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The Relationship between Lifestyle and Costs Related to Medicine Use in Adults

Abstract

Background:

The unhealthy lifestyle is growing and this can have repercussions on health status demanding actions on the occurrence of diseases and leads to increased expenses.

Objective:

To examine the interrelationship between the costs of medicine use and lifestyle behaviors.

Methods:

A cohort study with 118 participants, age around 51.7 ± 7.1 years old. It was collected personal and anthropometric data and information about medicine of continuous use to calculate the costs. Lifestyle variables included habitual physical activity (PA) assessed by pedometer, sedentary behavior by Baecke questionnaire, sleep quality by mini sleep questionnaire and self-report of smoke and alcohol consumption. Statistical analyses were performed by BioEstat (version 5.2) and the significance level set at p-value < 0.05.

Results:

In 12 months, 62 subjects bought 172 medicines, representing an overall cost of US$ 3,087.01. Expenditures with drugs were negatively related to PA (r = -0.194, p-value = 0.035 and r = -0.281, p-value = 0.002), but positively related with sleep quality (r = 0.299, p-value=0.001 and r = 0.315, p-value = 0.001) and age (r = 0.274, p-value = 0.003). Four multivariate models were executed considering lifestyle behaviors in different moments of cohort and medicine costs, and all these models identify important relationship between lifestyle behaviors with expenditures with drugs.

Conclusion:

Worse sleep quality seems to increase the costs related to medicine use in adults, while obesity and ageing play a relevant role in this phenomenon and alcohol consumption seems a variable with relevant economic impact.

Keywords:
Quality of Life; Sedentary Lifestyle; Obesity; Sports Medicine; Longevity; Health Behavior, Exercíse

Resumo

Fundamento:

O estilo de vida pouco saudável está se expandindo e isso pode ter repercussões no estado de saúde, exigindo ações contra a ocorrência de doenças e levando ao aumento de gastos.

Objetivo:

Examinar a interrelação entre os custos do uso de medicamentos e comportamentos de estilo de vida.

Métodos:

Estudo de coorte com 118 participantes com idade de 51,7 ± 7,1 anos. Foram coletados dados pessoais e antropométricos e informações sobre medicamentos de uso contínuo para calcular os custos. As variáveis de estilo de vida incluíram: atividade física (AF) habitual, avaliada por pedômetro; comportamento sedentário, pelo questionário de Baecke; qualidade do sono, através do Mini Questionário do Sono, e autorrelato de tabagismo e consumo de álcool. As análises estatísticas foram realizadas no programa BioEstat (versão 5.2), e o nível de significância estabelecido como p < 0,05.

Resultados:

Em 12 meses, 62 indivíduos compraram 172 medicamentos, representando um custo total de US$ 3.087,01. Gastos com medicamentos foram negativamente relacionados à AF (r = -0,194, p-valor = 0,035 e r = -0,281, p-valor = 0,002), mas relacionaram-se positivamente com a qualidade do sono (r = 0,299, p-valor=0,001 e r=0,315, p-valor = 0,001) e idade (r = 0,274, p-valor = 0,003). Quatro modelos multivariados foram executados, considerando os comportamentos de estilo de vida em diferentes momentos da coorte e custos dos medicamentos, e todos esses modelos identificam relações importantes entre comportamentos de estilo de vida e gastos com medicamentos.

Conclusão:

A pior qualidade do sono parece aumentar os custos relacionados ao uso de medicamentos em adultos, enquanto a obesidade e o envelhecimento desempenham um papel relevante nesse fenômeno, e o consumo de álcool parece ser uma variável com impacto econômico significativo.

