition. https://doi.org/10.1007/S00394-022-02885-0
  11. Gregório, M. J., Graça, P., Santos, A. C., Gomes, S., Portugal, A. C., & Nogueira, P. J. (2017). Relatório Infofamília 2011-2014 Quatro anos de monitorização da Segurança Alimentar e outras questões de saúde relacionadas com condições socioeconómicas, em agregados familiares portugueses utentes dos cuidados de saúde primários do Serviço Nacional de Saúde.(Direção-Geral da Saúde, Ed.).
  12. Cohen, J. (1988). Statistical Power Analysis for the Behavioural Science (2nd Edition). In Statistical Power Anaylsis for the Behavioral Sciences (Vol. 3, Issue 2).
  13. Gregório, M. J., Graça, P., Santos, A. C., Gomes, S., Portugal, A. C., & Nogueira, P. J. (2017). Relatório Infofamília 2011-2014 Quatro anos de monitorização da Segurança Alimentar e outras questões de saúde relacionadas com condições socioeconómicas, em agregados familiares portugueses utentes dos cuidados de saúde primários do Serviço Nacional de Saúde. (Direção-Geral da Saúde, Ed.).
  14. Gregório, M. J., Nogueira, P. J., Gomes, S., Santos, C. A., & Boavida, J. (2014). Proposta Metodológica para a Avaliação da Insegurança Alimentar em Portugal. Nutrícias, 21, 4–11
  15. Pérez-Escamilla, R., Segall-Corrêa, A. M., Maranha, L. K., Sampaio, M. D. F. A., Marín-León, L., & Panigassi, G. (2004).An adapted version of the U.S. Department of Agriculture Food Insecurity module is a valid tool for assessing household food insecurity in Campinas, Brazil. The Journal of Nutrition, 134(8), 1923–1928. https://doi.org/10.1093/JN/134.8.1923.
  16. Bickel, G., Nord, M., Price, C., Hamilton, W., & Cook, J. (2000). Measuring Food Security in the United States Guide to Measuring Household Food Security Revised 2000. http://www.fns.usda.gov/oane.
  17. Schröder, H., Fitó, M., Estruch, R., Martínez-González, M. A., Corella, D., Salas-Salvadó, J., Lamuela-Raventós, R., Ros, E., Salaverría, I., Fiol, M., Lapetra, J., Vinyoles, E., Gómez-Gracia, E., Lahoz, C., Serra-Majem, L., Pintó, X., Ruiz-Gutierrez, V., & Covas, M.-I. (2011).A short screener is valid for assessing Mediterranean diet adherence among older  Spanish men and women. The Journal of Nutrition, 141(6), 1140–1145. https://doi.org/10.3945/jn.110.135566
  18. World Health Organization. (2000). Obesity: preventing and managing the global epidemic: report of a WHO consultation (World Health Organization, Ed.).
  19. Association, World Medical. (2013). World Medical Association Declaration of Helsinki: Ethical Principles for Medical Research Involving Human Subjects.JAMA, 310(20), 2191–2194. https://doi.org/10.1001/JAMA.2013.281053.
  20. Branco, M., Nunes, B., & Cantreiras, T. (2013). Uma Observação sobre “Insegurança Alimentar.”.
  21. Instituto Nacional de Estatística, & Instituto Nacional de Saúde Doutor Ricardo Jorge. (2009). Inquérito Nacional de Saúde 2005/2006 (Instituto Nacional de Estatística & Instituto Nacional de Saúde Doutor Ricardo Jorge, Eds.).
