Abstract
The dataset for this article contains Urinary and Intestinal Schistosomiasis from Lango region, northern Uganda which is the only known co-endemic region for S.mansoni and S.haematobium. Reported in the data, is the retrospective data review for historical information before interventions were implemented before 2003 and after interventions were implemented in 2003 by the national control program. In 2007 and 2011, parasitological surveys were conducted in the region to validate Schistosomiasis trends following World Health Organization (WHO) guidelines for surveys. In addition, malacological surveys were undertaken in 2007 to assess local transmission potential. The dataset can provide an insight into the health implications of Schistosomiasis control in co-endemic focus in Uganda, “The epidemiology of schistosomiasis in Lango region Uganda 60 years after Schwetz 1951: Can schistosomiasis be eliminated through mass drug administration without other supportive control measures?” (Adriko et al., 2018) [10].
Specifications table
| Subject area | Neglected Tropical Diseases |
| More specific subject area | Schistosoma mansoni and Schistosoma haematobium in co-endemic focus in Uganda |
| Type of data | Tables and figures |
| How data was acquired | Field surveys involving collection and examination of stool and urine samples from school age children and adults |
| Data format | Raw and analyzed |
| Experimental factors | The above parameters in the abstract were analyzed according to WHO guidelines |
| Experimental features | Stool and Urine samples were analyzed according to WHO guidelines[1] |
| Data source location | Kampala, Uganda Latitude & Longitude for collected data are presented in this data article |
| Data accessibility | All data are within this article. |
| Related research article | [10] Adriko, M., et al., The epidemiology of schistosomiasis in Lango region Uganda 60 years after Schwetz 1951: Can schistosomiasis be eliminated through mass drug administration without other supportive control measures? Acta Trop, 2018. 185: p. 412–418. |
Value of the data
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The dataset can be helpful to the concerned authorities and policy makers in designing interventions given the only region with co-endemic focus of the two disease species.
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The findings can be used by other researchers who wished to establish more insights into why the only region with co-endemic focuses for S.mansoni and S.haematobium in Uganda.
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The data can be used by the districts to validate health facility based detections.
1. Data
The data contains retrospective data review from studies [2], [3] and parasitological examination of urine samples for S.haematobium and stool samples for S.mansoni in 2007 and 2011 respectively. The datasets were collected from the Lango region of northern Uganda. Please See Table 1, Table 2, Table 3, Table 4, Table 5.
Table 1.
Showing S. mansoni prevalence in Lango region, 2007.
| Site | Sample size | % Prevalence (95% CI) |
|---|---|---|
| Abari Primary School | 18 | 0.00 (0.00–18.53) |
| Abarolam community | 29 | 3.45 (0.09–17.76) |
| Abilonino Primary School | 20 | 0.00 (0.00–16.84) |
| Aceno Primary School | 14 | 0.00 (0.00–23.16) |
| Agogoro Community | 23 | 17.39 (4.95–38.78) |
| Aleka Primary School | 16 | 56.25 (29.88–80.25) |
| Alenga Primary School | 13 | 0.00 (0.00–24.71) |
| Alerwang Primary School | 20 | 0.00 (0.00–16.84) |
| Aloi Community | 30 | 40.00 (22.66–59.40) |
| Amuda Community | 27 | 0.00 (0.00–12.77) |
| Aninolal Primary School | 14 | 0.00 (0.00–23.16) |
| Apire Primary School | 15 | 0.00 (0.00–21.80) |
| Atar Primary School | 18 | 0.00 (0.00–18.53) |
| Atar Community | 54 | 0.00 (0.00–6.60) |
| Atigolwok Primary School | 28 | 0.00 (0.00–12.34) |
| Awala Primary School | 29 | 10.34 (2.19–27.35) |
| Awila Primary School | 12 | 8.33 (0.21–38.48) |
| Baradilo Primary School | 19 | 0.00 (0.00–17.65) |
| Ebule Community | 27 | 3.70 (0.09–18.97) |
| Loro Primary School | 19 | 5.26 (0.13–26.03) |
| Odokogweno Community | 29 | 0.00 (0.00–11.94) |
| Okole Primary School | 21 | 4.76 (0.12–23.83) |
| Omer Primary School | 15 | 0.00 (0.00–21.80) |
| Ongica Primary School | 15 | 0.00 (0.00–21.80) |
| Teboke Primary School | 20 | 0.00 (0.00–16.84) |
| Wansolo Primary School | 55 | 69.09 (55.19–80.86) |
| Wigweng Primary School | 14 | 0.00 (0.00–23.16) |
| Total | 627 | 11.32 (8.95–14.07) |
Table 2.
