In Guinea, the NTP tuberculosis case notification rate decreased from 120 cases per 100,000 population in 2011 to 100 cases per 100,000 of the population in 2014, resuming an upward trend after 2015 (Figure 1). From 2012 to 2013, the trends were down slightly, but higher than in 2014. After this year, notifications of new cases and relapses for all forms of TB started to increase. The same is true for new clinically diagnosed cases, pulmonary relapses, and clinically confirmed cases and pulmonary relapses. The recovery of the upward trend just after the historic decline recorded in 2014 is very evident and continues to increase each year (Figure 1).
From 2011 to 2014, notification rates for both all forms of TB cases, bacteriologically confirmed and clinically diagnosed pulmonary cases showed negative variations, i.e., a decrease in the number of cases detected each year with a peak of –26 for bacteriologically confirmed TB cases in 2014. As of 2015, annual changes ranging from 7% for new TB cases clinically diagnosed to 17% for TB cases bacteriologically confirmed (Figure4).
About the tuberculosis surveillance system, out of 13 standards and criteria developed by WHO, five were reached by the NTP in 2019 compared to only 3 in 2015 (Table 3). This means that the surveillance system deserves targeted, long-term action to meet the challenge of screening and monitoring patients on treatment.
The analysis of the time series of NTP notifications shows essentially a gap with a much larger trough between 2014 and 2015. From 2011 to 2018, cascades are observed over the years concerning the reported cases of tuberculosis in all forms in the range of 2000 to 4000 cases per quarter and vary from year to year. The periods between 2014 and 2015 are those for which, it is notified the lowest rates (2000 cases) compared to other years that recorded at least 2500 cases (Figure 2).
For Ebola cases, if the number of cases is less than 2200, except for the period of July 2014 which records up to 4000 cases, the data for TB cases of any reported form vary from year to year. These variations, much more marked by a decrease in 2014, would be based on the availability of providers, access to the service, and the choice of patients because of the epidemic context. Thus, between July 2014 and January 2015, fewer cases (2,200) were reported than in January and July 2014 (more than 3,000), this highlights a decline in the Ebola rate.
By examining reported cases of tuberculosis in all forms, data are available for almost all years and above 2,500 cases per quarter, but these trends were down in 2014. After an increase of more than 3000 this year, a drop-off point is again observed shortly before 2015, when Ebola cases will see a sharp increase.
The success rate has gradually increased over the years, despite the severity of the Ebola outbreak. The diagnosed patients were followed and put on TB treatment. Outcome indicators did not vary sufficiently during the epidemic period; the success rates range from 76% to 94% for all years ( table1).
The Ebola incidence evolved rapidly to acme and then declined, with the most significant proportion recorded before 2014 (more than 500 cases). The incidence of tuberculosis, meanwhile, decreased by –1500 cases between 2014 and 2015 before fluctuating the following year positively and then remaining until 2016.
The analysis of auto-correlation curves for the incidence of tuberculosis and Ebola does not show any significant lag for bacteriologically confirmed TB cases, the therapeutic success rate, and the Ebola success rate, although all are independent but not seasonally adjusted (Figure 3). After converting these series to stationary time series, the cross-correlation test between the time series of tuberculosis and Ebola shows a significant lag of –0.6 (60%) for all forms of TB, which corresponds to the sharp decline in the incidence of TB seen at the height of the Ebola outbreak in 2014 (Figure 4). However, no significant lag is observed in the cross-correlation test for the therapeutic success rate an Ebola times series despite seasonal adjustment of the time series (Figure 4).