Literature DB >> 28910562

The Epidemiology of Trachoma in Mozambique: Results of 96 Population-Based Prevalence Surveys.

Mariamo Abdala1, Carlos C Singano2, Rebecca Willis3, Colin K Macleod4, Sharone Backers5, Rebecca M Flueckiger3, Anselmo Vilanculos6, Dantew Terefe7, Moises Houane8, Fitsum Bikele8, Amir Bedri Kello9, Philip Downs10, Zulquifla Bay5, Laura Senyonjo11, Anthony W Solomon12.   

Abstract

PURPOSE: Surveys are needed to guide trachoma control efforts in Mozambique, with WHO guidelines for intervention based on the prevalence of trachomatous inflammation-follicular (TF) in children aged 1-9 years and the prevalence of trichiasis in adults aged 15 years and above. We conducted surveys to complete the map of trachoma prevalence in Mozambique.
METHODS: Between July 2012 and May 2015, we carried out cross-sectional surveys in 96 evaluation units (EUs) covering 137 districts.
RESULTS: A total of 269,217 individuals were enumerated and 249,318 people were examined using the WHO simplified trachoma grading system. Overall, 102,641 children aged 1-9 years, and 122,689 individuals aged 15 years and above were examined. The prevalence of TF in children aged 1-9 years was ≥10% in 12 EUs, composed of 20 districts, covering an estimated total population of 2,455,852. These districts require mass distribution of azithromycin for at least 3 years before re-survey. The TF prevalence in children was 5.0-9.9% in 17 EUs (28 districts, total population 3,753,039). 22 EUs (34 districts) had trichiasis prevalences ≥0.2% in adults 15 years and above, and will require public health action to provide surgical services addressing the backlog of trichiasis. Younger age, more children resident in the household, and living in a household that had an unimproved latrine or no latrine facility, were independently associated with an increased odds of TF in children aged 1-9 years.
CONCLUSIONS: Trachoma represents a significant public health problem in many areas of Mozambique.

Entities:  

Keywords:  Mozambique; Trachoma; prevalence; survey

Mesh:

Year:  2017        PMID: 28910562      PMCID: PMC6444277          DOI: 10.1080/09286586.2017.1351996

Source DB:  PubMed          Journal:  Ophthalmic Epidemiol        ISSN: 0928-6586            Impact factor:   1.648


Introduction

Trachoma is an infectious eye disease caused by repeated conjunctival infection with the bacterium Chlamydia trachomatis. Recurrent infections lead to chronic inflammation and scarring of the eyelid, altering the eyelid morphology and, in some people, causing an in-turning of the eyelashes. When eyelashes rub on the globe of the eye, trachomatous trichiasis is said to be present. This process of abrasion can ultimately lead to corneal opacification with visual impairment or blindness.[1,2] Trachoma is the leading infectious cause of blindness worldwide, with the global focus in Sub-Saharan Africa, particularly its poorest and most isolated rural communities. In 2013, it was estimated that the African continent was host to 4 million cases of trichiasis (47% of all cases globally), with 33 of 56 African countries thought to be endemic.[3] The disease is associated with low levels of sanitation and inadequate water access.[4-6] In general, transmission occurs by close contact within the home, both directly (via contaminated hands) and indirectly (on cloths, or on the bodies of eye-seeking flies).[1,7] The World Health Organization (WHO) has targeted trachoma for elimination as a public health problem by the year 2020, using the SAFE strategy of Surgery for trachomatous trichiasis; and Antibiotic distribution, Facial cleanliness and Environmental improvement in areas where the prevalence of the disease “trachomatous inflammation—follicular” (TF) in 1–9-year-olds is greater than 5%. Mozambique, with a population of over 25 million people, was thought to have trachoma-endemic districts in many provinces, but there were few data to guide elimination efforts. Therefore the Mozambique Ministry of Health, in collaboration with partners, undertook population-based surveys to determine the prevalence of trachoma across all 11 provinces.