Showing S.haematobium prevalence, by Hemastix result - 2011.
| Site | Sample Size | Trace = positive (% Prev.) (95% CI) | Trace = negative (% Prev.)(95% CI) |
|---|---|---|---|
| Abako Com | 61 | 0.00 (0.00–5.87) | 0.00 (0.00–5.87) |
| Aber P/S | 63 | 0.00 (0.00–5.69) | 0.00 (0.00–5.69) |
| Abilonono Com | 67 | 19.40 (10.76–30.89) | 5.97 (1.65–14.59) |
| Abilonyero Com | 57 | 3.51 (0.43–12.11) | 3.51 (0.43–12.11) |
| Abwal-A Com | 56 | 17.86 (8.91–30.40) | 8.93 (2.96–19.62) |
| Acandyang Com | 62 | 20.97 (11.66–33.18) | 11.29 (4.66–21.89) |
| Adyanglim P/S | 61 | 0.00 (0.00–5.87) | 0.00 (0.00–5.87) |
| Agweng P/S | 60 | 6.67 (1.85–16.20) | 6.67 (0.00–5.87) |
| Akia P/S | 62 | 8.06 (2.67–17.83) | 8.06 (2.67–17.83) |
| Aleka P/S | 69 | 1.45 (0.04–7.81) | 0.00 (0.00–5.21) |
| Alenga P/S | 63 | 12.70 (5.65–23.50) | 3.17 (0.39–11.00) |
| Anget P/S | 63 | 14.29 (6.75–25.39) | 0.00 (0.00–5.69) |
| Apoi P/S | 63 | 1.59 (0.04–8.53) | 1.59 (0.04–8.53) |
| Atar P/S | 64 | 7.81 (2.59–17.30) | 4.69 (0.98–13.09) |
| Atigolwok P/S | 65 | 27.69 (17.31–40.19) | 10.77 (4.44–20.94) |
| Atoma Com | 64 | 40.63 (28.51–53.63) | 14.06 (6.64–25.02) |
| Awali P/S | 64 | 4.69 (0.98–13.09) | 4.69 (0.98–13.09) |
| Ayer P/S | 62 | 24.19 (14.22–36.74) | 24.19 (14.22–36.74) |
| Baraliro Com | 62 | 3.23 (0.39–11.17) | 3.23 (14.22–36.74) |
| Barocok P/S | 59 | 3.39 (0.41–11.71) | 3.39 (0.41–11.71) |
| Ebule P/S | 66 | 4.55 (0.95–12.71) | 4.55 (0.95–12.71) |
| Fatima Aloi P/S | 66 | 3.03 (0.37–10.52) | 3.03 (0.37–10.52) |
| Malika P/S | 61 | 0.00 (0.00–5.87) | 0.00 (0.00–5.87) |
| Obangangeo P/S | 58 | 3.45 (0.42–11.91) | 3.45 (0.42–11.91) |
| Ogogoro P/S | 60 | 0.00 (0.00–5.96) | 0.00 (0.00–5.96) |
| Ojul P/S | 64 | 0.00 (0.00–5.60) | 0.00 (0.00–5.60) |
| Olarokwon Com | 61 | 0.00 (0.00–5.87) | 0.00 (0.00–5.60) |
| Teboke P/S | 65 | 32.31 (21.23–45.05) | 9.23 (3.46–19.02) |
| Wansolo P/S | 63 | 1.59 (0.04–8.53) | 1.59 (0.04–8.53) |
| Wigua P/S | 63 | 19.05 (10.25–30.91) | 0.00 (0.00–5.96) |
| Total | 1874 | 9.50 (8.21–10.92) | 3.74 (2.92–4.70) |
Table 3.