Palavras-chave:
Qualidade de Vida; Estilo de Vida Sedentário; Obesidade; Medicina Esportiva; Longevidade; Comportamento Relacionado à Saúde; Exercício

Introduction

Over the last decades, the occurrence of obesity and chronic diseases has increased dramatically among adults worldwide.11 NCD Risk Factor Collaboration (NCD-RisC). Trends in adult body-mass index in 200 countries from 1975 to 2014: a pooled analysis of 1698 population-based measurement studies with 19·2 million participants. Lancet. 2016;387(10026):1377-96. On the other hand, advances in medical sciences, development of new generation of medicines/therapies, have significantly improved quality of life and longevity.22 Holloway KA, Henry D. WHO essential medicines policies and use in developing and transitional countries: an analysis of reported policy implementation and medicines use surveys. PLoS Med. 2014;11(9):e1001724.,33 Narayan SW, Tordoff JM, Nishtala PS. Temporal trends in the utilisation of preventive medicines by older people: A 9-year population-based study. Arch Gerontol Geriatr. 2016 Jan-Feb;62:103-11.

In developing countries, the use of any medicine is reported by 60% of the adult population, while the use of three or more medicines in the last two weeks is reported by about 18% of the population.44 Bertoldi AD, Hallal PC, Barros AJ. Physical activity and medicine use: evidence from a population-based study. BMC Public Health. 2006 Sep 6;6:224. A similar pattern is observed in Central Eastern Europe where more than 20% of adults (18 years plus) report three or more medicine use.55 Vogler S, Österle A, Mayer S. Inequalities in medicine use in Central Eastern Europe: an empirical investigation of socioeconomic determinants in eight countries. Int J Equity Health. 2015 Nov 5;14:124. Narayan et al.33 Narayan SW, Tordoff JM, Nishtala PS. Temporal trends in the utilisation of preventive medicines by older people: A 9-year population-based study. Arch Gerontol Geriatr. 2016 Jan-Feb;62:103-11. found that in a period of nine years (from 2005 to 2013) the use of drugs for prevention purposes (aspirin, clopidogrel, statins and bisphosphonates) increased significantly among New Zealand adults aged 65 years or more (about 19.5%, 2.9%, 7% and 2.3%, respectively).

The dramatic rise in the prevalence of obesity and its associations with the development of metabolic and cardiovascular diseases would explain, at least in part, this increase trend.11 NCD Risk Factor Collaboration (NCD-RisC). Trends in adult body-mass index in 200 countries from 1975 to 2014: a pooled analysis of 1698 population-based measurement studies with 19·2 million participants. Lancet. 2016;387(10026):1377-96.,66 Fernandes RA, Zanesco A. Early sport practice is related to lower prevalence of cardiovascular and metabolic outcomes in adults independently of overweight and current physical activity. Medicina (Kaunas). 2015;51(6):336-42. In fact, wide access to medicines by population signifies an improvement in the prevention/treatment of diseases.22 Holloway KA, Henry D. WHO essential medicines policies and use in developing and transitional countries: an analysis of reported policy implementation and medicines use surveys. PLoS Med. 2014;11(9):e1001724. However, the potential adverse drug reactions generated by the use of unprescribed medicines constitute a global public health concern related to high healthcare costs.77 Hyttinen V, Jyrkkä J, Valtonen H. A systematic review of the impact of potentially inappropriate medication on health care utilization and costs among older adults. Med Care. 2016;54(10):950-64.,88 White KG. UK interventions to control medicines wastage: a critical review. Int J Pharm Pract. 2010;18(3):131-40.

The economic burden related to medicine use involves not only health care costs resulting from inappropriate medication but also the purchase of prescribed and unprescribed medicines (public and out of pocket expenses).77 Hyttinen V, Jyrkkä J, Valtonen H. A systematic review of the impact of potentially inappropriate medication on health care utilization and costs among older adults. Med Care. 2016;54(10):950-64.,99 Arsenijevic J, Pavlova M, Rechel B, Groot W. Catastrophic health care expenditure among older people with chronic diseases in 15 european countries. PLoS One. 2016;11(7):e0157765. For example, in a period of five years from 2000 to 2004, the Brazilian Ministry of Health expended US$ 916 million in programs to provide high-cost drugs to population.1010 Brandão CM, Guerra AA Jr, Cherchiglia ML, Andrade EL, Almeida AM, da Silva GD, et al. Expenses of the Brazilian Ministry of Health for high-cost drugs: a demographic and clinical analysis. Value Health. 2011;14(5 Suppl 1):S71-7. Despite the increasing economic burden related to medicines use, little is known about its underlying determinants. We need to know these determinants in order to identify target areas for policy making on managing health budgets, particularly in health systems in developing settings.