Methods

The suspected-trachoma-endemic area of Mozambique was mapped after dividing it into 96 evaluation units (EUs). To create these divisions, administrative districts with relatively low populations were combined with neighbouring districts, such that each EU had a population of approximately 100,000–250,000 people.[8] The surveys were conducted in two phases – a first phase of five trachoma prevalence surveys in Erati, Memba, Nacala-a-Velha, Ribaue, and Malema of Nampula Province, which took place prior to the launch of the Global Trachoma Mapping Project (GTMP), and a second phase of 91 surveys conducted with the support of the GTMP.[9] In each EU, a two-stage cluster random sampling methodology was used. The primary sampling unit was the village or enumeration area, selected with probability proportional to size, where the measure of size was the total number of households from the 2007 population census (data provided by the Mozambique National Statistics Institute).[10] In the second stage, households were selected, on the day of the survey, either quasi-randomly using the random walk (first phase) or randomly using compact segment sampling (second phase). A list was kept of households from which some or all inhabitants were absent at the time of survey, with teams encouraged to return to those locations later in the day. In keeping with the known epidemiology of trachoma, urban areas (defined nationally as communities with more than 5000 inhabitants) were excluded from the sampling frame. At sampled households, all residents aged 1 year and above were eligible for inclusion. In all 96 EUs, participants were examined for clinical signs of trachoma using the WHO simplified trachoma grading system, including TF and trichiasis.[1,11] In the five first-phase EUs (Nampula Province), and in nine of the 91 second-phase EUs (those in Niassa Province, which used version 3 of the GTMP training system), data on the presence or absence of trachomatous scarring (TS)[11] were collected either for all subjects (Nampula) or in eyes with trichiasis (Niassa). (The remaining 82 EUs were mapped following version 1 of the GTMP training system, which did not include collection of data on TS.) Field teams were trained on data collection and (in the second phase) were required to pass an examination using the standard GTMP procedures, following a 5-day training program led by a GTMP-certified master grader and a GTMP-certified grader trainer; prospective trachoma graders had to achieve kappa scores of at least 0.7 in an inter-grader agreement exercise with the grader trainer, in order to work on a survey.[9] Data recorders were trained to conduct household interviews and to capture data using Android smartphones enabled with standard survey forms outlined in the GTMP protocol. In both phases, survey teams were taught to establish contact with and obtain permission from traditional leaders prior to data collection. Water, Sanitation and Hygiene (WASH) factors associated elsewhere with trachoma were collected at household-level by focused interview of household heads, and by direct observation by trained data recorders. Variables collected were categorized according to WHO/UNICEF Joint Monitoring Programme standards, in which an “improved” sanitation facility is one that hygienically separates excreta from human contact, and an “improved” water source is one that is constructed to protect the source from outside contamination.[9,12,13] Due to slight wording differences in the WASH-related questions used in the two phases of work, only the 91 second-phase surveys were included in the risk factor analysis.

Sample size

Using 2007 census data, it was estimated that there would be 1.59 children aged 1–9 years per household. Therefore, if teams could visit 32 households per day, 24 clusters would be required per EU to achieve the sample size of 1019 children needed to estimate a TF prevalence of 10% with a precision of ±3% at the 95% confidence level, including a design effect of 2.65 to account for the clustered design.[9]

Data collection tools

In the five first-phase EUs, data collection was carried out using a paper-based questionnaire. In the 91 second-phase EUs, data collection was carried out using Android smartphones, with bespoke GTMP forms developed in open data kit (ODK) and available through LINKS (https://gtmp.linkssystem.org/).[9]

Statistical analysis

For the paper-based census questionnaires, all data were double-entered into a database using the Census and Survey Processing system (CSPro v5 United States Census Bureau, 2012). Adjustment was carried out using R (version 3.0.2, The R Foundation for Statistical Computing, 2013). The overall EU-level adjusted prevalence was calculated by adjusting the proportion of children with TF in each cluster, in 1-year age groups, using the 2007 Mozambique census data projected to 2010, and calculating the mean of all such clusters. The same method was used to calculate the overall adjusted trichiasis prevalence, but with adjustment for both sex and age in 5-year age groups. When data on TS were available, we report the prevalence of TT: this was possible for 14 EUs. (As noted above, in 82 of the 96 EUs, data on the presence or absence of TS in eyes with trichiasis were not collected.) Confidence intervals around prevalence estimates were calculated by bootstrapping the adjusted cluster proportions over 10,000 iterations and taking the 2.5th and 97.5th centiles. For the 91 second-phase EUs, analyses of associations of TF were carried out at individual level, accounting for clustering at EU, cluster, and household levels using mixed-effects logistic regression. Variables were considered for inclusion in the multivariable model if significant on univariable analysis at the p < 0.05 level. A step-wise inclusion approach was used to build the multivariable model, with variables retained if significant at the p < 0.05 level (Likelihood ratio test).

