at the clinic

reduces “duplicate testing” with both RDT and microscopy and significantly improves both positive and negative match rates (Lopez, Sautmann, and Schaner 2022a; see also figure 3.1, in chapter 3). This evidence, along with the high mismatch rates under microscopy testing, suggests that doctors may be treating based on incorrectly interpreted blood smears.

Of course, another explanation for nonindicated care may be that the facility simply does not have the required diagnostic tools and materials available. Table 7.1 shows that the clinics in the sample of the malaria case study were stocked out of some malaria test materials 31 percent of the time. Figure 7.4 shows that nearly 50 percent of patients without a malaria test nonetheless received an antimalarial, perhaps due to providers writing prescriptions when a test was unavailable. Knowledge gaps and capacity gaps may respond to PBF in the long run if facility directors respond to these incentives by training or hiring their staff more thoroughly and managing their supply chain better. These changes are likely to take time.

Figure 7.4 Malaria incidence and treatment outcomes by type of test conducted at the clinic

1.0 0.8 0.6 0.4 0.2 0 a. Share of patients with malaria and antimalarial purchases, by clinic test type

Not tested RDT only

Microscopy Positive home test Antimalarial purchased

b. Match between malaria test result and treatment, by clinic test type

1.0 0.8 0.6 0.4 0.2 0

Not tested RDT only

Microscopy Overall match Negative test and no treatment Positive test and treated Source: World Bank, using data from Lopez, Sautmann, and Schaner 2022a, 2022b.

Note: Panel a shows the share of patients who tested positive for malaria at home and the share who received an antimalarial. Panel b shows the match between treatment for malaria and malaria home test result. From left to right, antimalarial purchases increase from under 50 percent to over 70 percent, but the share of patients who were correctly treated worsens from 60 to 40 percent, largely due to overtreatment. RDT = rapid detection test.

However, the treatment rates for untested patients in figure 7.4 could also indicate that at least some doctors choose not to carry out a test, even if the materials are available, and prescribe based on clinical diagnosis alone. Some doctors may also order lab or RDT tests but then ignore the results. This is the effort or provider choice component of quality of care. The providers in the case study clinics may not test or may override a test result that does not conform to their assessment and prescribe an antimalarial anyway, despite clear health policies requiring a positive malaria test for an antimalarial prescription (Ministère de la Santé 2013).5

Further evidence that doctors choose not to follow diagnostic protocol and provide care that is not needed, despite having the knowledge and resources to do so, comes from the audit and standardized patient studies. These studies compare the actual diagnostic steps taken in daily clinical practice with behavior in a hypothetical vignette. They show that doctors only complete a fraction of the essential case-specific checks and prescribe treatment for conditions the patient does not have, against their better knowledge (see, for example, Das and Hammer (2007) for an early such study). Strikingly, doctors in India who practice both in public facilities and their own private practices deliver very different quality of care in the two settings and are 15 percent more likely to diagnose the patient correctly and 37 percent more likely to offer the correct treatment in their private office (Das, Holla, et al. 2016). The authors argue that the fee-for-service provision of health care in the private sector holds doctors more accountable and incentivizes them to provide higher quality care. Lack of provider effort and misaligned incentives are clearly important contributors to low quality health care and specifically the provision of nonindicated care.

Financial and nonmonetary incentives to prescribe and the role of PBF The role of provider incentives in connection with the overprovision of care is twofold. First, providers often lack incentives for diagnostic accuracy or low expenditure and therefore do not explicitly work to avoid overuse. Second, there is a range of external incentives that tend to encourage providers to sell medications or services that are not needed. In this context, PBF has the potential to improve incentives for accurate diagnosis and treatment allocation, but in practice the nature of the incentives offered often reinforces existing incentives to oversell. This problem relates to the challenges of evaluating the quality of health care allocation and accurately rewarding desired behavior. As health systems develop, outside incentives for overprovision may become even stronger

and reinforce performance pay incentives further. This subsection discusses the evidence for incentives to provide nonindicated care in LMIC health systems and in economies in transition, using the examples of China and India. It also briefly points to the potential touchpoints with PBF. The next subsection returns to the example of ANC from chapter 4 and shows evidence of nonindicated care in response to PBF.

Financial incentives are most often cited as the main driver of what is termed “physician-induced demand.” Induced demand occurs when the provider “influences a patient’s demand for care against the physician’s interpretation of the best interests of the patient” (McGuire 2000, 504). The provider can influence what the patient wants because health care is, in many aspects, a credence good, meaning that the patient does not observe the benefits and harms of the treatment directly and must rely on the judgment of the “expert,” here the health care provider. Even professional and altruistic providers may act against what they deem is in the patient’s best interests if other interests strongly compel them. There is ample evidence that providers respond to direct or indirect financial incentives, for example, by increasing prescription rates to boost sales profits. In a standardized patient study in China, prescription rates were 55 percent when the provider’s clinic benefited from the sale, compared with 10 percent when the patient indicated they would purchase a prescribed antibiotic at a nonaffiliated pharmacy (Currie, Lin, and Meng 2014).

