Waiting time and medical consultation length in the Manaus metropolitan region, Brazil: a cross-sectional, population-based study, 2015**Study funded by the National Council for Scientific and Technological Development (CNPq) / Ministry of Science, Technology, Innovation and Communication (MCTIC): Process Numbers 404990/2013-4 and 448093/2014-6.

Taís Freire Galvão Gustavo Magno Baldin Tiguman Douglas Borges da Costa Filho Marcus Tolentino Silva About the authors

Abstract

Objective

. To assess waiting times and length of medical consultations in the Manaus metropolitan region, Brazil.

Methods

. This was a cross-sectional study conducted with adults living in the region in 2015. Waiting time for consultation and length of consultation in minutes were reported by the participants. Analysis was performed using Tobit regression and weighted by the sample design.

Results

. 4,001 individuals were interviewed. Average waiting time was 125.4 minutes (95%CI 120.2;130.5), while consultations lasted an average of 52.5 minutes (95%CI 48.0;57.0). Women, poor people, people with less education, indigenous people, people resident in the state interior, people without health insurance, and individuals seen by gynecologists waited longer for their consultation (p <0.05). Consultation time was shorter for non-White people, those with poor health status and those who had health insurance (p <0.05).

Conclusion

. On average length of medical consultations was half the waiting time. Social inequalities were observed in these lengths of time.

Waiting Lists; Health Services Accessibility; Quality of Health Care; Cross-Sectional Studies; Physicians’ Offices

Introduction

A medical consultation is a moment of interaction between health professionals and patients and is based on interpersonal relationships.11. Chichirez CM, Purcarea VL. Interpersonal communication in healthcare. J Med Life [Internet]. 2018 Apr-Jun [cited 2020 Jun 24];11(2):119-22. Available from: https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6101690/
https://www.ncbi.nlm.nih.gov/pmc/article...
Adequate communication is essential for high quality diagnosis and treatment, and affects health outcomes.22. Riedl D, Schussler G. The Influence of doctor-patient communication on health outcomes: a systematic review. Z Psychosom Med Psychother [Internet]. 2017 Jun [cited 2020 Jun 24];63(2):131-50. Available from: https://doi.org/10.13109/zptm.2017.63.2.131
https://doi.org/10.13109/zptm.2017.63.2....
Medical care, technical knowledge, empathy towards patients, ease of access to medical care and adequate consultation length are associated with better medical consultation quality.33. Oostrom T, Einav L, Finkelstein A. Outpatient office wait times and quality of care for medicaid patients. Health Affairs [Internet]. 2017 May [cited 2020 Jun 24];36(5):826-32. Available from: https://doi.org/10.1377/hlthaff.2016.1478
https://doi.org/10.1377/hlthaff.2016.147...
In order to achieve continuing health service improvement, periodic evaluation of the entire process is necessary as well as evaluation of patients’ satisfaction with consultations.44. Szwarcwald CL, Damacena GN, Souza Júnior PR, Almeida WS, Malta DC. Perception of the Brazilian population on medical health care. Brazil, 2013. Ciên Saúde Coletiva [Internet]. 2016 Feb [cited 2020 Jun 24];21(2):339-49. Available from: http://dx.doi.org/10.1590/1413-81232015212.19332015
http://dx.doi.org/10.1590/1413-812320152...