Ethical considerations

Approval for conducting the study was obtained from the National Committee on Bio-Ethics, the Provincial Directorates of Health in each province of Mozambique, and (for the second-phase, GTMP-supported EUs) the ethics committee of the London School of Hygiene & Tropical Medicine (references 6319 and 8355). In the paper-based, first-phase surveys, consent forms were signed or finger stamped by all participants before the start of the data collection process. For the second-phase, GTMP-supported surveys, consent was recorded electronically for all participants. Individuals with active trachoma and their family members received 1% tetracycline eye ointment, in accordance with Mozambique Ministry of Health guidelines. Each person who took part in the research was thanked and received a bar of soap plus information about the importance of daily face washing for the reduction of trachoma transmission. All patients found to have significant ocular pathology were referred to the nearest health centre.

Results

A total of 2378 clusters were visited within the 96 EUs, with 75,993 households enrolled between July 2012 and May 2015. In total, 249,318 people aged 1 year or older were examined from the 269,217 residents living in those households, representing a coverage of 92.6%. A total of 138,210 (55.4%) women were examined. A total of 102,641 children aged 1–9 years and 122,689 individuals aged 15 years or greater were examined. A greater proportion (60.8%) of examined subjects aged 15 years or greater were female. Among the 102,641 children aged 1–9 years examined, 50,911 (49.6%) were male and 51,730 (50.4%) were female. In the 91 second-phase EUs included in the risk factor analyses, a total of 2240 clusters were surveyed, covering 70,757 households. A household head was present and consented to the survey in 70,663 (99.8% of) households, with 248,494 individuals subsequently sampled for inclusion, and 228,595 (92.0%) consenting to examination. 19,325 (7.8%) individuals were resident in selected households but not present at the time of survey, and 516 (0.2%) did not consent to examination. In the five first-phase surveys, a total of 20,723 individuals provided consent to be examined and were examined over 138 clusters covering 5233 households. Table 1 shows, for each EU, the adjusted TF prevalence in children aged 1–9 years, and the adjusted trichiasis prevalence (or TT prevalence, for those EUs in which TS data could be applied to eyes with trichiasis) for adults aged 15 years and above. There were 12 EUs (20 districts, 2,455,852 total population) with TF prevalences in 1–9-year-olds ≥10%, and 17 EUs (28 districts, 3,753,039 total population) with TF prevalences of 5.0–9.9%. In 22 EUs, the trichiasis prevalence in adults aged 15 years and above was ≥0.2%, the WHO trichiasis elimination threshold.[14]
Table 1.

Prevalence of trachomatous inflammation—follicular (TF) in children aged 1–9 years and trichiasis in adults aged 15 years and above, by evaluation unit, Mozambique, 2012–2015. The name given to each evaluation unit is a concatenation of the names of its constituent districts.

