coverage tree

Figure 7.1 Nonindicated care for a specific condition in the effective coverage tree

In need of care Consults Correctly diagnosed

Incorrectly diagnosed

Relevant care provided

No care or irrelevant care provided despite correct diagnosis

Correctly treated and maximum gain is realized

Some care is relevant but maximum health gain is not realized

(3) Some relevant and irrelevant care

Irrelevant care provided (4) (2)

Population

Not in need of care Does not consult No care provided

Consults Correctly diagnosed No care provided

Incorrectly diagnosed

Irrelevant care provided even though not needed (1)

Does not consult No care provided

Source: World Bank.

Note: At the end of each branch of the tree, the green color denotes a desirable outcome, and orange denotes an undesirable outcome. Purple border around box indicates irrelevant care that uses scarce resources without health benefits or even causing harm.

This chapter begins by summarizing the evidence on nonindicated treatment in LMICs, especially in curative care, drawing frequently on data collected for a study of malaria treatment at community health clinics in Bamako, Mali in 2016, which is described in box 3.1, in chapter 3 (Lopez, Sautmann, and Schaner 2022a, 2022b). It then turns to a discussion of how performance pay interacts with the provision of nonindicated care. Finally, the data on antenatal care (ANC) from chapter 4 are used to show that PBF may worsen the provision of preventive measures such as vaccines, including in ways that could harm the patient.

Incorrect diagnosis and nonindicated treatment in LMICs Nonindicated care is an important manifestation of low health care quality. Many arms in the “effective coverage tree” lead to the patient receiving irrelevant care, instead of or in addition to relevant care for their condition, as illustrated in figure 7.1. Relative to preventive care, where the needs

assessment is often comparatively straightforward (for example, all children under five should be vaccinated), curative care is particularly at risk of misdiagnosis and consequently nonindicated care. Thus, an important task of the health care system is to provide an accurate diagnosis ensuring that each condition receives the appropriate treatment. Nonindicated care occurs (often alongside undertreatment) when the patient is misdiagnosed and treated for the wrong condition, when the provider treats several conditions at once to “cover their bases,” or when the provider reaches for a more powerful, more invasive, or more expensive treatment than is needed, such as giving an injection instead of an oral tablet.

Consider the example of treatment for P. falciparum malaria. The treatment for uncomplicated or simple malaria is artemisinin-based combination therapy (ACT), usually given in tablet form. In severe malaria cases, the patient will initially receive parenteral (intravenous or intramuscular) antimalarials and should be admitted to intensive care (Pasvol 2005; Trampuz et al. 2003). Nonindicated treatment for malaria can occur in multiple ways. A patient may be mistakenly diagnosed with malaria, for example, if the provider conducts a microscopy test and misinterprets the result (branch 1 in figure 7.1).1 Even if the patient has malaria and is correctly diagnosed, the doctor may provide nonindicated care, such as treatment for severe malaria in the case of an uncomplicated malaria infection (branch 2). The provider might even knowingly substitute irrelevant for relevant care, for example, by giving an antibiotic instead of an antimalarial because the clinic is stocked out of ACTs (branch 3). Last, the provider might mistake the diffuse symptoms of uncomplicated malaria for a different illness, such as a bacterial infection, and wrongly prescribe an antibiotic (branch 4).

Identifying misuse of care requires a third-party diagnosis for verification, which is not always possible. An exception is presented by malaria rapid detection tests (RDTs), which can be easily and quickly administered with minimal training and detect parasite antigens even after treatment has started. This approach makes it possible to measure treatment received conditional on true malaria status. Researchers took advantage of this in the studies that form the basis of the case study in box 3.1, in chapter 3 (Lopez, Sautmann, and Schaner 2022a, 2022b).

The malaria data from Mali are used as a case study and referred to throughout the chapter. Patient intake and exit interviews were conducted at 60 community health clinics in the capital of Mali, Bamako. Table 7.1 summarizes the characteristics of the clinics. Although some of the clinics

are large, during a given shift there are typically one to three physicians on staff, along with nurses, midwives, a pharmacist, a lab technician, and some nonmedical staff. In a novel approach to measuring the misallocation of treatment, the patients with acute symptoms were “re-diagnosed” by conducting a malaria test in a follow-up visit at the patient’s home one day after their clinic visit. The RDT used in these visits, CareStart HRP2(Pf), performed well in quality checks, with less than 1 percent false positives and 91 and 100 percent correct detection rates for low and high parasite loads, respectively (WHO 2015).

Figure 7.2 shows the results for the correct allocation of treatment. The quality of malaria care that patients receive is worryingly low. On the one hand, there is a large amount of nonindicated care, corresponding to branches 1 and 2 in figure 7.1. Although purchase rates are lower than prescription rates, 40 percent of patients with a negative malaria test at home took a malaria treatment, and nearly 50 percent of them received an intravenous line or injection, which, according to official treatment guidelines, is only indicated for severe malaria cases (Ministère de la Santé 2013). Among those with a positive test, over 65 percent (correctly) received treatment, but a large majority of these patients received more expensive severe malaria care. This is despite few reports of severe symptoms in the intake interviews and an estimated rate of severe malaria in this population of 10 percent of malaria cases (PMI 2015). Remarkably, although these are patients who decided to visit a clinic and seek care, there is parallel substantial undertreatment: more than 20 percent of the patients with a positive

Table 7.1 Overview of clinics in the malaria case study in Mali

Variable Mean SD

Self-reported patient load per day Clinic has a laboratory for malaria microscopy 29.7 22.1

83% 38%

Clinic has a pharmacy/dispensary 100% n.a. Average number of staff who can prescribe antimalarials 11.1 4.4 Days with stockouts of any malaria test materials 31% 46% Days with stockouts of all malaria test materials 0.8% 9.1% Days with stockouts of any malaria drugs 69% 47% Days with stockouts of all malaria drugs 1.7% 13% Sources: World Bank, using data from Lopez, Sautmann, and Schaner 2022a, 2022b. Note: The study included 60 public clinics (Centres de Santé Communautaire) in Bamako, Mali. Data were collected in a baseline survey and on six observation days per clinic. Baseline information is missing for one study clinic. n.a. = not applicable; SD = standard deviation.