Waiting lists indicate an imbalance between demand for health services and ability to provide them.55. Manolitzas P, Stylianou N. Modelling waiting times in an emergency department in greece during the economic crisis. J Health Manag [Internet]. 2018 Nov [cited 2020 Jun 24];20(4):475-85. Available from: https://doi.org/10.1177%2F0972063418799212
https://doi.org/10.1177%2F09720634187992...
Organizational and structural restrictions are the main factors contributing to this imbalance,55. Manolitzas P, Stylianou N. Modelling waiting times in an emergency department in greece during the economic crisis. J Health Manag [Internet]. 2018 Nov [cited 2020 Jun 24];20(4):475-85. Available from: https://doi.org/10.1177%2F0972063418799212
https://doi.org/10.1177%2F09720634187992...
and one of them, time spent waiting to be seen, affects patient satisfaction and the resolutive capacity of the health system.66. Saxon RL, Gray MA, Oprescu FI. Reducing geriatric outpatient waiting times: impact of an advanced health practitioner. Australas J Ageing [Internet]. 2018 Mar [cited 2020 Jun 24];37(1):48-53. Available from: https://doi.org/10.1111/ajag.12459
https://doi.org/10.1111/ajag.12459...
In high complexity environments, long waiting lists and delays in service delivery unnecessarily increase length of hospitalization time.88. Rahman AS, Shi S, Meza PK, Jia JL, Svec D, Shieh L. Waiting it out: consultation delays prolong in-patient length of stay. Postgrad Med J [Internet]. 2019 Jan [cited 2020 Jun 24];95(1119):1-5. Available from: https://doi.org/10.1136/postgradmedj-2018-136269
https://doi.org/10.1136/postgradmedj-201...
Difficulty in getting appointments is one of the main causes of dissatisfaction, given that having to wait for long periods of time generally results in shorter consultations.88. Rahman AS, Shi S, Meza PK, Jia JL, Svec D, Shieh L. Waiting it out: consultation delays prolong in-patient length of stay. Postgrad Med J [Internet]. 2019 Jan [cited 2020 Jun 24];95(1119):1-5. Available from: https://doi.org/10.1136/postgradmedj-2018-136269
https://doi.org/10.1136/postgradmedj-201...

Whereas waiting time is attributed to organizational processes, length of consultation is characterized by health demands and concerns, that differ between individuals and, for this reason, require empathetic interactions.99. Rodriguez Torres A, Jarillo Soto EC, Casas Patino D. Medical consultation, time and duration. Medwave [Internet]. 2018 Sep [cited 2020 Jun 24];18(5):e7266. Available from: https://doi.org/10.5867/medwave.2018.05.7264
https://doi.org/10.5867/medwave.2018.05....
Greater length of medical consultation time does not necessarily improve Primary Health Care performance results, including prescriptions issued, requests for laboratory tests and referrals, or patient satisfaction with the service provided.1010. Wilson AD, Childs S, Goncalves-Bradley DC, Irving GJ. Interventions to increase or decrease the length of primary care physicians’ consultation. Cochrane Database Syst Rev [Internet]. 2016 Aug [cited 2020 Jun 24];(8):Cd003540. Available from: https://doi.org/10.1002/14651858.cd003540.pub3
https://doi.org/10.1002/14651858.cd00354...
The contents of a medical consultation appear to play an important role with regard to both patient and doctor satisfaction.1111. Dobler CC, Sanchez M, Gionfriddo MR, Alvarez-Villalobos NA, Singh Ospina N, Spencer-Bonilla G, et al. Impact of decision aids used during clinical encounters on clinician outcomes and consultation length: a systematic review. BMJ Qual Saf [Internet]. 2019 Oct [cited 2020 Jun 24];28(6):499-510. Available from: http://dx.doi.org/10.1136/bmjqs-2018-008022
http://dx.doi.org/10.1136/bmjqs-2018-008...

Population-based studies evaluating waiting time and length of medical consultations in Brazil are scarce, especially in the country’s Northern region. The objective of this study was to analyze waiting time and medical consultation length and associated factors in the Manaus metropolitan region in 2015.

Methods

A population-based cross-sectional study was conducted with adults living in the Manaus metropolitan region in the state of Amazonas. The metropolitan region comprises a further seven municipalities apart from the state capital Manaus: Careiro da Várzea, Iranduba, Itacoatiara, Manacapuru, Novo Airão, Presidente Figueiredo and Rio Preto da Eva. We used data from a study conducted in the second semester of 2015 about use of health supplies and the health situation of the population in the region.1212. Silva MT, Galvão TF. Use of health services among adults living in Manaus Metropolitan Region, Brazil: population-based survey, 2015. Epidemiol Serv Saúde [Internet]. 2017 Oct-Dec [cited 2020 Jun 24];26(4):725-34. Available from: https://doi.org/10.5123/s1679-49742017000400005
https://doi.org/10.5123/s1679-4974201700...

Individuals aged 18 years or over were eligible to take part in the study. The sampling process was comprised of three stages: (i) random selection of census tracts; (ii) systematic selection of households; and (iii) random selection of an adult present in the household, according to predefined sex and age quotas, based on official national estimates, in accordance with the process detailed in an earlier publication.1212. Silva MT, Galvão TF. Use of health services among adults living in Manaus Metropolitan Region, Brazil: population-based survey, 2015. Epidemiol Serv Saúde [Internet]. 2017 Oct-Dec [cited 2020 Jun 24];26(4):725-34. Available from: https://doi.org/10.5123/s1679-49742017000400005
https://doi.org/10.5123/s1679-4974201700...

