Journal Article > ResearchFull Text
Disasters. 2006 September 1; Volume 30 (Issue 3); 364-376.; DOI:10.1111/j.0361-3666.2005.00326.x
Grais RF, Coulombier D, Ampuero J, Lucas MES, Barretto AT, et al.
Disasters. 2006 September 1; Volume 30 (Issue 3); 364-376.; DOI:10.1111/j.0361-3666.2005.00326.x
Emergencies resulting in large-scale displacement often lead to populations resettling in areas where basic health services and sanitation are unavailable. To plan relief-related activities quickly, rapid population size estimates are needed. The currently recommended Quadrat method estimates total population by extrapolating the average population size living in square blocks of known area to the total site surface. An alternative approach, the T-Square, provides a population estimate based on analysis of the spatial distribution of housing units taken throughout a site. We field tested both methods and validated the results against a census in Esturro Bairro, Beira, Mozambique. Compared to the census (population: 9,479), the T-Square yielded a better population estimate (9,523) than the Quadrat method (7,681; 95% confidence interval: 6,160-9,201), but was more difficult for field survey teams to implement. Although applicable only to similar sites, several general conclusions can be drawn for emergency planning.
Journal Article > ResearchFull Text
Popul Health Metr. 2012 September 4 (Issue 1); DOI:10.1186/1478-7954-10-18.
Caleo GNC, Sy AP, Balandine S, Polonsky JA, Palma PP, et al.
Popul Health Metr. 2012 September 4 (Issue 1); DOI:10.1186/1478-7954-10-18.
During 2010, a community-based, sentinel site prospective surveillance system measured mortality, acute malnutrition prevalence, and the coverage of a Médecins Sans Frontières (MSF) intervention in four sous-préfectures of Lobaye prefecture in southwestern Central African Republic. We describe this surveillance system and its evaluation.
Journal Article > ResearchFull Text
Lancet. 2006 April 22; Volume 367 (Issue 9519); DOI:10.1016/S0140-6736(06)68580-2
Ferradini LLF, Jeannin A, Pinoges LLP, Izopet J, Odhiambo D, et al.
Lancet. 2006 April 22; Volume 367 (Issue 9519); DOI:10.1016/S0140-6736(06)68580-2
BACKGROUND: The recording of outcomes from large-scale, simplified HAART (highly active antiretroviral therapy) programmes in sub-Saharan Africa is critical. We aimed to assess the effectiveness of such a programme held by Médecins Sans Frontières (MSF) in the Chiradzulu district, Malawi. METHODS: We scaled up and simplified HAART in this programme since August, 2002. We analysed survival indicators, CD4 count evolution, virological response, and adherence to treatment. We included adults who all started HAART 6 months or more before the analysis. HIV-1 RNA plasma viral load and self-reported adherence were assessed on a subsample of patients, and antiretroviral resistance mutations were analysed in plasma with viral loads greater than 1000 copies per mL. Analysis was by intention to treat. FINDINGS: Of the 1308 patients who were eligible, 827 (64%) were female, the median age was 34.9 years (IQR 29.9-41.0), and 1023 (78%) received d4T/3TC/NVP (stavudine, lamivudine, and nevirapine) as a fixed-dose combination. At baseline, 1266 individuals (97%) were HAART-naive, 357 (27%) were at WHO stage IV, 311 (33%) had a body-mass index of less than 18.5 kg/m2, and 208 (21%) had a CD4 count lower than 50 cells per muL. At follow-up (median 8.3 months, IQR 5.5-13.1), 967 (74%) were still on HAART, 243 (19%) had died, 91 (7%) were lost to follow-up, and seven (0.5%) discontinued treatment. Low body-mass index, WHO stage IV, male sex, and baseline CD4 count lower than 50 cells per muL were independent determinants of death in the first 6 months. At 12 months, the probability of individuals still in care was 0.76 (95% CI 0.73-0.78) and the median CD4 gain was 165 (IQR 67-259) cells per muL. In the cross-sectional survey (n=398), 334 (84%) had a viral load of less than 400 copies per mL. Of several indicators measuring adherence, self-reported poor adherence (<80%) in the past 4 days was the best predictor of detectable viral load (odds ratio 5.4, 95% CI 1.9-15.6). INTERPRETATION: These data show that large numbers of people can rapidly benefit from antiretroviral therapy in rural resource-poor settings and strongly supports the implementation of such large-scale simplified programmes in Africa.