Unhealthy lifestyle behaviors (alcohol consumption, smoking, poor sleep habits and sedentary behaviors [SB]) have been shown to play an important role in the development of many diseases,1111 Shi Z, Zhang T, Byles J, Martin S, Avery JC, Taylor AW. Food habits, lifestyle factors and mortality among oldest old chinese: The Chinese Longitudinal Healthy Longevity Survey (CLHLS). Nutrients. 2015;7(9):7562-79. but their direct relationship with the costs related to medicines use is unclear. For example, the occurrence of sleep disorders is highly prevalent in adults,1212 Zanuto EA, de Lima MC, de Araújo RG, da Silva EP, Anzolin CC, Araujo MY, et al. Sleep disturbances in adults in a city of Sao Paulo state. Rev Bras Epidemiol. 2015;18(1):42-53. but its economic burden is unknown.1313 Hui SK, Grandner MA. Trouble sleeping associated with lower work performance and greater health care costs: longitudinal data from Kansas State Employee Wellness Program. J Occup Environ Med. 2015;57(10):1031-8. A longitudinal study carried out with 11,698 American employees identified that how worse were the sleep disorder reported the health care costs to increase in average US$ 725.15.1313 Hui SK, Grandner MA. Trouble sleeping associated with lower work performance and greater health care costs: longitudinal data from Kansas State Employee Wellness Program. J Occup Environ Med. 2015;57(10):1031-8. Similarly, in a 12-months study carried out in Taiwan, adults with positive diagnosis to obstructive sleep apnea were 66% more expensive (in terms of physician diagnoses, medications, treatments, surgeries, laboratory tests and diagnostic imaging) than those adults without the same diagnosis (US$ 1,734.10 versus US$ 1,041.30, respectively).1414 Kao LT, Lee HC, Lin HC, Tsai MC, Chung SD. Healthcare service utilization by patients with obstructive sleep apnea: a population-based study. PLoS One. 2015;10(9):e0137459. Conversely, improved levels of physical activity (PA) could reduce costs related to medicine use in adults,1515 Codogno JS, Fernandes RA, Sarti FM, Freitas Júnior IF, Monteiro HL. The burden of physical activity on type 2 diabetes public healthcare expenditures among adults: a retrospective study. BMC Public Health. 2011 May 4;11:275.,1616 Codogno JS, Turi BC, Kemper HC, Fernandes RA, Christofaro DG, Monteiro HL. Physical inactivity of adults and 1-year health care expenditures in Brazil. Int J Public Health. 2015;60(3):309-16. but its role in the potential relationship between unhealthy lifestyle behaviors and costs of medicine use has not been studied to date.

In this study, we examine the interrelationship between costs of medicine use and lifestyle behaviors (both healthy and unhealthy).