Direct comparison of S.haematobium prevalence in sites surveyed both in 2007 and 2011, by Hemastix.
|
2007 |
2011 |
||||
|---|---|---|---|---|---|
| Site | Sample size | Prevalence(95% CI) | Sample size | Prevalence(95% CI) | Trace = negative (% Prevalence) |
| Abilonono** | 20 | 0.00 (0.00–16.84) | 67 | 19.40 (10.76–30.89) | 5.97 (1.65–14.59) |
| Abilonono_2** | 20 | 0.00 (0.00–16.84) | |||
| Acandyang_A_com* | 116 | 0.00 (0.00–3.13) | 62 | 20.97 (11.66–33.18) | 11.29 (4.66–21.89) |
| Acandyang_A2_com* | 60 | 0.00 (0.00–5.96) | |||
| Aleka | 30 | 0.00 (0.00–11.57) | 69 | 1.45 (0.04–7.81) | 0.00 (0.00–5.21) |
| Alenga | 30 | 0.00 (0.00–11.57) | 63 | 12.70 (5.65–23.50) | 3.17 (0.39–11.00) |
| Atigolwok | 31 | 12.90 (3.63–29.83) | 65 | 27.69 (17.31–40.19) | 10.77 (4.44–20.94) |
| Atigolwok_com* | 120 | 1.67 (0.20–5.89) | |||
| Awali_com* | 90 | 0.00 (0.00–4.02) | 64 | 4.69 (0.98–13.09) | 4.69 (0.98–13.09) |
| Barodilo** | 20 | 90.00 (68.30–98.77) | 62 | 3.23 (0.39–11.17) | 3.23 (14.22–36.74) |
| Ebule_com* | 120 | 0.00 (0.00–3.03) | 66 | 4.55 (0.95–12.71) | 4.55 (0.95–12.71) |
| Ogogoro_com* | 118 | 0.00 (0.00–3.08) | 60 | 0.00 (0.00–5.96) | 0.00 (0.00–5.96) |
| Teboke | 20 | 0.00 (0.00–16.84) | 65 | 32.31 (21.23–45.05) | 9.23 (3.46–19.02) |
| Teboke_2 | 20 | 0.00 (0.00–16.84) | |||
| Wansola_com* | 120 | 0.00 (0.00–3.03) | 63 | 1.59 (0.04–8.53) | 1.59 (0.04–8.53) |
| TOTAL | 955 | 2.51 (1.61–3.72) | |||
Table 4.
Pre-MDA and Post-MDA Schistosomiasis Control in Lango region.
The data Presented here shows the trends of co-endemic occurrence of Schistosoma mansoni (S.m) and Schistosoma haematobium (S.h) Pre-intervention (Mass Drug Administration, MDA) and Post-MDA.
| Data period | Year | Data Source | Current District | Survey District | School/Community | Lat | Long | Methods | % S.haem | Methods | % S.man |
|---|---|---|---|---|---|---|---|---|---|---|---|
| Post-MDA | 1992 | [10] | Alebtong | Lira | Aloi school | 2.51778 | 33.29500 | Filtration | 0.0 | Kato Katz | 82.0 |
| Post-MDA | 1992 | [10] | Alebtong | Lira | Awali school | 2.42295 | 33.07030 | Filtration | 0.0 | Kato Katz | 67.0 |
| Post-MDA | 1992 | [10] | Alebtong | Lira | Namasale | 1.51066 | 32.61987 | Filtration | 0.0 | Kato Katz | 38.0 |
| Post-MDA | 1992 | [10] | Alebtong | Lira | Ogogoro school | 2.21972 | 33.26806 | Filtration | 0.0 | Kato Katz | 63.0 |
| Post-MDA | 1992 | [10] | Amolator | Lira | Aputi | 1.83052 | 32.87519 | Filtration | 0.0 | Kato Katz | 42.0 |
| Post-MDA | 2007 | [10] | Alebtong | Lira | Aloi school | 2.51778 | 33.29500 | Filtration | 0.0 | Kato Katz | 33.3 |
| Post-MDA | 2007 | [10] | Alebtong | Lira | Awali school | 2.46167 | 33.29972 | Filtration | 0.0 | Kato Katz | 10.3 |
