Since the case detection rate is estimated indirectly and is uncertain, another indicator that can be measured more directly would be desirable. This indicator is the rate at which prevalent case-patients are recruited by TB programs, referred to here as the patient diagnostic rate. In practice, this indicator can be measured as follows: the number of newly reported cases (i.e., never treated) of smear-positive TB per 100,000 population per year (notification rate) divided by the prevalence of new cases of smear-positive TB per 100,000 population. The numerator is obtained from surveillance data and the denominator from a prevalence survey. The denominator represents the population at risk for case detection, the numerator those actually detected. At present, the proposal is to restrict patient diagnostic rate to smear-positive cases because smear microscopy is currently the most widely applied tool to confirm TB in countries with high rates of disease. The proposal is restricted to new cases, since this best captures the effects of case detection. The prevalence of previously treated TB depends strongly on the cure rate. Patient diagnostic rates in countries conducting and reporting a prevalence survey during the past decade are presented in the Table.

A more refined estimate of patient diagnostic rate may be obtained by stratification for important variables that are recorded routinely, such as age, sex, urban versus rural areas, and DOTS versus non-DOTS areas. DOTS areas are defined as those that have adopted the WHO TB control strategy. Such stratification may help identify TB priorities for strengthening case finding and assess the effect of DOTS. In countries with a high prevalence of HIV infection, separate estimates for persons with and without HIV infection indicate differences in the patient diagnostic rate and death rates between TB patients with and without HIV co-infection (6).

The quantitative relationship between the case detection rate, patient diagnostic rate, and expected program effect depends on the way we conceive case detection. Two approaches have been used in the past, perhaps best explained with the models of Styblo (model 1) (2,3) and Dye et al. (model 2) (4).

Model 1 assumes that cases are either detected after an average of 4 months or not at all (2,3). Patients whose cases are not detected either die or self-cure after an average of 2 years. Self-cure refers to patients reverting to latent infection without being treated. In model 2 (4), cases are detected at a certain rate (patient diagnostic rate), and the patients die or self-cure at a certain rate. The proportion of cases detected in model 2 thus depends on the relative size of these two rates: the larger the patient diagnostic rate, the larger the case detection rate and the shorter the average delay. As a result of these different assumptions, the same case detection rate of 70% is associated with a larger patient diagnostic rate and a larger impact on TB prevalence in model 2 than in model 1 (Appendix). In the absence of HIV infection, a case detection rate of at least 70% corresponds with a patient diagnostic rate of at least 0.84 per person-year in model 1 and a patient diagnostic rate of at least 1.17 per person-year in model 2.

How do these model targets compare with values of patient diagnostic rates we observe in the real world? A rough, indirect estimate of patient diagnostic rate in the Netherlands is 2.5 per person-year (Appendix). Of more relevance may be the direct estimates in the countries with high rates of TB (Table): the patient diagnostic rate was 0.24 in China, 0.43 in Korea, and 0.51 in the Philippines. These three countries did not meet the goal for case detection by models 1 or 2.

For the patient diagnostic rate to be a useful indicator, the best reporting rate should be obtained. For instance, if general hospitals in China, or the private sector in the Philippines and Korea, fail to notify the patients they treat, the patient diagnostic rate will be underestimated (the same limitation applies to the case detection rate). Therefore, the use of patient diagnostic rate is not an alternative to a good reporting system but supports the development of such a system. If the notification system detects most cases (e.g., with a patient diagnostic rate exceeding the goal of model 2 of 1.17), then reporting data may be used exclusively to monitor trends, as is done in countries with low rates of disease.

A limitation of the patient diagnostic rate is that measuring TB prevalence is complicated and costly with the current standard methods, which require the use of mobile chest radiograph equipment as a screening tool. However, this limitation can be overcome. High standard prevalence surveys have been shown to be feasible (Table). Moreover, their cost represents a small proportion of the cost of control programs. TB control programs in the 22 countries with high rates of the disease annually cost an estimated U.S. $940 million, approximately half of which is within the TB program budget, while the other half represents health infrastructure costs (16). Twenty-two national surveys, performed with current standard methods once every 5–10 years, would cost approximately U.S. $25–$50 million in total, i.e., <U.S. $10 million per year. This cost represents at most 1% of the cost of TB control programs.

Nevertheless, new survey methods, validating other diagnostic algorithms or developing new diagnostic methods, that did not require mobile chest radiographs would be beneficial. They would promote the measurement of TB case detection and program effect in the 22 countries with high rates of disease and in other high incidence-countries with limited resources, especially Africa.

The patient diagnostic rate is a measurable indicator for detecting patients with previously untreated cases of smear-positive TB. The expected effect of a TB control program on transmission increases with an increasing value of this indicator. A patient diagnostic rate of >0.84 would correspond to the original WHO goal proposed by Styblo of detecting >70% of incident cases. A patient diagnostic rate of >1.17 would meet the goal of 70% case detection as used by Dye et al. to project the effect of the DOTS strategy (4). On the basis of further evidence about patient diagnostic rates and associated TB program impact, a revised goal may be formulated in the future.

While monitoring performance is extremely useful in the short-term, monitoring effects, or at least the trend of TB incidence, is most important in the medium- and long-term. Programs aimed at reducing TB prevelance can assess whether the decrease is occurring through reporting rates, if case detection is good, or by carrying out prevalence surveys every 5–10 years, if the completeness of case detection varies or is uncertain. Prevalence surveys would provide direct information on indicator 23 for measuring progress towards meeting the Millennium Development Goals (1). Monitoring effect through prevalence surveys allows the patient diagnostic rate to be measured and the risk factors for nondetection to be identified by the health service. Developing new diagnostic methods, obviating the need for chest radiographs, would be extremely helpful for such surveys. Monitoring TB is recommended through prevalence surveys in countries with high rates of disease until reporting rates have been shown to provide sufficient information on TB trends in that particular setting.