The sample size was calculated as being 4,000 people, estimating 50% health service use, with a 95% confidence level, 2% accuracy and a 1.5 design effect.1212. Silva MT, Galvão TF. Use of health services among adults living in Manaus Metropolitan Region, Brazil: population-based survey, 2015. Epidemiol Serv Saúde [Internet]. 2017 Oct-Dec [cited 2020 Jun 24];26(4):725-34. Available from: https://doi.org/10.5123/s1679-49742017000400005
https://doi.org/10.5123/s1679-4974201700...

The primary outcomes were waiting time and medical consultation length. The independent variables were:

  1. age (in years: 18-24; 25-34; 35-44; 45-59; 60 or over);

  2. sex (male; female);

  3. socioeconomic class (A/B, C or D/E, where A represents the wealthiest people and D/E the poorest, as per reported durable goods items and head of household schooling, according to the 2015 Brazilian Economic Classification Criterion);1313. Associação Brasileira de Empresas de Pesquisa - ABEP. Critério de classificação econômica Brasil 2015 [Internet]. São Paulo: Associação Brasileira de Empresas de Pesquisa; 2015 [citado 2020 jun 24]. Disponível em: http://www.abep.org/criterio-brasil
    http://www.abep.org/criterio-brasil...

  4. schooling (higher education; high school education; elementary education; below elementary education);

  5. race/skin color (white; black; yellow; brown; indigenous);

  6. health status (very good/good; regular; poor/very poor);

  7. city of residence (capital; interior);

  8. health insurance (yes; no); and

  9. medical specialty at last consultation (family doctor /general practitioner; gynecologist; specialist).

The primary outcomes were: waiting time for medical consultation, defined with effect from the time the patient arrived at the health service until they entered the consulting room; and length of medical consultation, defined with effect from the time the patient entered the consulting room until they came out. Both outcomes were defined in minutes.

The primary outcomes were measured through the following questions:

The last time you went to the doctor’s, how long did you wait to be seen (from the time you arrived at the health service until the consultation started)?

How long did the medical consultation last?

Economic classification was estimated according to the 2015 Brazilian Economic Classification Criterion, as per reported durable goods, head of household schooling and access to mains water and asphalt paved streets in the vicinity of the residence.1313. Associação Brasileira de Empresas de Pesquisa - ABEP. Critério de classificação econômica Brasil 2015 [Internet]. São Paulo: Associação Brasileira de Empresas de Pesquisa; 2015 [citado 2020 jun 24]. Disponível em: http://www.abep.org/criterio-brasil
http://www.abep.org/criterio-brasil...

Experienced interviewers carried out face-to-face interviews and the completed questionnaires were stored on tablets (Tab3 SM-T110 Samsung® Galaxy), using the Survey To Go application (Dooblo Ltd, Israel). The data were transmitted to a server via internet. A pre-test was conducted to ensure the questionnaire was understandable. The interview site was georeferenced and part of the interview was sound recorded; 20% of the interviews were audited by telephone for the purposes of quality control.1212. Silva MT, Galvão TF. Use of health services among adults living in Manaus Metropolitan Region, Brazil: population-based survey, 2015. Epidemiol Serv Saúde [Internet]. 2017 Oct-Dec [cited 2020 Jun 24];26(4):725-34. Available from: https://doi.org/10.5123/s1679-49742017000400005
https://doi.org/10.5123/s1679-4974201700...

The bivariate analysis, performed using Tobit regression, enabled identification of factors associated with waiting time (after having removed values equal to 0 and greater than 180 minutes) and consultation length (after having removed values equal to 0 and greater than 120 minutes). The cut-off point was defined according to minimum and maximum values plausible for the results. The adjusted coefficients (β) were also calculated using Tobit regression, with all the variables that had a variance inflation factor (VIF) of less than 10, in order to avoid multicollinearity. Sensitivity analysis was performed using the backward stepwise method to investigate the robustness of the model. The variable with the highest p-value was removed from the complete model. This process was repeated until the highest p-value was less than 0.20 for all variables. Predictive graphs were produced using statistically significant variables (p<0.05), in order to provide a better illustration of the differences between the main variable categories. All the analyses were performed using Stata (v.14.2), with correction for the sample’s complex design (svy command) and calculation of 95% confidence intervals (95%CI).