But even if the volume of sales is not financially incentivized, as in most public health facilities and for salaried physicians,6 providers may be tempted to furnish nonindicated care. Concern about their reputation may motivate them to offer treatment rather than asking the patient to “wait and see” (Das, Hammer, and Leonard 2008). This is related to an often reported, but less often rigorously studied, cause for overprescription, namely, patients’ demand for powerful treatment. The theory of “induced demand” assumes that doctors work to persuade reluctant patients to buy more than they need, yet providers often report that patients arrive at the consultations with expectations about receiving specific treatments (Kotwani, Chaudhury, and Holloway 2012; Linder et al. 2014; van Staa and Hardon 1996). In the malaria case study, 57 percent of the health workers reported pressure from patients to prescribe unnecessary medications, and many named antibiotics and antimalarials specifically. Meanwhile, 55 percent of the patients said they believed they had malaria even before consulting with a physician.

A main motivation of the research that forms the basis of the malaria case study was to test the effects of patient demand on provider prescription practices. The authors conducted an experiment where they gave out vouchers that reduced the price of a simple ACT for malaria but varied on randomly selected days whether patients were informed about this discount or whether doctors could mention it at their discretion (Lopez, Sautmann, and Schaner 2022b). In the treatment arm where patients knew about the discount, antimalarial prescription rates were significantly higher and the match between treatment and illness was worse. Moreover, among the randomly selected clinics where providers received training and the allocation of malaria treatment improved, patient satisfaction declined. This suggests that, at least in this context, some nonindicated care is the result of “induced demand”: a nonnegligible share of patients demand malaria care despite not having malaria, and it is doctors who reluctantly bend to patient preferences.

Performance-based incentives can reinforce financial or other external incentives and increase the problem of overprovision when they reward the volume of care provided, which is often the case (Miller and Babiarz 2014). An incentive based on carrying out a procedure without verifying its appropriateness acts as piece-rate pay and encourages quantity over quality. This is more of an issue in curative care, where an important aspect of quality of care is to allocate treatment to the right recipients rather than give it to as many recipients as possible; however, as the next subsection shows, it also occurs in preventive care (here using the example of ANC).

The experience of high-income countries provides a preview of the problems to come in the overprovision of care when health systems become less resource constrained. The incentives to provide nonindicated care are further reinforced when patients have high incomes and therefore a high willingness to pay. Moreover, health policies that increase access and protect patients from unexpected shocks, such as health insurance coverage and subsidized public health care, also create a wedge between the costs patients face and the value they receive. In this situation, patients are willing to accept expensive treatments or diagnostics even if they provide only moderate benefits.

To give an example, the high rates of medication use in China are often attributed to the pharmaceutical policy that was historically aimed at promoting local drug companies, leading to uneven price regulation and high markups (Sun et al. 2008). As a result, drug sale revenues at providerowned pharmacies effectively cross-subsidized other health services (see also

Currie, Lin, and Meng 2014), and physicians were heavily incentivized to increase drug sales (Dupas and Miguel 2017). The abuse of antibiotics and corticosteroids was particularly severe, with between 55 and 85 percent of drug prescriptions containing an antibiotic (Currie, Lin, and Meng 2014; Currie, Lin, and Zhang 2011; Li et al. 2012; Sun et al. 2008). In a recent paper, Fang et al. (2021) describe the effects of the “zero markup policy” that was implemented in public hospitals to curb the problem. The policy was introduced in a staggered rollout across China starting in 2009, and in response, physicians shifted their treatment regimen so that patients’ drug expenses were substituted by nondrug expenses, keeping hospital revenues the same.

Another example is the ongoing shift in India to paying for health care with public health insurance but procuring it through private hospitals. Evidence from Rajasthan shows that changes in the fixed reimbursement rates for different types of services led to significant shifts in the supply of those services as well as to changes in (prohibited) charges to patients and rates of false claims (Jain 2021). These findings echo longstanding evidence, for example, from physician response to reimbursement policies in the US Medicare system (Cabral, Geruso, and Mahoney 2018; Rice 1983).

It is imperative to anticipate the rising costs of health care and the increased provision of nonindicated diagnostics and care in LMICs as their health systems transform and to design policies that can address both the underprovision and overprovision of health care. Pay-for-performance schemes can support these efforts if they can disincentivize nonindicated care while promoting needed care. An example of an intervention that had moderate success in reducing the use of antibiotics in rural China was a joint capitation and performance pay scheme piloted in Ningxia province (Yip et al. 2014).

Inappropriate or irrelevant care in ANC visits and the effects of PBF An inherent danger of PBF is that paying for certain actions can cause health workers to do them even if they are not strictly necessary or even harmful (Cors et al. 2011; Lyu et al. 2017; Morgan et al. 2019). This subsection explores overuse in the context of ANC using the three-gap framework and data presented in chapter 4. As touched on in chapter 4, although the available ANC data were not geared toward picking up overuse, there are indications of unnecessary treatment even at baseline, before the introduction of any PBF interventions.