7.3 Efficiency of effective coverage provision

Figure 7.3 Efficiency of effective coverage provision

(i) Individuals who consult and receive appropriate care (%) (iii) Per person cost of appropriate care

(Daniels et al. 2017; Das, Chowdhury, et al. 2016; Das et al. 2015; Das, Holla, et al. 2016; Kwan et al. 2018; Sylvia et al. 2017).

Even if there are no negative health consequences for the patient or the public, nonindicated treatment uses up valuable resources. In the Tanzania study, for example, fully 81 percent of the patients received such wasteful care, and in the standardized patient studies in Hussam et al. (2020), more than 70 percent of health care expenses were medically not indicated. In LMICs, where basic health care coverage continues to be low, unnecessary care for one person may imply that another person does not receive the care they need. The regular stockouts in Bamako’s public clinics demonstrate that even in urban areas, medication and materials are often scarce (table 7.1).

To capture the opportunity costs of nonindicated care, this chapter proposes a back-of-the-envelope calculation of the “efficiency” of providing effective coverage, as illustrated in figure 7.3. This measure expresses what

(ii) Actual per person cost of care

Source: World Bank.

Note: The efficiency of effective coverage provision is calculated based on (i) the share of patients who consult and do (versus do not) receive the correct care, (ii) the actual cost of health care per person, and (iii) the cost of providing the optimal level of care per person. The product of (i) and (iii) divided by (ii) gives the share of total expenditure going toward appropriate care.

proportion of health care expenditure goes toward care that fills a correctly identified health care need. As touched on in chapter 3, it is generally very difficult to calculate this share without data specifically gathered for this purpose. However, table 7.2 illustrates the idea using the malaria case study, with approximations from the data. Table 7.2 shows the share of patients who tested positive for malaria and received malaria treatment as well as the share of patients who tested negative and did not receive malaria treatment. These are the patients who (in approximation) received appropriate care. The table shows that these are only 51 percent of all patients. In addition, exit interview data on payments were used to obtain the per-person price of care for these two groups. The lower per-person price of care of patients who (correctly) did not receive an antimalarial reflects that malaria care is relatively expensive. In these data, the average per-person price for all patients was CFA 5,396, or approximately US$8.99 at 2016 exchange rates (CFA 600 per US dollar). Using this number in the denominator, the share of expenses going toward treatment that correctly matches malaria status is approximately 45 percent, implying that 55 percent of health care spending by patients is at least partially wasteful.

The calculation uses patient prices rather than true cost, and the observed price of care when malaria treatment choices match malaria status rather than the true price of appropriate care. Several caveats are therefore in order. First, public health care in Mali is at least partly subsidized.

Table 7.2 Approximating the efficiency of health care provision using data from 60 community health centers in Bamako, Mali

Description Indicator

Appropriate treatment

Positive match: malaria test was positive and the patient received malaria treatment as part of the prescription. Negative match: malaria test was negative and the patient did not receive malaria treatment. 19% of patients, at visit cost of CFA 5,507 each

32% of patients, at visit cost of CFA 4,312 each

All treatment

Per person average visit cost CFA 5,396

Share of patient expenditure going to appropriate care

Calculation: (19% x 5,507 + 32% x 4,312) / 5,396 Efficiency: 45% Source: World Bank, using data from Lopez, Sautmann, and Schaner 2022a, 2022b. Note: This table uses data from clinic exit interviews on prescriptions received and the price paid for treatment, combined with information from a malaria test conducted in a follow-up visit at home. The average exchange rate was approximately CFA 600 per US$.

Unless the prices that patients pay are proportional to the true cost of services, the 45 percent expenditure share may not represent the share of the total social cost that goes toward providing appropriate care. Second, health care providers would likely have to spend more diagnostic effort per patient to improve the allocation of care for all patients. As a result, the labor cost of providing appropriate care is higher than the current labor cost per visit.