Methods

Sample

The data comes from a cohort study carried out in the city of Presidente Prudente which presents human development index 0.806,1717 Turi BC, Monteiro HL, Fernandes RA, Codogno JS. The impact of physical activity on mitigation of health care costs related to diabetes mellitus: findings from developed and developing settings. Curr Diabetes Rev. 2016;12(4):307-11. placed on western Sao Paulo State (which is the state of the most industrialized Brazilian federation) from February/June 2014 (baseline) to May/December 2015 (follow-up). Sample size estimation was based on an equation for the correlation coefficient. Due to the absence of specific data about the relationship between lifestyle behaviors and health care costs in Brazil,1616 Codogno JS, Turi BC, Kemper HC, Fernandes RA, Christofaro DG, Monteiro HL. Physical inactivity of adults and 1-year health care expenditures in Brazil. Int J Public Health. 2015;60(3):309-16.,1818 Instituto Brasileiro De Geografia e Estatística (IBGE). Cidades e estados [mapa na internet]. Rio de Janeiro; [s.d.] [citado 11 set. 2018]. Disponível em: https://www.ibge.gov.br/estatisticas-novoportal/por-cidade-estado-estatisticas.html?t=destaques&c=3541406.
https://www.ibge.gov.br/estatisticas-nov...
we have adopted a correlation coefficient of 0.30 between PA and health care costs,1616 Codogno JS, Turi BC, Kemper HC, Fernandes RA, Christofaro DG, Monteiro HL. Physical inactivity of adults and 1-year health care expenditures in Brazil. Int J Public Health. 2015;60(3):309-16.,1818 Instituto Brasileiro De Geografia e Estatística (IBGE). Cidades e estados [mapa na internet]. Rio de Janeiro; [s.d.] [citado 11 set. 2018]. Disponível em: https://www.ibge.gov.br/estatisticas-novoportal/por-cidade-estado-estatisticas.html?t=destaques&c=3541406.
https://www.ibge.gov.br/estatisticas-nov...
z = 1.96 and power of 80% (adopting the above-mentioned parameters, the minimum sample size required for this study was 86 participants). The inclusion criteria for participants were: 40-65 years old, no diagnosis of previous cardiovascular complications (e.g. stroke, heart attack), no diabetes complications (amputation or visual problems), no regular medication use, and no physical disability.

Invitation to participate in the study was conducted using advertisements (ie posters) in the Sao Paulo State University in Presidente Prudente and gyms/fitness centers across the city. Interested participants contacted the research staff, who then checked the profile of the participants against the inclusion criteria (participants who met all the inclusion criteria signed a written consent form). One hundred ninety-eight adults contacted the research staff and were considered eligible and undertook baseline assessment. The analysis herein covered 118 subjects (44 men and 74 women) assessed at both baseline and follow-up (12 months later). The excluded people were due to (a) dropouts (n = 62) and (b) provision of less than seven days of pedometer use at baseline (n = 18).

All procedures (questionnaires, pedometers and body composition assessment) were performed by trained staff of researchers (Professors, MSc and PhD students) following the protocols of the Laboratory of Investigation in Exercise (LIVE), Brazil.1919 Mantovani AM, Duncan S, Codogno JS, Lima MC, Fernandes RA. Different amounts of physical activity measured by pedometer and the associations with health outcomes in adults. J Phys Act Health. 2016;13(11):1183-91. The Ethics committee of the Sao Paulo State University (UNESP), campus of Presidente Prudente, approved the study.

Costs of medicines use

At baseline, the participants were given a questionnaire (in diary form) for medicine use and instructions (further clarification offered face to face by research staff) on how to fulfil the questionnaire. The participants reported the following data: (a) number and type of all medicines (prescribed and unprescribed); (b) how they obtained the medicines - through the Brazilian National Health System [BNHS] or out of pocket expenditure. The diary was filled for each of the 12 months of the cohort study. At the end of the follow-up period, the research staff collected back the completed diaries (Table 1). To calculate the cost of medicines, we used national prices presented by BNHS (for medicines delivered by the BNHS) and market prices from drug stores in the study area (medicines obtained via personal expenses). Costs were computed in Brazilian currency (Real$) and converted to US dollar (US$) using the cambial information provided by the Central Bank of Brazil.