  1–9 years
≥15 years
ProvinceEvaluation unitExamined (n)Adjusted TFa (%, 95%CIb)Examined (n)Adjusted trichiasisc prevalence (%, 95%CIb)
Cabo DelgadoIbo-Meluco-Macomia7276.6 (3.7–10.5)11830.8 (0.2–1.2)
Cabo DelgadoMuidumbe-Mocimboa Praia7698.8 (5.3–13.4)12540.7 (0.3–1.2)
Cabo DelgadoPemba Metuge-Mecufi-Quissanga7538.4 (5–12.4)11640.4 (0.1–0.7)
Cabo DelgadoNangade-Palma90815.7 (10.6–19.8)14801.2 (0.7–1.7)
Cabo DelgadoAncuabe84511.6 (7.5–16.8)12000.4 (0.1–0.6)
Cabo DelgadoBalama10195.3 (3.7–7.3)12750.8 (0.4–1.1)
Cabo DelgadoChiure79813.9 (8.7–19.4)11810.2 (0–0.6)
Cabo DelgadoMontepuez8207.8 (5–11.5)12330.6 (0.4–1)
Cabo DelgadoMueda74412.2 (8.6–16.8)13130.7 (0.3–1.2)
Cabo DelgadoNamuno8354.8 (2.9–6.6)11060.6 (0.2–1.1)
GazaMacia10430.3 (0–0.6)11120 (0–0.3)
GazaChibuto8660.1 (0–0.2)10880.1 (0–0.3)
GazaGuija-Chigubo-Mabalane8680.9 (0–2.4)10550 (0–0.4)
GazaChokwe8620.2 (0–0.6)10860.2 (0–0.4)
GazaManjacaze9350 (0–0)12140.3 (0–0.6)
GazaMassingir-Massangena-Chicualacuala10421.2 (0.3–2.5)10260 (0–0.4)
GazaXai-Xai9250.1 (0–0.3)11140 (0–0.3)
InhambaneInhassoro-Govuro-Mabote10951.4 (0.7–2.5)13420.1 (0–0.4)
InhambaneFunhalouro-Panda8790.4 (0–1)11330 (0–0.3)
InhambaneHomoine8570.1 (0–0.2)11950.2 (0.1–0.4)
InhambaneInharrime9250 (0–0)12360.1 (0.1–0.2)
InhambaneJangamo9360 (0–0)12130.1 (0–0.3)
InhambaneMassinga8190.4 (0–1)11830.3 (0–0.5)
InhambaneMorrumbene8940.3 (0–0.6)12220.1 (0–0.2)
InhambaneVilankulo9040.9 (0.3–1.4)12330.2 (0–0.4)
InhambaneZavala9630.7 (0.1–1.3)13180.2 (0.1–0.5)
ManicaMacossa-Guro-Tambara169310.7 (7.2–14.3)12800.3 (0.1–0.5)
ManicaBarue13441.6 (0.5–2.8)13210 (0–0.3)
ManicaMacate-Gondola13940.3 (0–0.7)12400.1 (0–0.2)
ManicaMachaze12732.6 (1.2–3.9)10850.1 (0–0.3)
ManicaVanduzi-Manica14320.3 (0.1–0.6)12190 (0–0.3)
ManicaMossurize12180.1 (0–0.4)11100 (0–0.3)
ManicaSussundenga12930.4 (0.1–0.8)11810.1 (0–0.3)
MaputoBoane7600.6 (0.1–1.3)12450.1 (0–0.3)
MaputoManhica12941.1 (0.2–2.4)14520 (0–0.3)
MaputoMarracuene12110.6 (0.2–1)15820 (0–0.2)
MaputoMoamba-Magude7681 (0.2–2)11610 (0–0.3)
MaputoMatutuine-Namaacha11250.5 (0.1–1.1)14470 (0–0.3)
NampulaIlha Mozambique-Mossuril119214.5 (9.1–20)12620.1 (0–0.1)
NampulaMecuburi-Lalaua11373.7 (2.2–5.2)12120.1 (0–0.3)
NampulaNacaroa-Muecate10496.2 (3.6–9.2)12110.3 (0–0.7)
NampulaAngoche119310.3 (6.1–14.9)12910 (0–0.3)
NampulaMeconta10003.7 (1.7–6.4)12390.1 (0–0.1)
NampulaMogincual-Liupo115917.1 (11.6–22)12490.1 (0–0.1)
NampulaMogovolas10843.6 (1.8–5.3)12730.1 (0–0.2)