The study project was approved by the Federal University of Amazonas Research Ethics Committee (CEP/UFAM): Opinion No. 974.428/2015 and Certificate of Submission for Ethical Appraisal [CAAE] No. 42203615.4.0000.5020, on March 3rd 2015. All participants confirmed their agreement to taking part in the study by signing a Free and Informed Consent form.

Results

A total of 5,410 people were invited to take part in the study; 95 were not eligible and 1,314 refused (946 women and 368 men), resulting in 26% losses and refusals. 4,001 individuals were interviewed. There was a greater concentration of respondents in the 25-44 year age range (49.9%), 84.2% fell into lower socioeconomic classification (C/D/E) and 4.0% had higher education. The majority of interviewees stated that their skin color was brown (72.2%), reported good health status (66.1%), lived in the state capital (86.9%) and did not have health insurance (87.0%). The majority had consulted a general practitioner or family doctor the last time they had an appointment (77.1%) (Table 1).

Table 1
– Characteristics of the population, median waiting time and median medical consultation time (n=4,001), Manaus metropolitan region, 2015

Average waiting time for a medical consultation was 125.4 minutes (95%CI 120.2;130.5), and average length of consultation time was 52.5 minutes (95%CI 48.0;57.0). The highest median waiting time in minutes was found among people belonging to socioeconomic classification D/E (median=120 – interquartile range: 30;180), indigenous people (median=120 – interquartile range: 60;180), people resident in the state interior (median=120 – interquartile range: 30;180) and those seen by a gynecologist at their last consultation (median=120 – interquartile range: 45;180). Lowest median waiting times corresponded mainly to people who had health insurance (median=40 – interquartile range: 20;120) and those falling into economic classification A/B (median=60 – interquartile range: 30;120). The most frequent medical consultation length median was 15 minutes. The shortest median consultation length was found for indigenous people (median=10 – interquartile range: 5;20) and people resident in the state interior (median=10 – interquartile range: 10;30) (Table 1).

People aged 60 years or over (β=-21.8 – 95%CI -32.1;-11.6), people who were of black (β=-19.8 – 95%CI -29.9; -9.8) and brown race/skin color (β=-107 – 95%CI -177;-37) and health insurance beneficiaries (β=-32.5 – 95%CI -39.1;-25.9), women (β=9.3 – 95%CI 4.2;14.4), people falling into economic classifications C (β=11.3 – 95%CI 4.7;17.9) and D/E (β=17.3 – 95%CI 9.0;25.6), those who had not completed elementary education (β=13.0 – 95%CI 0.1;25.9), those resident in the state interior (β=10.6 – 95%CI 2.8;18.4) and those seen by a gynecologist (β=12.0 – 95%CI 3.2;20.8) waited longer before having their consultation (Table 2).

Table 2
– Coefficients () of waiting time for medical consultation and consultation length in minutes (n=4,001), Manaus metropolitan region, 2015

People who were 25-34 years old (β=-8.5 – 95%CI -12.5;-4.5), 35-44 years old (β=-9.2 – 95%CI -13.4;-5.0) and 45-59 years old (β=-7.6 – 95%CI -11.9;-3.2), people who stated they were of black (β=-20.7 – 95%CI -26.0;-15.5), yellow (β=-23.2 – 95%CI -28.2;-18.2), brown (β=-12.0 – 95%CI -16.2;-7.7) and indigenous race/skin color (β=-21.3 – 95%CI -33.5;-9.0), individuals with regular health status (β=-6.3 – 95%CI -9.1;-3.4), poor health status (β=-15.0 – 95%CI -18.8;-11.1) and people who had health insurance (β=-8.8 – 95%CI -11.9;-5.7) spent less time in their consultations. Medical consultations lasted longer for individuals falling into economic classifications C (β=6.5 – 95%CI 3.3;9.8) and D/E (β=12.5 – 95%CI 8.2;16.7) (Table 2).

Women spent more time waiting than men to have their consultation, regardless of age range (Figure 1). Expected waiting time and consultation length, stratified by health insurance, economic classification and race/skin color, showed that individuals who had health insurance, those who fell into a lower economic classification, and who were of black and brown skin color waited less time and had shorter consultations (Figure 2).