| Post-MDA | 2007 | [10] | Alebtong | Lira | Ogogoro school | 2.21972 | 33.26806 | Filtration | 0.0 | Kato Katz | 14.8 |
| Post-MDA | 2007 | [10] | Apac | Apac | Akokoro school | 1.78000 | 32.56333 | Filtration | 0.0 | Kato Katz | 0.0 |
| Post-MDA | 2007 | [10] | Apac | Apac | Alenga school | 1.10361 | 32.40750 | Filtration | 0.0 | Kato Katz | 0.0 |
| Post-MDA | 2007 | [10] | Apac | Apac | Alerwang school | 2.16833 | 32.55639 | Filtration | 0.0 | Kato Katz | 0.0 |
| Post-MDA | 2007 | [10] | Apac | Apac | Aninolal school | 2.25806 | 32.63278 | Filtration | 0.0 | Kato Katz | 0.0 |
| Post-MDA | 2007 | [10] | Apac | Apac | Apire school | 2.01861 | 32.91500 | Filtration | 0.0 | Kato Katz | 0.0 |
| Post-MDA | 2007 | [10] | Apac | Apac | Atar community | 2.13528 | 32.66056 | Filtration | 0.9 | Kato Katz | 0.0 |
| Post-MDA | 2007 | [10] | Apac | Apac | Atar school | 2.04056 | 32.58972 | Filtration | 7.5 | Kato Katz | 0.0 |
| Post-MDA | 2007 | [10] | Apac | Apac | Atigolwo school | 2.34361 | 32.65333 | Filtration | 10.3 | Kato Katz | 0.0 |
| Post-MDA | 2007 | [10] | Apac | Apac | Atogolwo com | 2.34389 | 32.70361 | Filtration | 1.7 | Kato Katz | 0.0 |
| Post-MDA | 2007 | [10] | Apac | Apac | Awila school | 1.03583 | 32.48111 | Filtration | 0.0 | Kato Katz | 0.0 |
| Post-MDA | 2007 | [10] | Apac | Apac | Barodilo school | 2.21222 | 32.73222 | Filtration | 0.0 | Kato Katz | 0.0 |
| Post-MDA | 2007 | [10] | Apac | Apac | Chegere school | 2.23667 | 32.62917 | Filtration | 0.0 | Kato Katz | 0.0 |
| Post-MDA | 2007 | [10] | Apac | Apac | Ikwera school | 2.12944 | 32.94028 | Filtration | 0.0 | Kato Katz | 0.0 |
| Post-MDA | 2007 | [10] | Apac | Apac | Kwibale school | 1.69722 | 32.33389 | Filtration | 0.0 | Kato Katz | 0.0 |
| Post-MDA | 2007 | [10] | Apac | Apac | Okutoagwe school | 2.26333 | 32.63278 | Filtration | 0.0 | Kato Katz | 0.0 |
| Post-MDA | 2007 | [10] | Apac | Apac | Omer school | 2.04778 | 32.79028 | Filtration | 0.0 | Kato Katz | 0.0 |
| Post-MDA | 2007 | [10] | Apac | Apac | Ongica school | 2.34083 | 32.86000 | Filtration | 0.0 | Kato Katz | 0.0 |
| Post-MDA | 2007 | [10] | Apac | Apac | Teboke school | 2.45778 | 32.67389 | Filtration | 0.0 | Kato Katz | 0.0 |
| Post-MDA | 2007 | [10] | Apac | Apac | Wansolo com | 1.75528 | 32.72556 | Filtration | 0.0 | Kato Katz | 58.6 |
| Post-MDA | 2007 | [10] | Dokolo | Lira | Amuda school | 1.23861 | 33.02083 | Filtration | 0.0 | Kato Katz | 0.0 |
| Post-MDA | 2007 | [10] | Kole | Apac | Abari school | 2.30583 | 32.70583 | Filtration | 0.0 | Kato Katz | 0.0 |
| Post-MDA | 2007 | [10] | Kole | Apac | Abelonino school | 2.39500 | 32.85667 | Filtration | 0.0 | Kato Katz | 0.0 |
