This study estimated time costs as well as transport and medical costs of accessing and utilising MCH services at PHC facilities, and examined the distribution of these costs across patient subgroups. This study adds to a limited evidence base examining transport and time costs and examining their equity in a LMIC setting. An advantage of the study is the use of patient exit-interviews to minimise recall bias. We found that overall, the time cost associated with seeking outpatient care was 90 minutes on average, driven primarily by travel and waiting time. The burden of travel and waiting time were significantly greater for the poorest groups, while consultation time was similar across wealth groups; waiting time was also significantly higher among rural compared to urban respondents. In terms of direct costs, transport costs were almost double compared to medical costs, with a large majority not facing medical costs associated with care seeking. The burden of transport and medical expenditures were significantly among the least poor and higher among urban respondents. Patients spent more time travelling to public facilities and dispensaries than other provider types, but waiting and consultation time did not vary significantly by facility types. Patients were less likely to pay for care in public facilities, and ANC clients faced the longest waiting and consultation times.
Our estimate of travel time, half an hour on average, is similar to a previous study in Tanzania (Kowalewski et al. 2002), but lower than a study in Malawi which estimated a median 1 hour travel time to a health centre (Varela et al. 2019). However, our estimate of 1.9 USD for transport cost among who paid something only (0.4 USD for all clients) is lower than previously reported 2.5 USD for only who paid something in Tanzania (Perkins et al. 2009). Our finding that the poorest and rural patients faced significant time burden accessing care and paid less amount on transport cost is largely explained by the means of transport, since the poorest and rural patients often travel on foot. In many settings including Tanzania, lower-level public facilities such dispensaries are much preferred by poorest and rural patients as closest facilities and they offer ‘free’ PHC services (Asante et al. 2016; Macha et al. 2012; Mtei et al. 2012).
Patients in our sample spent on average 47 minutes waiting for MCH services, 56 minutes for ANC only, which is less than previously reported (1 hour and half) for ANC in Tanzania (Neke et al. 2018). Our analysis revealed that the poorest and rural patients waited longer than their counterparts which is consistent with the pattern observed across hospitals in developed countries (Landi et al. 2018; Laudicella et al. 2012; Siciliani & Verzulli 2009). Ours is the first study to reveal this evidence from a LMIC. However, the waiting time in developed countries is measured as number of days passed from the date a patient was added in the waiting list and the date of actual admission for treatment. In our setting, the longer waiting time to enter into the consultation room especially among the poorest and rural patients indicates the inadequate supply of health care –including shortage or maldistribution of health facilities and human resources for health (Macha et al. 2012). Tanzania like other developing countries has significant shortage of health staff with relatively more staff (e.g., specialists) in urban settings (Afnan-Holmes et al. 2015; Munga & Maestad 2009). The better-offs also waited for shorter time possibly because they able to pay informal payment or ‘under the table’ to health workers in order to jump the queue (Maestad & Mwisongo 2011).
Our study estimated about 16.3 minutes consultation time for ANC, which is slightly higher than 15 minutes reported earlier in southern Tanzania (von Both et al. 2006), higher than 10 minutes reported earlier in Dar es Salaam (Boller et al. 2003), but less than 20 and 48 minutes reported earlier in rural Ngorongoro district (Magoma et al. 2011) and in Kisarawe district for a mobile clinic (Neke et al. 2018), respectively. However, with the exception of consultation time reported by Neke et al. (2018), the other estimates falls below the recommended time between 30–40 minutes for ANC particularly first visit (WHO 2002). The consultation time in our study was generally similar across subgroups of patients. Our 13 minutes of consultation time for MCH services is relatively longer compared to approximately 5 minutes for outpatient consultation reported in Mozambique (Wagenaar et al. 2016) and Nigeria (Oche & Adamu 2013). Since medical doctors and clinicians use longer consultation time than nurses and midwives (Boller et al. 2003), there is a need for qualified staff to offer comprehensive consultation to clients.
The finding that shows patients spent on average more time on waiting than consultation is consistent with findings from previous studies in Tanzania for ANC (Neke et al. 2018) and elsewhere for outpatient consultation (Oche & Adamu 2013; Wagenaar et al. 2016). This is partly explained with the persistent shortage of healthcare workers and health facilities especially in poor countries (Darzi & Evans 2016; Oche & Adamu 2013; WHO 2016). We further found that services with shorter consultation time (e.g., PNC and vaccination) also had shorter waiting time. This implies that consultation time plays a significant role in explaining how long patients would wait for health care.
The total time cost of accessing and using MCH was largely driven by waiting time as previously reported in Tanzania (Kowalewski et al. 2002; Neke et al. 2018). In our study, the waiting time contributed almost 52.1% of total time cost, and about 53% when ANC service only considered. This is similar to previous studies that found half of the total time cost were spent waiting for ANC services in Tanzania (Kowalewski et al. 2002; Neke et al. 2018). However, when including the time spent travelling back home, travel time became the main contributor of total time cost. In terms of share for total direct cost, the main driver was transport cost which contains 64% of total costs, although it was less than a dollar on average. This finding is consistent to what reported earlier in southern Tanzania, where travel costs to access maternity services represented almost a half of total direct costs (Kowalewski et al. 2002). A similar pattern was reported in Nepal by Borghi et al. (2006), where transport cost took more than 50% of the total costs for clients seeking delivery care. However, in Bangladesh (Nahar & Costello 1998) and Nigeria (Dalaba et al. 2015) transport cost took relatively lesser share of about 20% and 32% of total costs for maternity services, respectively. Also, transport cost accounted for 42% of health expenditure in South Africa (Goudge et al. 2009). Our results imply that transport cost contribute significantly to total health care costs, and have the potential to deter individuals from accessing health care especially among the poorest and those residing in remote areas.
