The Challenges of Conducting Clinical Research on Neglected Tropical Diseases in Remote Endemic Areas in Sudan

<p>The Challenges of Conducting Clinical Research on Neglected Tropical Diseases in Remote Endemic Areas in Sudan</p

Study location.

<p>Tabarak Allah, Gedaref province, Sudan.</p

Unexpected visitor during a GCP/GCLP training workshop in Tabarak Allah Health Centre, Sudan.

<p>Unexpected visitor during a GCP/GCLP training workshop in Tabarak Allah Health Centre, Sudan.</p

NIDIAG study issues identified in Sudan and lessons learnt.

<p>NIDIAG study issues identified in Sudan and lessons learnt.</p

Study profile: selection of study patients.

<p>Patients from 12 Médecins Sans Frontières sites in Uganda, Sudan, Angola, Central African Republic, Republic of Congo and Democratic Republic of Congo, 1995–2006. ^a Among these 906 exclusions there are 125 patients that relapsed before 6 months (hence falling out of the scope of this study); ^b Tolerance window up to 9 months.</p

Evolution of the CSF leucocytes count by final treatment outcome, by treatment group and overall.

<p>CSF leucocytes count expressed as the median and interquartile range (IQR). Cohort of 1822 patients having a leukocytes count performed at 6 months and not relapsing at 6 months or earlier, who had a complete follow-up. Values at month 0 are pre-treatment measurements. The number of patients per group is shown at each time point. Dotted lines are used to signify that samples over time contain some different patients.</p

Baseline characteristics of patients included in each analysis.

<p>First analysis: leukocytes at 6 months, excluding relapses at 6 months; Second analysis: leukocytes at 6 months, including relapses at 6 months, two-step algorithms. Combination treatment: within this selected cohort, it included melarsoprol-eflornithine, nifurtimox-eflornithine and melarsoprol-nifurtimox combinations. Coma score: Glasgow Coma Scale assessing the level of consciousness. Interpretation: 3–8 = severe impairment; 9–12 = moderate impairment; 13–14 = mild impairment; 15 = normal <a href="http://www.plosntds.org/article/info:doi/10.1371/journal.pntd.0001662#pntd.0001662-Teasdale1" target="_blank">[13]</a>; Karnofsky index <a href="http://www.plosntds.org/article/info:doi/10.1371/journal.pntd.0001662#pntd.0001662-Mor1" target="_blank">[14]</a>.</p

Comparison of several two-steps (6 and 12 months) algorithms for early outcome determination.

a<p>As reported by Mumba 2010, “algorithm C”: includes deaths as treatment failures and patients with incomplete follow-up;</p>b<p>Same algorithm tested with our dataset including only laboratory-confirmed outcomes. LR: Likelihood ratio. False cured: fraction of patients that are wrongly classified as cured by the algorithm.</p

Performance of different cut-off values of CSF leucocytes count at 6 months for the detection of relapse.

<p>Performance of different cut-off values of CSF leucocytes count at 6 months for the detection of relapse.</p

The impact of passive case detection on the transmission dynamics of gambiense Human African Trypanosomiasis

<div><p>Gambiense Human African Trypanosomiasis (HAT), or sleeping sickness, is a vector-borne disease affecting largely rural populations in Western and Central Africa. The main method for detecting and treating cases of gambiense HAT are active screening through mobile teams and passive detection through self-referral of patients to dedicated treatment centres or hospitals. Strategies based on active case finding and treatment have drastically reduced the global incidence of the disease over recent decades. However, little is known about the coverage and transmission impact of passive case detection. We used a mathematical model to analyse data from the period between active screening sessions in hundreds of villages that were monitored as part of three HAT control projects run by Médecins Sans Frontières in Southern Sudan and Uganda in the late 1990s and early 2000s. We found heterogeneity in incidence across villages, with a small minority of villages found to have much higher transmission rates and burdens than the majority. We further found that only a minority of prevalent cases in the first, haemo-lymphatic stage of the disease were detected passively (maximum likelihood estimate <30% in all three settings), whereas around 50% of patients in the second, meningo-encephalitic were detected. We estimated that passive case detection reduced transmission in affected areas by between 30 and 50%, suggesting that there is great potential value in improving rates of passive case detection. As gambiense HAT is driven towards elimination, it will be important to establish good systems of passive screening, and estimates such as the ones here will be of value in assessing the expected impact of moving from a primarily active to a more passive screening regime.</p></div