2.0 Motivation
Respiratory illnesses are among the main sources of the global burden of disease (according with the Fig. 2 in Vos et al., 2020), the same being true in Brazil where they are the third main cause of mortality (DATASUS, 2021b). A recent large international survey, conducted by Velden et al. (2020), with 5,196 individuals from seven developed and seven developing countries including Brazil, estimated that only 30% of patients with sore throat, a symptom associated with Influenza-like illnesses and acute respiratory illnesses (Zhang et al., 2020), seek a general practitioner. Similarly high rates of refusal and also delay in seeking care were found in national-level surveys (Datiko et al., 2020, Seid and Metaferia, 2018, Zhang et al., 2020, Suka et al., 2016). These behaviours besides prolonging the impairment of own health and labour productivity (Piabuo and Tieguhong, 2017), and increasing risk of own mortality, which already have social consequences, may also increase contagion locally in case of viral and bacterial diseases (Datiko et al., 2020, Seid and Metaferia, 2018).
Such negative social effects are more probable in less developed regions such as the Brazilian Amazon. There, the per capita number of families receiving poverty alleviation cash transfers is 1.5 that of the whole country (MC, 2021, IBGE, 2021) and large scale exposure to pollution from agricultural fires is seasonal and comparable in its intensity to exposure to urban pollution in megacities such as London and Mexico City (Morello, 2021, Gonçalves et al., 2018). Also, the prevalence of both tuberculosis (TB) and Influenza is 1.3 fold larger than the country’s average (of six cases per 10,000 inhabitants; SINAN, 2021, IBGE, 2021). Contradictorily, the greater health challenge coincides with a smaller endowment of health resources. In the Amazon, the number of physicians per capita is thirty fold below the world average and 0.09 hospital beds per 1,000 inhabitants are available as compared with 3.2 in the world (WB, 2021, DATASUS, 2021).
Besides the supply-side gap, previous studies in Brazil have also detected demand-side barriers to healthcare, such as low capacity to recognize, both in oneself and in children cared for, diseases, their severity and contagiousness (Passos et al., 2018, Borges et al., 2018), limited capacity to obtain and use health-relevant information, mainly by the low-educated (Apolinario et al., 2013, Almeida et al., 2019), household income incompatible with transport costs required by visiting a health facility, which is more likely for rural residents (Parry et al., 2018), and reliance on self-medication (Arrais et al., 2016).
Nevertheless, none of these studies are based on patient surveys uncovering factors enabling and disabling care seeking for respiratory illnesses, which are rare in Brazil, and, as far as it could be assessed, unavailable for the Amazonian region. Nevertheless, in order to stimulate healthcare seeking, there is need to detect patients’ characteristics predicting whether such course of action would be spontaneously carried out or not. This is required for knowing which social groups should be prioritized by specific interventions (Diaz et al., 2013), such as behavioural nudges (Schmidt, 2019) and educational campaigns (Suka et al., 2016), and how these should be designed. Seeking to fill such gaps, this study detects predictors of healthcare seeking among respiratory ill individuals of Acre state, western Amazon.
2.2 Factors of demand for health care
A total of twelve recently published papers were resorted to in order to identify potential factors influencing the demand for healthcare, as basis for selecting covariates for quantitative analysis and for discussing and complementing the results. Summaries of all papers are provided in the Additional Information and here a short overall synthesis and taxonomy are presented.
The papers revised highlighted the relevance of multiple factors including characteristics of patients, of social groups (households and communities), social context and macroeconomic features such as development and economic growth level (Table 1). These factors exert effect through multiple mechanisms at individual (e.g., identification of illness from symptoms), household (e.g., women autonomy to seek care for children), community (e.g., support for seeking care) and macroeconomic level (e.g., economic growth expanding national health systems). Therefore, the demand for health care is both a multidimensional and multi-level process which can hardly be comprehensively apprehended with only one research method. This is clear from the larger diversity of factors in the literature as compared with the datasets used in this paper (Table 1). Nevertheless it is also clear that a significant fraction of factors is captured, with the uncaptured (macroeconomic) factors being left to be accounted for in the results’ discussion (Sect. 5). Also, the focus on demand-side variables is coherent with the consulted literature, especially with the finding by Datiko et al. (2020) and Seid and Metaferia (2018) that delay in receiving TB treatment was mostly demand-driven.