| Post-MDA | 2007 | [10] | Kole | Apac | Damatira school | 2.32417 | 32.80528 | Filtration | 0.0 | Kato Katz | 0.0 |
| Post-MDA | 2007 | [10] | Kole | Apac | Okole school | 2.43222 | 32.66056 | Filtration | 0.0 | Kato Katz | 4.8 |
| Post-MDA | 2007 | [10] | Lira | Lira | Abarolam school | 1.02028 | 33.16917 | Filtration | 0.0 | Kato Katz | 3.4 |
| Post-MDA | 2007 | [10] | Lira | Lira | Ebule school | 2.15333 | 33.55306 | Filtration | 0.0 | Kato Katz | 3.7 |
| Post-MDA | 2007 | [10] | Lira | Lira | Odekogweno | 1.03972 | 33.27778 | Filtration | 0.0 | Kato Katz | 0.0 |
| Post-MDA | 2007 | [10] | Oyam | Oyam | Acaba school | 2.60694 | 32.61444 | Filtration | 0.0 | Kato Katz | 0.0 |
| Post-MDA | 2007 | [10] | Oyam | Oyam | Aceno school | 2.46944 | 32.65583 | Filtration | 0.0 | Kato Katz | 0.0 |
| Post-MDA | 2007 | [10] | Oyam | Oyam | Ader school | 2.54944 | 32.91528 | Filtration | 0.0 | Kato Katz | 0.0 |
| Post-MDA | 2007 | [10] | Oyam | Oyam | Aleka school | 2.72806 | 32.85417 | Filtration | 0.0 | Kato Katz | 46.7 |
| Post-MDA | 2007 | [10] | Oyam | Oyam | Anget school | 2.75500 | 32.81278 | Filtration | 3.3 | Kato Katz | 23.1 |
| Post-MDA | 2007 | [10] | Oyam | Oyam | Loro school | 2.23861 | 32.53611 | Filtration | 0.0 | Kato Katz | 5.0 |
| Post-MDA | 2007 | [10] | Oyam | Oyam | Obot school | 2.46972 | 32.60389 | Filtration | 0.0 | Kato Katz | 0.0 |
| Post-MDA | 2007 | [10] | Oyam | Oyam | Onegwok | 2.64444 | 32.69667 | Filtration | 0.0 | Kato Katz | 0.0 |
| Post-MDA | 2007 | [10] | Oyam | Oyam | Wigweng school | 2.45667 | 32.70583 | Filtration | 0.0 | Kato Katz | 0.0 |
| Post-MDA | 2008 | [10] | Alebtong | Lira | Abako Com | 2.14602 | 33.22521 | Filtration | 0.0 | Kato Katz | 20.3 |
| Post-MDA | 2008 | [10] | Alebtong | Lira | Ogogoro P/S | 2.18874 | 33.20177 | Filtration | 0.0 | Kato Katz | 13.4 |
| Post-MDA | 2008 | [10] | Alebtong | Lira | Ojul P/S | 2.12264 | 33.20377 | Filtration | 0.0 | Kato Katz | 1.7 |
| Post-MDA | 2008 | [10] | Amolatar | Amolatar | Muntu P/S | 1.58197 | 32.89720 | Filtration | 0.0 | Kato Katz | 2.9 |
| Post-MDA | 2008 | [10] | Amolatar | Amolatar | Namasale P/S | 1.51066 | 32.61987 | Filtration | 0.0 | Kato Katz | 1.6 |
| Post-MDA | 2008 | [10] | Amolatar | Amolatar | Opir P/S | 1.55203 | 32.82683 | Filtration | 0.0 | Kato Katz | 2.5 |
| Post-MDA | 2008 | [10] | Oyam | Oyam | Atur Com | 2.13525 | 32.33604 | Filtration | 0.0 | Kato Katz | 9.6 |
| Post-MDA | 2008 | [10] | Oyam | Oyam | Nora P/S | 2.29298 | 32.26281 | Filtration | 0.0 | Kato Katz | 0.4 |
| Post-MDA | 2009 | [10] | Alebtong | Lira | Ogogoro p/s | 2.18874 | 33.20177 | Filtration | 0.0 | Kato Katz | 5.8 |
