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Table 2 Characteristics of clusters of new HIV diagnosesa in Siaya County

From: Mapping geographic clusters of new HIV diagnoses to inform granular-level interventions for HIV epidemic control in western Kenya

Number of sub-locations in the cluster Names of sub-locations in the cluster Radius (kilometers) Observed cases Expected cases Relative risk Log likelihood ratio P value
Clusters with significant (p value < 0.05) higher new HIV diagnoses
 3 Malunga West, Sirembe, Malunga East 3.36 49 18.79 2.64 16.91 < 0.001
 2 Gangu, Ojwando ‘A’ 3.24 62 28.57 2.2 14.81 < 0.001
 7 Kochieng ‘A’, Kodiere, Ojwado ‘B’, Kochieng ‘B’, Koyeyo, Komeny, Kalaka, Ojwando ‘A’ 4.91 145 70.32 2.12 31.24 < 0.001
 5 Komolo, Hono, Kukumu_kombewa, Nyalgunga, Koyeyo 3.95 140 72.96 1.97 25.01 < 0.001
 4 Komenya Kowala, Kalkada Uradi, Komenya Kalaka, Simur Kondiek 3.15 72 38.93 1.87 11.4 0.002
 7 Ulafu, Umala, Nyalgunga, Nyamila, Olwa, Hono, Karapul 4.65 197 111.58 1.82 27.89 < 0.001
 4 Mur_ngiya, Olwa, Masumbi, Umala 3.43 91 57.76 1.59 8.32 0.026
 3 Bar Chando, Abom, North Ramba 3.69 97 62.91 1.56 8.12 0.032
 2 Kagwa, Kokwiri 3.92 81 47.93 1.71 9.62 0.008
Clusters with significant (p value < 0.05) lower new HIV diagnoses
 5 Gombe, Onyinyore, Ramula, Kambare, Uranga 3.69 68 115.55 0.58 11.9 < 0.001
 5 Omia Malo, Omia Diere, Memba, South Ramba, Omia Mwalo 4.14 81 150.33 0.53 20.11 < 0.001
 4 Lihanda, Uranga, Marenyo, Ramula 4.38 78 146.24 0.52 20.05 < 0.001
 6 Bar Sauri, Nyamninia, Anyiko_yala, Jina, Nyawara, Nyandiwa_yala 4.71 80 154.24 0.51 22.72 < 0.001
 5 Dienya East, Nguge, Dienya West, Ulamba, Wagai West 3.61 32 62.12 0.51 9.05 0.014
 7 Nyamninia, Bar Sauri, Jina, Nyandiwa_yala, Anyiko_yala, Nyawara, Marenyo 4.41 99 192.71 0.5 29.37 < 0.001
 5 Lihanda, Uranga, Marenyo, Ramula, Nyandiwa_yala 4.78 86 180.17 0.46 32.17 < 0.001
 4 Mahaya, Akom, Memba, Nyagoko 4.68 56 119.77 0.46 21.92 < 0.001
 5 Masala, Rachar, Akom, Kobong’, Nyagoko 4.85 63 164.62 0.37 42.97 < 0.001
 1 Ochieng’a 0 2 31.7 0.06 24.33 < 0.001
  1. aSub-location clusters of new HIV diagnoses were mapped using SaTScan, which gradually scans a window cyclically across space, noting the number of observed and expected observations inside the window at each location, adjusting for the underlying spatial inhomogeneity of the background population