NampulaMoma-Larde12098.3 (6.3–10.4)12880.8 (0.2–1.9)
NampulaMonapo11147.6 (4.5–10.7)12650 (0–3)
NampulaMurrupula10352.7 (1.3–4.4)12220 (0–0.3)
NampulaNampula9842.9 (1.3–4.8)11980 (0–0.3)
NampulaRibauee19641.3 (0.7–1.7)23350 (0–0.2)d
NampulaMalemae16801.1 (0.5–1.8)22270 (0–0.2)d
NampulaMembae184611.9 (9–16)23000 (0–0.2)d
NampulaEratie15818.4 (6.3–11.4)22850 (0–0.2)d
NampulaNacala-A-Velhae18589.4 (7.3–11.5)26470 (0–0.1)d
NiassaCuamba10942.1 (0.9–3)11190 (0–0.3)d
NiassaLichinga10161.2 (0.4–2)9780 (0–0.4)d
NiassaMandimba13951.4 (0.6–2.5)11420 (0–0.3)d
NiassaMecanelhas13081.9 (1–2.6)11940.1 (0–0.4)d
NiassaSanga-Lago11200.5 (0.2–0.9)11060 (0–0.3)d
NiassaMagune-Marupa9852.4 (1.1–4.1)10470.1 (0–0.2)d
NiassaN’gauma11393.3 (1.7–5.3)10860 (0–0.4)d
NiassaMaúa-Nipepe-Metarica12451.4 (0.7–2.4)12060 (0–0.3)d
NiassaMuembe -Mecula-Mavago11720.7 (0.1–1.5)11130 (0–0.3)d
SofalaBuzi10630 (0–0)10860 (0–0.4)
SofalaCaia14492.1 (1.3–3.3)12240.2 (0–0.5)
SofalaMachanga-Chibabava9790.8 (0.2–1.5)11110 (0–0.3)
SofalaDondo11280.4 (0.1–0.7)9980 (0–0.4)
SofalaGorongoza12921.2 (0.4–2.4)11730 (0–0.3)
SofalaCheringoma-Muanza-Marromeu15104.1 (2.8–5.7)13060.1 (0–0.3)
SofalaChemba-Maringue13916.9 (4.8–9.5)11390.2 (0–0.4)
SofalaNhamatanda11971 (0.3–1.8)10810.4 (0–1.1)
TeteAngonia8343 (1.2–5.6)12720.2 (0–0.5)
TeteChiuta-Cahora Bassa8925.7 (3.7–8.3)12100.1 (0–0.1)
TeteChangara9039.9 (6.9–12.9)11630.2 (0–0.5)
TeteChifunde10424.1 (2.5–6)12280.1 (0–0.3)
TeteMacanga10633.6 (2.1–5.2)12690 (0–0.3)
TeteMagoe-Zumbu8455.2 (2.9–8.3)12530.2 (0–0.4)
TeteMaravia8791.9 (0.8–3.1)12240 (0–0.3)
TeteMoatize10064.7 (2.8–6.8)12000.2 (0–0.5)
TeteDoa-Mutarara10739.7 (7.2–13)12480.1 (0–0.2)
TeteTsangano8361.7 (0.8–2.9)11930 (0–3)
ZambeziaChinde-Luabo-Inhassunge161319.8 (15.6–24.1)22000.2 (0–0.5)
ZambeziaGile9352.3 (0.9–3.4)12170 (0–3)
ZambeziaGurue7973.3 (1.9–4.7)10980 (0–0.4)
ZambeziaIle-Mulevala8291.2 (0.3–2.3)11500.3 (0–0.7)
ZambeziaLugela7942 (0.6–3.3)12310.1 (0–0.3)
ZambeziaMocubela-Maganja da Costa85610.2 (6.5–13.3)11880.2 (0–0.4)
ZambeziaMilange8723.7 (1.9–5.9)11690 (0–0.3)
ZambeziaMocuba8961.6 (0.8–2.7)12140 (0–0.3)
ZambeziaMolocue8211.8 (0.9–2.5)11700 (0–0.3)
ZambeziaMopeia8536.9 (4.4–10.3)11960 (0–0.3)
ZambeziaMorrumbala8657 (3.9–11.1)11680.1 (0–0.3)
ZambeziaNamacurra7703.4 (1.6–5.7)12630.2 (0–0.4)
ZambeziaNamaroi7742.7 (1–4.4)11470 (0–0.3)
ZambeziaNicoadala14383.9 (2.2–6)17130.1 (0–0.1)
ZambeziaPebane75114.1 (9.9–19.7)12100.3 (0–0.9)