Table 1
Most frequently bought medicines according to anatomical therapeutic chemical code

Lifestyle behavioral variables

PA was measured using both objective and subjective measures at baseline and follow-up. Objective measure of PA was collected using pedometers (Yamax digiwalker, SW200 model, Japan), and specified in terms of step count. At both assessments periods (baseline and follow-up), pedometers were worn by participants for seven consecutive days. The pedometers were fixed laterally at the hip and were taken off only during periods of sleep and water-based activities. Participants logged (at the end of each day) the total step count. In the present study, PA denoted the number of days (out of 14 days assessed) that ≥7,500 steps were achieved. In line with Tudor-Locke et al.,2020 Tudor-Locke C, Schuna JM Jr, Barreira TV, Mire EF, Broyles ST, Katzmarzyk PT, et al. Normative steps/day values for older adults: NHANES 2005-2006. J Gerontol A Biol Sci Med Sci. 2013;68(11):1426-32. participants who reached ≥7,500 steps/day were classified as “sufficiently active”. The subjective measure of PA was collected using Baecke’s questionnaire.2121 Baecke JA, Burema J, Frijters JE. A short questionnaire for the measurement of habitual physical activity in epidemiological studies. Am J Clin Nutr. 1982;36(5):936-42. The questionnaire is composed of 16 questions about three PA domains (occupational, sports participation and leisure-time PA).

Data on SB at work (both baseline and follow up) were captured using the following question: “At work I sit”…; potential responses were: never [score attributed = 1], seldom [score attributed = 2], sometimes [score attributed = 3], often [score attributed = 4] and very often [score attributed = 5]).

Quality of sleep was assessed at baseline and follow up using the Mini-Sleep Questionnaire,2222 Falavigna A, de Souza Bezerra ML, Teles AR, Kleber FD, Velho MC, da Silva RC, et al. Consistency and reliability of the Brazilian Portuguese version of the Mini-Sleep Questionnaire in undergraduate students. Sleep Breath. 2011;15(3):351-5. which includes 10 questions, each one with seven possible answers (ranging from never to always). The sum of these 10 answers generates a numerical score ranging from 10 to 70 points (higher scores indicate worse sleep quality).

Participants also self-reported at baseline and follow-up smoking status (yes or no current smoker) and weekly alcohol consumption (number of days per week with alcohol consumption).

Covariates

Covariates were data collected via questionnaire (sex [male or female], date of birth [chronological age estimated using the difference between birthday and date of assessment] and formal schooling [in years]). Clinical data also were evaluated (body fatness [dual-energy X-ray absorptiometry], systolic and diastolic blood pressure respectively). Researchers performed the clinical measures in university facilities with controlled temperature and followed standardized procedures.

Statistical analyses

Descriptive statistics were undertaken using mean, 95% confidence intervals (95%CI) and proportions as appropriate. Due to non-parametric distribution (attested by Kolmogorov-Smirnov test), the costs of medicine use were converted into base-10 logarithms.

Both the Pearson correlation and linear regression were conducted to assess the relationship between the costs of medicines and the independent variables. In the former approach, Pearson correlation (expressed as standardized coefficients [“r” values]) analyzed the relationship of the costs of medicine use with lifestyle behaviors (sleep quality, PA, SB at work, smoking and alcohol consumption) and covariates (sex, age, schooling, blood pressure and body fatness) separately. In the latter, linear regression models (expressed as unstandardized coefficients [β values]) were fitted to examine the relationship between the costs of medicine use and lifestyle behaviors controlling for all covariates. For each approach, four models were fitted based on different specifications of lifestyle behaviors ([A] only baseline values, [B] only follow-up values, [C] difference between follow-up and baseline and [D] sum of baseline and follow-up), to explore the differential relationship these specifications may present. Diagnosis of multicollinearity and homoscedasticity were assessed and the linear regression models were considered adequately fit.

All analyzes were performed using BioEstat (version 5.2) and the significance level was set at p-value < 0.05.

Results

At baseline, the mean age of the sample was 51.7 ± 7.1 years, ranging from 40 to 68 years (Table 2). Alcohol was consumed on average 2.1 days per week, while 5.1% of the sample were smokers. Expenses on medicine use were reported by 52.5% of the sample. During 12-months of follow-up, 62 subjects bought 172 medicines (Table 2), representing an overall cost of US$ 3,087.01 for the entire sample. There was no missing data.