Figure 1
– Consultation waiting time for men and women in minutes, by age range (N=4,001), Manaus metropolitan region, 2015

Figure 2
– Waiting time and consultation length for individuals of white, black and brown skin color, stratified by socioeconomic classification and health insurance (n=4.001), Manaus metropolitan region, 2015

Discussion

On average, the adult population of the Manaus metropolitan region waits two hours to have a medical consultation. Longer waiting times were found for women, people with lower educational attainment and lower income levels and those who did not have health insurance. On average, medical consultations lasted for less than an hour and were significantly shorter for non-White people and those with poorer health status and private health care insurance.

The results presented are subject to selection bias, due to the inclusion of people present at the household at the time of the interview and who agreed to take part in the study. This may have had an impact on the profile and the answers of the study participants. The results were self-reported and for this reason may be subject to information bias, including recall bias; as the time that had elapsed between the interview and the last most recent medical consultation was not collected, it was not feasible to make a detailed analysis of the effect of this possible bias on the results. Prior analyses of accuracy between self-reported time and administrative data were not identified, but high agreement was found between reported use of the service and administrative records.1414. Short ME, Goetzel RZ, Pei X, Tabrizi MJ, Ozminkowski RJ, Gibson TB, et al. How accurate are self-reports? Analysis of self-reported health care utilization and absence when compared with administrative data. J Occup Environ Med [Internet]. 2009 Jul [cited 2020 Jun 24];51(7):786-96. Available from: https://dx.doi.org/10.1097%2FJOM.0b013e3181a86671
https://dx.doi.org/10.1097%2FJOM.0b013e3...
The analyses did not consider case severity and resolutive capacity, nor the administrative and logistic process faced by patients in order to access the health services. The multivariate analysis was based purely on statistical criteria for inclusion of the variables, which may weaken the analytical findings. In order to obtain representativeness of the population, we used probabilistic sampling, with predefined sex and age quotas, and the complex design was taken into consideration in the analyses, which increase confidence in the results found.

The waiting times found in the Manaus metropolitan region are longer than those reported in other national contexts. A study conducted in 2000 at a health center in Porto Alegre, in the Southern region of Brazil, found that the users of that service waited between 5 and 85 minutes to be seen by the doctor.1515. Ramos DD, Lima MADS. Acesso e acolhimento aos usuários em uma unidade de saúde de Porto Alegre, Rio Grande do Sul, Brasil. Cad Saúde Pública [Internet]. 2003 jan-fev [citado 2020 jun 24];19(1):27-34. Disponível em: https://doi.org/10.1590/S0102-311X2003000100004
https://doi.org/10.1590/S0102-311X200300...
Among individuals living in Brazilian municipalities with a poverty rate greater than 20% and receiving health care via the More Doctors (Mais Médicos) program – a program to provide doctors in remote areas of the country where health care is lacking –, 76% of service users waited less than an hour for their consultations and consultation length was less than 30 minutes for 98% of them.1616. Comes Y, Trindade JS, Shimizu HE, Hamann EM, Bargioni F, Ramirez L, et al. Evaluation of user satisfaction and service responsiveness in municipalities enrolled in the Mais Médicos (More Doctors) Program. Ciênc Saúde Coletiva [Internet]. 2016 Sep [cited 2020 Jun 24];21(9):2749-59. Available from: https://doi.org/10.1590/1413-81232015219.16202016
https://doi.org/10.1590/1413-81232015219...

Interventions to reduce waiting times include open-access appointment making, medical monitoring by telephone, screening by nurses and general practitioners and consultations by email.1717. Ansell D, Crispo JA, Simard B, Bjerre LM. Interventions to reduce wait times for primary care appointments: a systematic review. BMC Health Serv Res [Internet]. 2017 Apr [cited 2020 Jun 24];17(1):295. Available from: https://doi.org/10.1186/s12913-017-2219-y
https://doi.org/10.1186/s12913-017-2219-...
Administrative matters, such as general practitioner working hours, for instance, can influence how long patients have to wait.1818. Swami M, Gravelle H, Scott A, Williams J. Hours worked by general practitioners and waiting times for primary care. Health Econ [Internet]. 2018 Oct [cited 2020 Jun 24];27(10):1513-32. Available from: https://doi.org/10.1002/hec.3782
https://doi.org/10.1002/hec.3782...
Tools to support joint decision making have shown themselves to be effective in terms of improving the contents of medical consultations and doctors’ satisfaction, without increasing consultation length.1111. Dobler CC, Sanchez M, Gionfriddo MR, Alvarez-Villalobos NA, Singh Ospina N, Spencer-Bonilla G, et al. Impact of decision aids used during clinical encounters on clinician outcomes and consultation length: a systematic review. BMJ Qual Saf [Internet]. 2019 Oct [cited 2020 Jun 24];28(6):499-510. Available from: http://dx.doi.org/10.1136/bmjqs-2018-008022
http://dx.doi.org/10.1136/bmjqs-2018-008...

