In this study, we report high TB CNRs in Adama City. However, about one-fifth of the TB cases notified were contributed by patients from outside the catchment population. This overstates the urban CNR and underestimates the CNR of the neighboring catchment areas. However, disaggregating the data about TB cases by place of residence offset the high CNR in urban and low CNR in adjacent communities, which could give the real picture of TB case notification in the areas. Like other studies in urban populations [19,20,21], studies from Ethiopia show that the urban population, representing only 8% of the country’s population, contributed 11% of the total TB cases notified . This phenomenon could be due to patients coming from neighboring catchment areas for access to better diagnostics and availability of technical expertise, thereby increasing urban poverty, overcrowding, urban migration, HIV infection, and disease transmission . This pattern of care seeking might have contributed to the increasing focus on urban TB programs.
The Zero TB Cities Initiative is one of the major efforts to combat TB and its transmission . Such efforts, however, should consider detailed analysis of case finding in urban areas by place of residence, which is not commonly done . Without specific analysis with regard to place of residence, the CNR of adjacent rural districts may be underestimated and the urban CNR may be inflated. Thus, high CNRs may give the impression that targets are being met in urban areas and may lead to reluctance to target TB in surrounding areas by urban TB programs. Inadequate data can, in turn, affect resource allocation. An increase in resources for urban TB programs could undermine efforts to reach surrounding catchment areas, which could contribute to continued disease transmission in peri-urban areas.
Evidence has shown that disaggregation of data about notified TB cases by urban or rural place of residence has reduced the CNRs of areas that were known for higher CNRs and increased the CNRs of areas that did not report many cases. A ten-year review of TB cases notified by districts in southern Ethiopia showed that about 23% of the TB cases notified came from other catchment areas or districts. The disaggregation of TB cases by their residence reduces the high CNRs of urban areas and increases the lower CNRs in adjacent rural areas .
Higher CNRs could be driven by patient preference, better service quality, increased geographic accessibility, better community awareness, and access to better diagnostics and treatment . Analysis of CNR by place of residence offsets both under- or over-reporting in urban and rural communities. Failure to consider this reality may contribute to urban bias, with the possible resource implications mentioned above, and may affect the type and magnitude of interventions designed by NTPs. Therefore, interventions in urban settings should analyze cases by place of residence, consider factors underlying higher CNRs, and design appropriate interventions to reach TB cases missed in the urban population.
While there are clear justifications for prioritizing TB in urban areas, other factors should be considered to ensure efficient use of scarce resources. In most resource-limited settings, a significant portion of urban health-service seekers come from rural areas, sometimes travelling long distances, due to lack of quality health services in remote areas. In addition, frequent bidirectional movements of people between urban and rural areas  for various purposes may increase disease transmission. Since most project-driven TB case-finding efforts use accessibility, feasibility, and yield as criteria for selection of intervention sites, there is a high probability that remote, rural, and low-case-notifying areas will be left behind. This issue suggests the need for review of the deceptively high CNRs in urban areas, so that efforts to reach remote areas or areas with low CNRs are not undermined. Urban areas, with their higher populations and compromised socioeconomic conditions, require interventions to strengthen the networking of urban-rural programs to reach their actual catchment populations in order to improve case finding. Ending TB will only be possible if the urban rural disaggregation of data leads designing interventions and reaching missed cases whether they are not diagnosed which will remain to be source of continued transmission or diagnosed and not notified by the health system.
In the absence of subnational TB prevalence reports, TB investment and the performance of TB programs should be evaluated in the light of actual CNRs, using data about patients’ place of residence as well as their place of treatment. This analysis will ensure the use of accurate data for decision-making and action. Further analysis of program limitations related to referral linkages, treatment success, and program capacity to reach target populations is needed.
Failure to account for patients from Adama City who might have sought care in surrounding rural areas could have led to underestimating the CNR in urban areas, and inability to verify the address registered, whether it is place of residence or temporary address, might have affect the results. Our study is limited to Adama city and did not measure the case notification and treatment success of the surrounding population which could give better estimate of the contribution of rural communities and identify TB cases from urban who received treatment in the rural. Further study is required to understand the patient flow between urban and rural areas to estimate CNRs in such settings.
This study could be generalized to urban sites were TB patients receive services within the catchment population and receive patents from surrounding areas that could increase the reported case notification of urban sites. In areas where patients are not strictly receiving treatment within their place of residence such underestimation of the real picture of the surrounding sites could be noticed while the urban areas could have higher notification rate which could be misleading. The results of the study should be cautiously interpreted as the sample size across the two groups was small to pick statistical difference among the two groups.