Moreover, we found almost 18% of women paid for MCH services in Tanzania. When restricting the analysis to public facilities (82% in our sample), about 13% paid for MCH services that are supposed to be offered free of charge in Tanzania. However, the likelihood of paying for MCH service was significantly low in public compared to private facilities. Since our sample included only patients at the facility level, it is likely that the extremely poorer never accessed health facility due to financial barrier, and possibly our results are reflecting the least poor and near poor patients only. Unsurprisingly, the direct payments for exempted MCH services have been reported before in Tanzania (Kruk et al. 2008; Maluka 2013; Manzi et al. 2005) and elsewhere (Dalaba et al. 2015; Goudge et al. 2009; Nahar & Costello 1998; Storeng et al. 2008). Paying for services which are exempted indicates a limited financial protection and weak enforcement of the exemption policy in Tanzania (Kruk et al. 2008; Maluka 2013). The inadequate budget allocation to the health sector (Ameur et al. 2012; Kruk et al. 2008) affects the enforcement of the exemption policy. In LMICs, however, expanding the resource envelop for health is constrained with limited fiscal space (Meheus & McIntyre 2017) and the larger share of people in the informal sector who hardly enrol into prepayment mechanisms (Adebayo et al. 2015; Dror et al. 2016).
Our findings have important policy implications. Prepayment mechanisms and user fee removal are important steps towards UHC (WHO 2010), but does not guarantee health care access due to other barriers such as transport and time costs which are often neglected (Ensor & Cooper 2004; O'Donnell 2007). Our finding indicates that the poorest and rural patients faced a relatively greater cost burden in terms of time loss from productivity partly because they have limited ability to pay for transport and/or health care; while their counterparts incurred huge direct cost because they have the ability to pay for transport and/or health care. Since the worse-off patients spent more time travelling, mostly on foot, to access public and lower level facilities, this reinforces the need for a greater investment in PHC facilities in order to bring quality health services closer to population as one of the recommended routes toward UHC (WHO 2019). This can be through PHC facility’s construction and renovation and an increase in supply of healthcare workers and medical commodities. These initiatives may help to reduce the time and direct costs of accessing and using PHC especially in remote and rural areas. Investing in PHC facilities will also meet patients’ needs and expectations and eventually reduce the time and travel cost incurred by patients bypassing closer PHC (Kahabuka et al. 2011; Kruk et al. 2009). In support of that argument, Tanzania implemented a Primary Health Care Services Development Programme (PHSDP) from 2007–2017 which involved construction and renovation of PHC facilities (Kapologwe et al. 2020; Maluka & Chitama 2017); and interestingly, the current government is continuing with construction and renovation of PHC facilities. Investing in PHC facilities aligns with the Alma Ata Declaration on PHC in 1978 (WHO 1978) and the Astana Declarations of 2018 (Walraven 2019) for the purpose of achieving health for all and the UHC goal (WHO 2019). Future research in Tanzania should examine the effect of investing in PHC on time and direct costs of access and using health care.
Our findings highlight the need for policy makers to think on how the benefit packages of health insurance schemes can cover transport costs, and move away from covering medical costs only. Other approach to reduce the travel time especially among the worse-offs is improving access to means of transportation though would not necessarily affect the transport costs (Karra et al. 2016). The success of this approach depends on other sectors beyond the health sector (e.g., transportation and infrastructure sector), which indicates the need of multisectoral approach to reduce access/ geographical barriers. Further evidence suggest some potential initiatives to reduce the costs of accessing care such as conditional cash transfers (Lagarde et al. 2009), vouchers to cover transport costs (Schmidt et al. 2010; Van de Poel et al. 2014), expanding outreach services (e.g. mobile clinics)(Ensor & Cooper 2004), establishing maternity waiting homes (Penn-Kekana et al. 2017) and implementing targeted policies for vulnerable and remote populations (Annear et al. 2019; Axelson et al. 2009). However, some of the suggested strategies are costly and may need a multisectoral collaboration.
This study has some strengths. First, we studied time costs of accessing and using MCH services as one of the cost aspects that received less attention despite its potential to limit health care access and use. Second, our time cost reflected a wider spectrum including time travelling, as well as waiting and consultation time. Third, this study examined the distribution of time and direct costs with equity implications. This is an important assessment as it shows who bears the cost burdens as an entry point for intervention. Fourth, we explored how time and direct cost varied by facility and service types, since previous studies largely focused on either one facility or service type. Lastly, we collected data through patient exit-interviews as an approach to reduce the recall bias as they had a recall period of less than 24 hours.
However, our study had some limitations. First, we did not assess the affordability of the amount paid due to unavailable data on household income/ expenditure to reflect the ability to pay. Second, we were unable to explore different coping strategies to finance costs of access and use due to data limitation. Third, we were unable to value the time costs (minutes) into monetary values because of unreliable income or wage rate data for rural and urban population. There is also considerable variation in measuring and valuing time lost into monetary terms, in some cases varies by age, gender, location or economic activity (Chima et al. 2003; McIntyre et al. 2006). Our sample also combined different service types which limits the process of valuing time lost. Fourth, although exit-interviews may have reduced recall bias, our findings reflect only those who were able to access and use health care. Lastly, we were unable to capture the hospitalisation costs for inpatient clients which adds significantly to cost burden, because of the survey design that focused on assessing quality of care for patients exiting after consultation.