In this study, patients who had health insurance spent less time waiting for their consultation. This finding is similar to that of a cross-sectional study that analyzed 21 million outpatient consultations by patients using the Medicaid system in the United States in 2013, which also found longer waiting times for patients who did not have private health care insurance, compared to patients who did have health insurance.33. Oostrom T, Einav L, Finkelstein A. Outpatient office wait times and quality of care for medicaid patients. Health Affairs [Internet]. 2017 May [cited 2020 Jun 24];36(5):826-32. Available from: https://doi.org/10.1377/hlthaff.2016.1478
https://doi.org/10.1377/hlthaff.2016.147...
A possible explanation for this fact could be greater availability of health personnel and/or less demand, leading to a shorter waiting time in health services.33. Oostrom T, Einav L, Finkelstein A. Outpatient office wait times and quality of care for medicaid patients. Health Affairs [Internet]. 2017 May [cited 2020 Jun 24];36(5):826-32. Available from: https://doi.org/10.1377/hlthaff.2016.1478
https://doi.org/10.1377/hlthaff.2016.147...

Low schooling was associated with longer waiting times and shorter consultation length. In a population-based study conducted in Argentina, 61% of those who had not completed their elementary education waited an hour or more for their consultation, compared to 32% of those who had higher education.1919. Ballesteros MS. Desigualdades sociales en los tiempos de espera para la consulta médica en Argentina. Rev Gerenc Polít Salud [Internet]. 2016 [citado 2020 jun 24];15(30):234-50. Disponível em: http://dx.doi.org/10.11144/Javeriana.rgyps15-30.dste
http://dx.doi.org/10.11144/Javeriana.rgy...
Analysis of administrative records conducted in Australia in 2004 and 2005, also revealed the influence of socioeconomic classification on waiting time and the close relationship between socioeconomic level, schooling and access to health services.2020. Johar M, Jones G, Keane MP, Savage E, Stavrunova O. Discrimination in a universal health system: Explaining socioeconomic waiting time gaps. J Health Econ [Internet]. 2013 Jan [cited 2020 Jun 24];32(1):181-94. Available from: https://doi.org/10.1016/j.jhealeco.2012.09.004
https://doi.org/10.1016/j.jhealeco.2012....
Low schooling may imply communication barriers during consultations and less access to health services. These factors are possibly related to shorter medical consultation length.2121. Carlsen F, Kaarboe OM. The relationship between educational attainment and waiting time among the elderly in Norway. Health Policy [Internet]. 2015 Nov [cited 2020 Jun 24];119(11):1450-8. Available from: https://doi.org/10.1016/j.healthpol.2015.09.013
https://doi.org/10.1016/j.healthpol.2015...
Within this scenario, the empathy of health workers involved and their ability to overcome limitations also deserve to be evaluated.

A national health survey conducted in Portugal between 2005 and 2006 concluded that socioeconomic variables and region of residence influenced times spent waiting for a consultation.2222. Henriques TGP. Tempo de espera no acesso a consultas médicas: influência do gradiente socioeconómico [dissertação]. Coimbra: Faculdade de Economia, Universidade de Coimbra; 2012. Disponível em: https://estudogeral.sib.uc.pt/bitstream/10316/21501/1/Tese%20Tania%20Henriques.pdf
https://estudogeral.sib.uc.pt/bitstream/...
This appears to be a primordial characteristic in countries with universal health systems, directly influencing the principle of equality. The same has been found in Italy, Canada and Spain.2323. Landi S, Ivaldi E, Testi A. Socioeconomic status and waiting times for health services: An international literature review and evidence from the Italian National Health System. Health Policy [Internet]. 2018 Apr [cited 2020 Jun 24];122(4):334-51. Available from: https://doi.org/10.1016/j.healthpol.2018.01.003
https://doi.org/10.1016/j.healthpol.2018...
In this study, individuals with a lower economic classification waited longer for a medical consultation.