Table 1
Classification of factors influencing demand for healthcare in previous studies and in this paper (demand enablers are indicated with “+” and disablers with “-”)
Papers | Class | Factors in the literature | Factors in this paper |
DAT20, NAK20, SUK16, SEI18, NGH17, D20, TET17 | Individual characteristics | .Education (+) [TET17] .Age (+) [SEI18, DAT20] (population ageing contributes for increased health expenditure at the macroeconomic level) (+) [NGH17] .Income (+) .Poverty (-) | .Education .Age .Income .Poverty .Gender |
SUK16, ABD18, NGH17, CHE21, ZHA20 | Health level and condition | .Chronic disease (+) .Interaction with ill individuals (+) .Illness stigmatization (-) .Anxiety (a positive predictor of TB stigmatization) [CHE21] (-) .Health level of the community and capacity to support healthcare seeking (+) | .Serious lung diseases (including chronic diseases) .Subjective pain level caused by respiratory illness or dry cough .Symptoms caused by respiratory illness .Duration of respiratory illness and dry cough |
D20, DAT20, SEI18, SUK16 | Health knowledge | .Ability to identify symptoms, their severity, linkage with illnesses and causes (+) .Limited knowledge about prevention and treatment (-) .Degree of "health literacy" or capacity to obtain and use information in order to ensure good health [SUK16] (+) | Proxied by education |
D20, NAK20, CHE21, SUK16 | Intra-household relationships, gender and communities | .Mothers' agency to seek healthcare (-) and control of household budget (+); .Household size (-) .Female gender (-/+) .Support from relatives in seeking healthcare and overcoming stigmatization (+) .Social capital [SUK16] (+) | .Household size .Gender of respondent .Gender of child’s caregiver |
D20, CHE21, NAK20, DAT20, DIA13 | Access to care (including relationship with providers and treatment cost) | .Previous contacts (+) and good personal relations with healthcare provider (+), including good patient-physician communication (+) [CHE21] .Health facility distance (-) .Treatment cost (-), co-payment or any out-of-pocket disbursement being required (-) .Opportunity cost of treatment (specifically transport cost) (-) .Waiting time (-) | .Facility distance or travel time .Waiting time .Has at least a moderate amount of time available to seek healthcare .Health insurance |
D20, NAK20, TET17, DIA13 | Non-professional care (traditional medicine and alternative, self-prescribed and over-the-counter medication) | .Resort to self-made medication (including traditional plant and herb based remedies) (-) and self-medication with either traditional or ordinary drugs (-) [SEI18]; .Resort to traditional healers and "chemists" (medication vendors) (-) .Relative distance of formal and non-formal care options (e.g., "chemist"/drug vendors are inside the community and health facility is outside) (-) | Resort to non-professional healthcare (mostly traditional medication) |
PIA17, NGH17 | Macroeconomic factors | .GDP (predictor of public health care expenditure) [PIA17] (+) .Labour force (idem) [PIA 17] (+) .Technological progress (which fosters economic growth and thus health expenditure) [PIA 17] (+) | Not addressed |
Note: citations are abbreviated as D20 ≡ Dougherty et al. (2020), CHE21 ≡ Chen et al. (2021), DAT20 ≡ Datiko et al. (2020), NAK20 ≡ Nakovics et al. (2020), SUK16 ≡ Suka et al. (2016), PIA17 ≡ Piabuo and Tieguhong (2017), ABD17 ≡ Abdullah et al. (2017), SEI18 ≡ Seid and Metaferia (2018), NGH17 ≡ Nghiem and Conelly (2017), TET17 ≡ Tetteh et al. (2017), ZHA20 ≡ Zhang et al. (2020), DIA13 ≡ Diaz et al. (2013). Detailed summary of papers in the Additional Information. |
2.3 Region of study
Acre state, western Brazilian Amazon, is amongst the least developed and healthcare-endowed states of Brazil, ranking 21st of the countries’ 27 states in human development (PNUD, 2021) and 23rd in physicians per capita (DATASUS, 2021, IBGE, 2021). Despite the limited resources, respiratory illnesses are targeted by multiple actions, including annual vaccination campaigns (SI-PNI, 2021), the Family Health primary care program including home visits by community health agents (CHAs), whom provide key advice on, for instance, home-based preventive measures, healthcare seeking and administration of prescribed drugs. Medication for asthma and rhinitis is also subsidized (SUS, 2021).