  22. Lopes, C., Torres, D., Oliveira, A., Severo, M., Alarcão, V., Guiomar, S., Mota, J., Teixeira, P., Rodrigues, S., Lobato, L., Magalhães, V., Correia, D., Pizarro, A., Marques, A., Vilela, S., Oliveira, L., Paul, & Ramos, E. (2017). Inquérito Alimentar Nacional e de Atividade Física IAN-AF, 2015-2016. In U. Porto (Vol. 112, Issue 483). https://doi.org/10.1192/bjp.112.483.211-a
  23. Gregório, M. J., Rodrigues, A. M., Graça, P., de Sousa, R. D., Dias, S. S., Branco, J. C., & Canhão, H. (2018).Food Insecurity Is Associated with Low Adherence to the Mediterranean Diet and Adverse Health Conditions in Portuguese Adults. Frontiers in Public Health, 6, 38. https://doi.org/10.3389/FPUBH.2018.00038/BIBTEX
  24. Alvares, L., & Amaral, T. F. (2014). Food insecurity and associated factors in the Portuguese population. The Nevin Scrimshaw International Nutrition Foundation, 35(4), 395–402. https://doi.org/10.1177/156482651403500401
  25. Oliveira, L., & De, Andro J. G. (2015). Avaliação da Insegurança Alimentar, Qualidade de Vida e Autoeficácia Alimentar na Ilha Terceira Avaliação da Insegurança Alimentar, Qualidade de Vida e Autoeficácia Alimentar na Ilha Terceira, Açores. Dissertação de Mestrado, Universidade do Porto.
  26. Gowda, C., Hadley, C., & Aiello, A. E. (2012).The association between food insecurity and inflammation in the US adult population. American Journal of Public Health, 102(8), 1579–1586. https://doi.org/10.2105/AJPH.2011.300551
  27. Rose, D. (1999). Economic Determinants and Dietary Consequences of Food Insecurity in the United States. American Society for Nutritional Sciences, 517–520. https://doi.org/10.1093/jn/129.2.517S
  28. Martin-Fernandez, J., Grillo, F., Parizot, I., Caillavet, F., & Chauvin, P. (2013a). Prevalence and socioeconomic and geographical inequalities of household food insecurity in the Paris region, France, 2010. BMC Public Health, 13(1). https://doi.org/10.1186/1471-2458-13-486
  29. Inglesias-Rios, L., Bromberg, J. E., Moser, R. P., & Augustson, E. M. (2015). Food Insecurity, Cigarette Smoking, and Acculturation Among Latinos: Data from NHANES 1999–2008. Journal of Immigrant and Minority Health, 17(2), 349–357. https://doi.org/10.1007/s10903-013-9957-7
  30. Cutler-Triggs, C., Fryer, G. E., Miyoshi, T. J., & Weitzman, M. (2008). Increased rates and severity of child and adult food insecurity in households with adult smokers.JAMA Pediatrics, 162(11), 1056–1062. https://doi.org/10.1001/archpediatrics.2008.2
  31. Armou, B. S., Pitts, M. M., & Lee, C.-W. (2008).Cigarette smoking and food insecurity among low-income families in the United States, 2001. American Journal of Health Promotion, 22(6), 386–392. https://doi.org/10.4278/ajhp.22.6.386
  32. John, R. M., Ross, H., & Blecher, E. (2011). Tobacco expenditures and its implications for household resource allocation in Cambodia.Tobacco Control, 21(3), 341–346. https://doi.org/10.1136/tc.2010.042598
  33. Alvares, L., & Amaral, T. F. (2014).Food insecurity and associated factors in the Portuguese population. The Nevin Scrimshaw International Nutrition Foundation, 35(4), 395–402. https://doi.org/10.1177/156482651403500401
  34. Martin-Fernandez, J., Grillo, F., Parizot, I., Caillavet, F., & Chauvin, P. (2013a). Prevalence and socioeconomic and geographical inequalities of household food insecurity in the Paris region, France, 2010.BMC Public Health, 13(1). https://doi.org/10.1186/1471-2458-13-486
  35. Instituto Nacional de Estatística. (2018). Estatísticas do Emprego – 4.o trimestre de 2017 (Issue 4, pp. 1–16).
  36. Bergmans, R. S., Coughlin, L., Wilson, T., & Malecki, K. (2019). Cross-sectional associations of food insecurity with smoking cigarettes and heavy alcohol use in a population-based sample of adults. Drug and Alcohol Dependence, 205. https://doi.org/10.1016/j.drugalcdep.2019.107646
  37. Bonaccio, M., Donati, M., Iacoviello, L., & Caetano, G. (2016). Socioeconomic determinants of the adherence to the Mediterranean diet at a time of economic crisis: the experience of the MOLI-SANI study. Agric Agric Sci Procedia, 6. https://doi.org/10.3389/fpubh.2018.00038