Women waited longer than men for their consultations and these also lasted longer for women. Gynecology was the specialty with the longest waiting time. A Swedish cohort study conducted between 2009 and 2012 assessed 159,352 medical consultations and concluded that even when they had identical levels of priority according to classification protocols, women waited longer than men to have their medical consultations.2727. Robertson J. Waiting time at the emergency department from a gender equality perspective [dissertação]. Gothenburg: Programe in Medicine, University of Gothenburg; 2014. Available from: https://gupea.ub.gu.se/bitstream/2077/39196/1/gupea_2077_39196_1.pdf
https://gupea.ub.gu.se/bitstream/2077/39...
A hypothesis here is greater self-care tendency among women, and possibly taking longer to report their health problems and interact with health professionals during consultations.2828. Pinkhasov RM, Wong J, Kashanian J, Lee M, Samadi DB, Pinkhasov MM, et al. Are men shortchanged on health? Perspective on health care utilization and health risk behavior in men and women in the United States. Int J Clin Pract [Internet]. 2010 Mar [cited 2020 Jun 24];64(4):475-87. Available from: https://doi.org/10.1111/j.1742-1241.2009.02290.x
https://doi.org/10.1111/j.1742-1241.2009...
Particularities of physical examination and specific care procedures for females may also explain this difference found between the sexes.

Waiting time and consultation time was lower for individuals of black and brown race/skin color, even after stratifying for health insurance. Less frequent use of health services may explain the shorter waiting time found for ethnic minorities.2929. Alencar RRFR, Galvao TF, Antonio BVR, Silva MT. Prevalence of self-reported chronic diseases and health services utilization by ethnic minorities in Manaus metropolitan region. Ethn Dis [Internet]. 2018 Feb [cited 2020 Jun 24];28(1):49-54. Available from: https://doi.org/10.18865/ed.28.1.49
https://doi.org/10.18865/ed.28.1.49...
Previous analyses of this population have shown that people of brown skin color have less health insurance coverage and fewer self-reported chronic diseases – possibly due to lack of diagnosis –, whereas indigenous people have higher prevalence of chronic diseases.2929. Alencar RRFR, Galvao TF, Antonio BVR, Silva MT. Prevalence of self-reported chronic diseases and health services utilization by ethnic minorities in Manaus metropolitan region. Ethn Dis [Internet]. 2018 Feb [cited 2020 Jun 24];28(1):49-54. Available from: https://doi.org/10.18865/ed.28.1.49
https://doi.org/10.18865/ed.28.1.49...
Structural discrimination and racism my also explain the shorter consultation times found.

Demand for health service improvements is continuous. The need exists to move forward with management models and increase human and structural resources in order for the National Health System to guarantee comprehensive, universal and equal access to the entire population.3030. Nunes BP, Flores TR, Garcia LP, Chiavegatto Filho ADP, Thumé E, Facchini LA. Tendência temporal da falta de acesso aos serviços de saúde no Brasil, 1998-2013. Epidemiol Serv Saúde [Internet]. 2016 out-dez [citado 2020 jun 24];25(4):777-87. Disponível em: http://dx.doi.org/10.5123/S1679-49742016000400011
http://dx.doi.org/10.5123/S1679-49742016...
Restrictions on investments, such as that used to establish an upper limit on social expenditure, will be potentially prejudicial to the scenario found.

The time spent by the population of the Manaus metropolitan region in relation to medical consultations is long; and consultations last half as long as waiting time, on average. Waiting time and consultation length are negatively impacted by social inequalities. Future studies should consider the quality of the services provided.

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  • *
    Study funded by the National Council for Scientific and Technological Development (CNPq) / Ministry of Science, Technology, Innovation and Communication (MCTIC): Process Numbers 404990/2013-4 and 448093/2014-6.

Publication Dates

  • Publication in this collection
    10 Aug 2020
  • Date of issue
    2020

History

  • Received
    04 Feb 2020
  • Accepted
    22 May 2020
Secretaria de Vigilância em Saúde - Ministério da Saúde do Brasil Brasília - Distrito Federal - Brazil
E-mail: leilapgarcia@gmail.com