| Post-MDA | 2009 | [10] | Alebtong | Lira | Ojul p/s | 2.12264 | 33.20377 | Filtration | 0.0 | Kato Katz | 2.0 |
| Post-MDA | 2009 | [10] | Amolator | Amolator | Muntu p/s | 1.58197 | 32.89720 | Filtration | 0.0 | Kato Katz | 2.1 |
| Post-MDA | 2009 | [10] | Amolator | Amolator | Opir p/s | 1.55203 | 32.82683 | Filtration | 0.0 | Kato Katz | 3.8 |
| Post-MDA | 2011 | [10] | Alebtong | Alebtong | Abako Com | 2.14602 | 33.22521 | Filtration | 0.0 | Kato Katz | 18.0 |
| Post-MDA | 2011 | [10] | Alebtong | Alebtong | Adyanglim | 2.10130 | 33.21291 | Filtration | 0.0 | Kato Katz | 9.8 |
| Post-MDA | 2011 | [10] | Alebtong | Alebtong | Awali | 2.42295 | 33.07030 | Filtration | 0.0 | Kato Katz | 17.2 |
| Post-MDA | 2011 | [10] | Alebtong | Alebtong | Ebule | 2.15339 | 33.36017 | Filtration | 0.0 | Kato Katz | 3.0 |
| Post-MDA | 2011 | [10] | Alebtong | Alebtong | Fatima Aloi Demo | 2.26912 | 33.14071 | Filtration | 0.0 | Kato Katz | 29.7 |
| Post-MDA | 2011 | [10] | Alebtong | Alebtong | Obangangeo | 2.18572 | 33.36504 | Filtration | 0.0 | Kato Katz | 0.0 |
| Post-MDA | 2011 | [10] | Alebtong | Alebtong | Ogogoro | 2.18874 | 33.20177 | Filtration | 0.0 | Kato Katz | 11.7 |
| Post-MDA | 2011 | [10] | Alebtong | Alebtong | Ojul | 2.12264 | 33.20377 | Filtration | 0.0 | Kato Katz | 6.3 |
| Post-MDA | 2011 | [10] | Apac | Apac | Abwal A com | 2.08301 | 32.55774 | Filtration | 0.0 | Kato Katz | 0.0 |
| Post-MDA | 2011 | [10] | Apac | Apac | Acandyang com | 2.00903 | 32.60387 | Filtration | 11.3 | Kato Katz | 1.6 |
| Post-MDA | 2011 | [10] | Apac | Apac | Alenga | 1.84964 | 32.35359 | Filtration | 0.0 | Kato Katz | 1.6 |
| Post-MDA | 2011 | [10] | Apac | Apac | Apoi | 1.73001 | 32.46858 | Filtration | 0.0 | Kato Katz | 1.6 |
| Post-MDA | 2011 | [10] | Apac | Apac | Atar | 2.04032 | 32.59378 | Filtration | 1.6 | Kato Katz | 0.0 |
| Post-MDA | 2011 | Apac | Apac | Atigolwok | 2.08349 | 32.55926 | Filtration | 0.0 | Kato Katz | 0.0 | |
| Post-MDA | 2011 | [10] | Apac | Apac | Atoma Com | 1.81005 | 32.75776 | Filtration | 0.0 | Kato Katz | 23.0 |
| Post-MDA | 2011 | [10] | Apac | Apac | Teboke | 2.19976 | 32.58875 | Filtration | 0.0 | Kato Katz | 0.0 |
| Post-MDA | 2011 | [10] | Apac | Apac | Wansolo | 1.67725 | 32.50212 | Filtration | 0.0 | Kato Katz | 49.2 |
| Post-MDA | 2011 | [10] | Kole | Kole | Abilonono Com | 2.22691 | 32.64050 | Filtration | 0.0 | Kato Katz | 1.5 |
| Post-MDA | 2011 | [10] | Kole | Kole | Ayer | 2.29128 | 32.71657 | Filtration | 0.0 | Kato Katz | 1.6 |
| Post-MDA | 2011 | [10] | Kole | Kole | Wigua | 2.36085 | 32.67409 | Filtration | 0.0 | Kato Katz | 1.6 |
| Post-MDA | 2011 | [10] | Lira | Lira | Agweng | 2.49592 | 32.93468 | Filtration | 0.0 | Kato Katz | 42.9 |