Last, the study assumed that the cost of care for appropriate malaria treatment is CFA 5,507, or equivalently what malaria-positive patients who receive an antimalarial spend. However, an unusually high share of these patients received treatment that indicates severe malaria (and this treatment tends to be more expensive than a simple ACT). Similarly, the study assumes that the cost of care for appropriate treatment for conditions other than malaria is given by the observed price of visits that did not include malaria. However, 63 percent of the patients in the study received an antibiotic, often prescribed for respiratory issues or diarrhea, and it is likely that some of these drug prescriptions were not indicated. Both factors imply that the cost of providing appropriate medications may be lower than the observed medication costs. Chapter 3 briefly discusses how the efficiency of care might be measured in a more complete manner.

Although these numbers are therefore not precise, a key takeaway from the efficiency-of-care indicator is that even in basic primary care in low-income countries, resources go to waste because patients are treated for illnesses they do not have. The problem of nonindicated care is even more serious in high-income countries, partly driven by differences in medicine use patterns due to epidemiology and age profiles. Overuse is particularly severe for high-cost diagnostic testing, such as colonoscopy (Kruse et al. 2015) and medical imaging (FDA 2010), and for surgical procedures (Chan et al. 2011), and there is systemic growth in the nonindicated use of medications for psychological and degenerative conditions. Busfield (2015) highlights that many drugs prescribed to large percentages of the population and heavily promoted by pharmaceutical companies may have few proven benefits, such as antihypertensives (Diao et al. 2012), antidepressants (Ioannidis 2008), and antipsychotics for dementia (Banerjee 2009). The evidence overwhelmingly shows that poor quality of care in the form of overprescription and overdiagnosis is a pressing problem in mature health systems. Correspondingly, it is likely that growing issues with nonindicated care will be seen in LMICs as their health systems begin to transform. The following subsections discuss the reasons in more detail.

What causes nonindicated care and what is the role of financial incentives?

Multiple factors contribute to the provision of unnecessary or inappropriate care. Building on the effective coverage framework, the patient may be incorrectly diagnosed (branch 1) and as a result receive a treatment that is not needed. Alternatively, the doctor’s diagnosis may be accurate—or at least, the doctor may be capable of accurately diagnosing—but he or she is choosing to provide inappropriate care (branches 2 and 3). Correspondingly, along the lines of chapter 3, the causes of nonindicated care are classified into knowledge and capacity gaps versus provider effort.

Knowledge and capacity gaps versus provider choice Errors in diagnosis—or in prescription choice after diagnosis—may be the result of knowledge or capacity gaps. For example, the provider may not know the diagnostic protocols or may follow the protocols but draw faulty conclusions. Providers who are uncertain about the correct treatment often perceive the risks of overtreating to be lower than the risk of undertreating (see, for example, Krockow et al. 2019) and therefore tend to overprescribe. In the malaria case study, for example, there are many signs that diagnostics are poor and physicians overtreat as a result. Figure 7.4 splits treatment outcomes by the type of malaria test that was administered at the clinic: no test, RDT only, or microscopy test. In general, microscopy tests carried out by an experienced technician are considered the “gold standard” of malaria testing. However, in field conditions, microscopy can perform poorly, for example because dust particles may be mistaken for malaria parasites. Panel a in figure 7.4 shows the malaria rates for the home RDT test and the share of patients who bought an antimalarial. Panel b shows the match between malaria treatment received and malaria status. Patients who received a microscopy test at the clinic (alone or in combination with an RDT) had relatively low rates of malaria in the home test and yet in nearly 80 percent of the cases received an antimalarial. As a result, compared with patients who did not receive a test at all, patients who were tested with microscopy had very low match rates between malaria status and malaria treatment. This is largely due to very high rates of overtreatment and low shares of patients with a “negative match” (that is, patients who did not have malaria and correctly did not receive an antimalarial).

Interestingly, both positive and negative match rates are higher when only an RDT is used than when microscopy is conducted. Moreover, the study found that providing training to clinic staff on the accuracy of RDTs