Table 2
Summarized characteristics of the sample (n = 118)

PA decreased significantly from baseline to follow-up (p-value = 0.024), while the score for SB at work (p-value = 0.396), sleep quality (p-value = 0.951) and alcohol consumption (p-value = 0.100) remained stable between baseline and follow-up.

In the bivariate analysis, costs of medicine use were negatively related to PA baseline (r = -0.194; p-value = 0.035), PA follow-up (r = -0.281; p-value = 0.002), but positively related with sleep quality baseline (r = 0.299; p-value= 0.001) and sleep quality follow-up (r = 0.315; p-value = 0.001), and age baseline (r = 0.274; p-value = 0.003). Gender, education, SB at work, alcohol consumption and smoking were not significantly related with costs of medicine use. There was no interrelationships among the lifestyle behaviors.

In the multivariate model considering lifestyle behaviors at baseline (Model-A), sleep quality and body fatness were positively related to higher 12-months medicine costs, while alcohol consumption was negatively related to it. Model-A explained 19.1% of all variance in the outcome (Table 3). In the multivariate model considering lifestyle behaviors at follow-up (Model-B), only sleep quality was positively related to higher 12-months medical costs. Model-B explained 21.9% of all variance in the medicine costs.

Table 3
Linear regression describing the relationship between 12-months medicine costs (dependent variable) and lifestyle behaviors (n= 118)

In the multivariate model considering changes over time in lifestyle behaviors (Model-C), age and body fatness were positively related to higher 12-months medicine costs. Model-C explained 13.1% of all variance in the medicine costs. In Model-D (sum of baseline and follow-up values), sleep quality had a positive relationship with medicine use (Table 3). On the other hand, alcohol consumption was negatively related to costs of medicine use. Model-D explained 21.7% of all variance in the medicine costs.

Discussion

This study shows that lifestyle behaviors particularly worse sleep quality leads to higher costs related to medicine use. Body fatness was also found to be an important predictor - positive effect on costs. In overall, 52.5% of adults reported any medicine use during the cohort period, while 20.5% (n = 24) of these same adults reported the use of three or more medicines. These rates are similar to Brazilian (18%) and European (20%) surveys, in which population-based samplings were carried out.44 Bertoldi AD, Hallal PC, Barros AJ. Physical activity and medicine use: evidence from a population-based study. BMC Public Health. 2006 Sep 6;6:224.,55 Vogler S, Österle A, Mayer S. Inequalities in medicine use in Central Eastern Europe: an empirical investigation of socioeconomic determinants in eight countries. Int J Equity Health. 2015 Nov 5;14:124.

Another similarity with previous studies observed in our findings is that drugs for the treatment of cardiovascular diseases were the most reported by the participants. A study carried out in New Zealand examining the trends of medicine use in adults aged ≥ 65 years from 2005 to 2013 identified that the use of drugs to prevent cardiovascular events (aspirin and statins) increased significantly.33 Narayan SW, Tordoff JM, Nishtala PS. Temporal trends in the utilisation of preventive medicines by older people: A 9-year population-based study. Arch Gerontol Geriatr. 2016 Jan-Feb;62:103-11. The dynamics observed for medicines to the treatment of cardiovascular diseases seems affected by aging as well (in our study, a relevant covariate in the multivariate models). Previous data have identified that consumption of aspirin and dipyridamole increased in older adults at a higher rate than observed in younger ones.33 Narayan SW, Tordoff JM, Nishtala PS. Temporal trends in the utilisation of preventive medicines by older people: A 9-year population-based study. Arch Gerontol Geriatr. 2016 Jan-Feb;62:103-11.