| Post-MDA | 2011 | [10] | Lira | Lira | Akia | 2.25183 | 32.94784 | Filtration | 0.0 | Kato Katz | 3.9 |
| Post-MDA | 2011 | [10] | Otuke | Otuke | Abilonyero com | 2.40515 | 33.23411 | Filtration | 0.0 | Kato Katz | 0.0 |
| Post-MDA | 2011 | [10] | Otuke | Otuke | Baraliro com | 2.47074 | 33.17848 | Filtration | 0.0 | Kato Katz | 3.2 |
| Post-MDA | 2011 | [10] | Otuke | Otuke | Barocok | 2.50718 | 33.10303 | Filtration | 0.0 | Kato Katz | 5.1 |
| Post-MDA | 2011 | [10] | Otuke | Otuke | Malika | 2.43519 | 33.25354 | Filtration | 0.0 | Kato Katz | 11.5 |
| Post-MDA | 2011 | [10] | Otuke | Otuke | Olarokwon com | 2.51360 | 33.26091 | Filtration | 0.0 | Kato Katz | 0.0 |
| Post-MDA | 2011 | [10] | Oyam | Oyam | Aber | 2.20114 | 32.34769 | Filtration | 0.0 | Kato Katz | 1.6 |
| Post-MDA | 2011 | [10] | Oyam | Oyam | Aleka | 2.56069 | 32.75598 | Filtration | 0.0 | Kato Katz | 43.3 |
| Post-MDA | 2011 | [10] | Oyam | Oyam | Anget | 2.57830 | 32.78435 | Filtration | 0.0 | Kato Katz | 32.3 |
| Pre-MDA | 1951 | [2] | Oyam | Aloro | Direct Micro | 28.6 | Direct Micro | 0.0 | |||
| Pre-MDA | 1951 | [2] | Apac | Ayer | Direct Micro | 39.3 | Direct Micro | 0.0 | |||
| Pre-MDA | 1951 | [2] | Apac | Aboki | Direct Micro | 33.3 | Direct Micro | 0.0 | |||
| Pre-MDA | 1951 | [2] | Apac | Nyunbuke Catholic | Direct Micro | 0.0 | Direct Micro | 0.0 | |||
| Pre-MDA | 1951 | [2] | Apac | Nyunbuke Protestant | Direct Micro | 0.0 | Direct Micro | 0.0 | |||
| Pre-MDA | 1951 | [2] | Apac | Aber Protestant | Direct Micro | 20.0 | Direct Micro | 0.0 | |||
| Pre-MDA | 1951 | [2] | Apac | Adyegi | Direct Micro | 0.0 | Direct Micro | 0.0 | |||
| Pre-MDA | 1951 | [2] | Apac | Ibuje-Alenga | Direct Micro | 27.3 | Direct Micro | 0.0 | |||
| Pre-MDA | 1951 | [2] | Apac | Akokoro | Direct Micro | 0.0 | Direct Micro | 0.0 | |||
| Pre-MDA | 1951 | [2] | Apac | Nyalu Village | Direct Micro | 0.0 | Direct Micro | 0.0 | |||
| Pre-MDA | 1967 | [2] | Kole | Abilonino | Filtration | 51.6 | Formal- ether | 0.0 | |||
| Pre-MDA | 1967 | [3] | Apac | Abiya | Filtration | 0.0 | Formal- ether | 3.6 | |||
| Pre-MDA | 1967 | [3] | Apac | Aduku | Filtration | 0.0 | Formal- ether | 0.0 | |||
| Pre-MDA | 1967 | [3] | Lira | Akia (Lira) | Filtration | 0.0 | Formal- ether | 10.3 | |||
| Pre-MDA | 1967 | [3] | Alebtong | Aloi | Filtration | 0.0 | Formal- ether | 0.0 | |||
| Pre-MDA | 1967 | [3] | Lira | Atura | Filtration | 0.0 | Formal- ether | 0.0 | |||
| Pre-MDA | 1967 | [3] | Amolator | Muntu | Filtration | 0.0 | Formal- ether | 0.0 | |||
| Pre-MDA | 1967 | [3] | Otuke | Paranga | Filtration | 0.0 | Formal- ether | 53.3 | |||
| Pre-MDA | 1967 | [3] | Oyam | Teboke | Filtration | 0.0 | Formal- ether | 0.0 |
Table 5.
