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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