The increased amount paid by older adults can be supported by the natural effects that ageing exert over organs of the human body and their functions,2323 Wichi RB, De Angelis K, Jones L, Irigoyen MC. A brief review of chronic exercise intervention to prevent autonomic nervous system changes during the aging process. Clinics (Sao Paulo). 2009;64(3):253-8. but also boosted by the reduced PA observed in older groups.2424 Milanović Z, Pantelić S, Trajković N, Sporiš G, Kostić R, James N. Age-related decrease in physical activity and functional fitness among elderly men and women. Clin Interv Aging. 2013;8:549-56. In the analyzed sample, although the effect of age on costs with medicine use was not mediated by PA, they were related with other in crude analyzes, denoting the relevance of actions targeting the improvement of PA practice mainly in population groups composed of older adults.2323 Wichi RB, De Angelis K, Jones L, Irigoyen MC. A brief review of chronic exercise intervention to prevent autonomic nervous system changes during the aging process. Clinics (Sao Paulo). 2009;64(3):253-8.,2424 Milanović Z, Pantelić S, Trajković N, Sporiš G, Kostić R, James N. Age-related decrease in physical activity and functional fitness among elderly men and women. Clin Interv Aging. 2013;8:549-56.

In this sample, the higher cost with medicines in adults with sleep disorders can represent not only the treatment of the sleep disorders itself, but also the use of medicines to relief its symptoms and hence to maintain the daily activities, such as work.1313 Hui SK, Grandner MA. Trouble sleeping associated with lower work performance and greater health care costs: longitudinal data from Kansas State Employee Wellness Program. J Occup Environ Med. 2015;57(10):1031-8.,2525 Araujo MY, Sarti FM, Fernandes RA, Monteiro HL, Turi BC, Anokye N, et al. Association between costs related to productivity loss and modified risk factors among users of the Brazilian National Health System. J Occup Environ Med. 2017;59(3):313-19.

The findings related to alcohol consumptions were surprising because usually the higher alcohol consumption is linked to higher health care costs,2626 Gómez-Restrepo C, Gómez-García MJ, Naranjo S, Rondón MA, Acosta-Hernández AL. Alcohol consumption as an incremental factor in health care costs for traffic accident victims: evidence in a medium sized Colombian city. Accid Anal Prev. 2014 Dec;73:269-73.,2727 Neramitpitagkul P, Lertpitakpong C, Yothasamut J, Thavorncharoensap M, Chaikledkaew U, Teerawattananon Y. Economic impact on health-care costs related to major diseases including HIV/AIDS due to alcohol drinking among Thai populations. Value Health. 2009;12(Suppl 3):S97-100. and not the opposite as observed in our study. In fact, the linkage between alcohol consumption and health care costs can be direct (e.g. diseases directly linked to alcohol consumption) and indirect (e.g. traffic car accident), but it is important to take into account that some kinds of alcoholic drinks have healthy characteristics, such as anti-inflammatory properties observed in the red wine.2828 Kwan HY, Chao X, Su T, Fu X, Tse AK, Fong WF, et al. The anticancer and antiobesity effects of Mediterranean diet. Crit Rev Food Sci Nutr. 2017;57(1):82-94. Therefore, the explanation for our interesting finding could be due to both the type and amount of alcohol consumed. However, our study looked at only the number of days per week with alcohol consumption, and not amount and type of alcohol consumed, which characterizes a limitation in our study.

Other limitations of the study are worth mentioning. The first limitation of the study is the small sample size. The current study has statistical power of 80% to detect coefficient of correlation of 0.256 or higher, while the relationship between some behaviors and costs with medicine are around r = 0.110.1616 Codogno JS, Turi BC, Kemper HC, Fernandes RA, Christofaro DG, Monteiro HL. Physical inactivity of adults and 1-year health care expenditures in Brazil. Int J Public Health. 2015;60(3):309-16. Even considering the fact that the inclusion of covariates increases the power of multivariate models,2929 Lingsma H, Roozenbeek B, Steyerberg E; IMPACT investigators. Covariate adjustment increases statistical power in randomized controlled trials. J Clin Epidemiol. 2010;63(12):1391. the reduced sample size may have been responsible for the absence of significant relationship between PA and costs with medicine. Another limitation related to objective measures of PA is the logging of data by the participants because every day they had to note the number of steps displayed in the pedometer. Although this method is widely used,1919 Mantovani AM, Duncan S, Codogno JS, Lima MC, Fernandes RA. Different amounts of physical activity measured by pedometer and the associations with health outcomes in adults. J Phys Act Health. 2016;13(11):1183-91. it could have led to misreporting. As above mentioned, the absence of measures of amount and kind of alcoholic drinks and sedentary behavior (by screen time on TV or computer) constitute limitations as well. Further studies could explore the impact of these.