Snail data model results.
| Snail species | Dependent variable | Factor (baseline; category) | Odds ratio (95% CI) | p- value |
|---|---|---|---|---|
| Bi. sudanica | Presence/Absence | altitude (meters); continuous (+ 1) | 0.95 (0.89–1.01) | 0.073 |
| Temperature (C); Continuous (+ 0.1) | 0.61 (0.41–0.91) | 0.017 | ||
| Bi. pfeifferi | Abundance | pH; continuous (+ 0.1) | 564.45* (5.50–5.794) | 0.010 |
| Bu. forskalii | Presence/Absence | altitude (meters); continuous (+ 1) | 0.96 (0.93–0.99) | 0.019 |
| Abundance | altitude (meters); continuous (+ 1) | 0.93 (0.87–0.99) | 0.021 | |
| Bu. tropicus | Presence/Absence | altitude (meters); continuous (+ 1) | 1.02 (1.00–1.04) | 0.074 |
Note pH is on a logarithmic scale, and so an odds ratio of 10 corresponds to an increase of 1 on the pH scale, an increase of 100 corresponds to 2 pH points, etc. No factors were significant in predicting the presence/absence or abundance of snails infected with non-human cercariae.
2. Experimental designs, methods and materials
This study related to the data was carried out in the former Lango district previously described by [2]. About 20 ml of urine were collected and tested for the presence of microhaematuria using reagent strips (Hemastix©, Bayer, Germany) and recorded following grading [4]. For confirmation of the infection, a syringe filtration method [5] and examined for schistosome eggs [1] while stool samples for S.mansoni infections were processed using Kato-Katz double thick smears [6] using a 41.7 mg template and duplicate smears examined under a microscope according to WHO guidelines [1]. Snail surveys were conducted in 2007 in the vicinity of each school surveyed for Bulinus and Biomphalaria snail species following guidelines [7] and identified using field keys [8] and [9]. The following datasets are presented.
2.1. 2007 data
The Table 1 below shows the data on the generalized linear model (GLM) looking at factors influencing binomial prevalence of S. haematobium infection (as diagnosed by Hemastix), with inclusion of age, sex and knowledge of bilharzia as explanatory variables.
2.2. 2011 data
The 2011 data presented in Table 2 shows S. mansoni infection, the relationships with sex and age amongst those surveyed.
2.3. Direct comparison of S.haematobium prevalence in sites surveyed both in 2007 and 2011, by Hemastix
In several cases, multiple surveys had been conducted in the same region in 2007 whereas only a single survey was carried out in 2011. In some cases, the survey in 2007 took place in the community whereas the follow-up in 2011 took place in the local primary school; these cases are marked with “*”. The inverse cases, where the initial survey took place in a primary school and the follow-up in the community, are marked with “**”. Urine syringe filtration was only carried out in 2011 (Table 3).
2.4. Snail data model
All models were multivariate, including altitude, temperature, pH, conductivity and dissolved oxygen as covariates. Presence/absence models were estimated using a generalized linear model (glm) whereas abundance mo dels were estimated using a linear model (lm) with only factors that had a p-value less than 0.1 included(at the 95% confidence level) (Table 5).
Acknowledgement
This research would not have been possible without funding from the EU grant CONTRAST (FP6 STREP contract no: 032203, http://www.eu-contrast.eu). The 2011 survey was supported with funding from DFID through SCI (the ICOSA project) for which we are most grateful. Approval for the surveys was gratefully received from NHS-LREC of the imperial College, London and Uganda National Council of Science & Technology.
Footnotes
Transparency data associated with this article can be found in the online version at 10.1016/j.dib.2018.08.200.
Transparency document. Supplementary material
Supplementary material.
.
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