Conclusions

Worse sleep quality seems to increase the costs related to medicine use in adults, while obesity and ageing play a relevant role in this phenomenon. Moreover, alcohol consumption seems a variable with relevant economic impact, but further studies are necessary to identify clearly the direction of its relationship with medicine costs.

  • Sources of Funding
    There were no external funding sources for this study.
  • Study Association
    This article is part of the thesis of Doctoral submitted by Alessandra Madia Mantovani, from Universidade Estadual Paulista.
  • Ethics approval and consent to participate
    This study was approved by the Ethics Committee of the Faculdade de Ciências e Tecnologia da Universidade Estadual Paulista under the protocol number 349.306/2013.

Acknowledgment

To the Fundação de Amparo a Pesquisa do Estado de São Paulo (FAPESP) (process number: 2017/50026-7 and 2015/20460-1) and to the Coordenação de Aperfeiçoamento de Pessoal de Nível Superior (CAPES).

References

  • 1
    NCD Risk Factor Collaboration (NCD-RisC). Trends in adult body-mass index in 200 countries from 1975 to 2014: a pooled analysis of 1698 population-based measurement studies with 19·2 million participants. Lancet. 2016;387(10026):1377-96.
  • 2
    Holloway KA, Henry D. WHO essential medicines policies and use in developing and transitional countries: an analysis of reported policy implementation and medicines use surveys. PLoS Med. 2014;11(9):e1001724.
  • 3
    Narayan SW, Tordoff JM, Nishtala PS. Temporal trends in the utilisation of preventive medicines by older people: A 9-year population-based study. Arch Gerontol Geriatr. 2016 Jan-Feb;62:103-11.
  • 4
    Bertoldi AD, Hallal PC, Barros AJ. Physical activity and medicine use: evidence from a population-based study. BMC Public Health. 2006 Sep 6;6:224.
  • 5
    Vogler S, Österle A, Mayer S. Inequalities in medicine use in Central Eastern Europe: an empirical investigation of socioeconomic determinants in eight countries. Int J Equity Health. 2015 Nov 5;14:124.
  • 6
    Fernandes RA, Zanesco A. Early sport practice is related to lower prevalence of cardiovascular and metabolic outcomes in adults independently of overweight and current physical activity. Medicina (Kaunas). 2015;51(6):336-42.
  • 7
    Hyttinen V, Jyrkkä J, Valtonen H. A systematic review of the impact of potentially inappropriate medication on health care utilization and costs among older adults. Med Care. 2016;54(10):950-64.
  • 8
    White KG. UK interventions to control medicines wastage: a critical review. Int J Pharm Pract. 2010;18(3):131-40.
  • 9
    Arsenijevic J, Pavlova M, Rechel B, Groot W. Catastrophic health care expenditure among older people with chronic diseases in 15 european countries. PLoS One. 2016;11(7):e0157765.
  • 10
    Brandão CM, Guerra AA Jr, Cherchiglia ML, Andrade EL, Almeida AM, da Silva GD, et al. Expenses of the Brazilian Ministry of Health for high-cost drugs: a demographic and clinical analysis. Value Health. 2011;14(5 Suppl 1):S71-7.
  • 11
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Publication Dates

  • Publication in this collection
    14 Mar 2019
  • Date of issue
    June 2019

History

  • Received
    25 May 2018
  • Reviewed
    18 Sept 2018
  • Accepted
    19 Sept 2018
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