aPopulation-based trachomatous inflammation–follicular (TF) prevalence derived from cluster-level proportions adjusted for age in 1 year agebands using the latest available census data

b95% Confidence intervals estimated by bootstrapping adjusted cluster-level proportions over 10,000 replicates. Zero-count estimate upper confidence interval limits estimated as a one-sided 97.5% exact binomial confidence interval.

cPopulation-based trichiasis prevalence derived from cluster-level proportions adjusted for sex and age in 5 year agebands using the latest available census data

dData on trachomatous scarring (TS) recorded for all subjects, or for eyes with trichiasis (see text), in these surveys, so the trichiasis prevalence estimates here may be accurately referred to as trachomatous trichiasis (TT) prevalence estimates.

eFirst-phase, paper-based surveys.

Prevalence of trachomatous inflammation—follicular (TF) in children aged 1–9 years and trichiasis in adults aged 15 years and above, by evaluation unit, Mozambique, 2012–2015. The name given to each evaluation unit is a concatenation of the names of its constituent districts. aPopulation-based trachomatous inflammation–follicular (TF) prevalence derived from cluster-level proportions adjusted for age in 1 year agebands using the latest available census data b95% Confidence intervals estimated by bootstrapping adjusted cluster-level proportions over 10,000 replicates. Zero-count estimate upper confidence interval limits estimated as a one-sided 97.5% exact binomial confidence interval. cPopulation-based trichiasis prevalence derived from cluster-level proportions adjusted for sex and age in 5 year agebands using the latest available census data dData on trachomatous scarring (TS) recorded for all subjects, or for eyes with trichiasis (see text), in these surveys, so the trichiasis prevalence estimates here may be accurately referred to as trachomatous trichiasis (TT) prevalence estimates. eFirst-phase, paper-based surveys. In the 82 surveys in which trachomatous scarring (TS) did not form part of the examination, a total of 100,904 individuals aged 15 years or greater consented to examination and were examined. Altogether, 38,522 (38.2%) males aged 15 years or greater were examined, and 87 were found to have trichiasis (0.22%). Among the 62,382 (61.8%) females aged 15 years or greater examined in these surveys, 276 had trichiasis (0.44%). In the 14 surveys in which TS was included in the examination, a total of 21,753 individuals aged 15 years or greater were examined. Of these, 9517 (43.8%) of those examined aged 15 years or greater were male, with one case of TT identified (0.01%). A total of 12,236 (56.2%) of those examined aged 15 years or greater were female, with five cases of TT identified (0.04%). EU-level prevalences of TF in children aged 1–9 years and trichiasis (or TT) in adults aged 15 years and above are shown in Figures 1 and 2, respectively. In 74 EUs, the trichiasis (or TT) prevalence was less than the WHO elimination threshold of <0.2% of the population aged 15 years and above, and in 67 EUs, the TF prevalence in children aged 1–9 years was less than the 5% threshold for elimination. Overall, 59 EUs had both a TF prevalence <5% in children aged 1–9 years and a trichiasis prevalence of <0.2% of those aged 15 years and above. Among the 22 EUs with trichiasis prevalences in adults ≥0.2%, seven had TF prevalence >10% and seven had TF prevalence between 5.0 and 9.9%.
Figure 1.

Prevalence of trachomatous inflammation–follicular (TF) in those aged 1–9 years in 96 population-based prevalence surveys, Mozambique, 2012–2015.

Figure 2.

Prevalence of trichiasis in those aged 15 years and above in 96 population-based prevalence surveys, Mozambique, 2012–2015. In 14 evaluation units (hatched), prevalence categories shown are for trachomatous trichiasis (TT), defined as trichiasis and (in the same eye) either (a) trachomatous conjunctival scarring (TS); or (b) the examiner’s inability to evert the eyelid to look for TS (with the difficulty in eversion presumed to be due to TS). In the other 82 evaluation units, prevalence categories shown are for all-trichiasis, regardless of the presence or absence of TS.

Prevalence of trachomatous inflammation–follicular (TF) in those aged 1–9 years in 96 population-based prevalence surveys, Mozambique, 2012–2015. Prevalence of trichiasis in those aged 15 years and above in 96 population-based prevalence surveys, Mozambique, 2012–2015. In 14 evaluation units (hatched), prevalence categories shown are for trachomatous trichiasis (TT), defined as trichiasis and (in the same eye) either (a) trachomatous conjunctival scarring (TS); or (b) the examiner’s inability to evert the eyelid to look for TS (with the difficulty in eversion presumed to be due to TS). In the other 82 evaluation units, prevalence categories shown are for all-trichiasis, regardless of the presence or absence of TS.

Risk factor analysis

TF risk factors

Full univariable results for the outcome of TF in children aged 1–9 years are shown in Table 2. Among the 91 surveys conducted in the second phase, TF prevalence was 4% among boys and 4% among girls. A multivariable analysis found that younger age, living with more children aged 1–9 years in the household, and living in a household with unimproved or no latrine facilities (including use of the bush or field) was independently associated with TF in the same age group (Table 3).
Table 2.

Univariable mixed-effects logistic regression analysis of the outcome trachomatous inflammation–follicular (TF) in children aged 1–9 years against putative risk factors, using data from 91 population-based prevalence surveys, Global Trachoma Mapping Project, Mozambique, 2012–2015.

VariableExamined (n)TF cases (%)Odds ratio (95% CI)ap-valueb
Age    
 1–442,9192377 (5.5)2.16 (2.01–2.33)<0.0001
 5–950,7931386 (2.7)1 
Sex    
 Male46,5321859 (4.0)1 
 Female47,1801904 (4.0)1.03 (0.96–1.10)0.43
Number of children aged 1–9 years in household    
 1–376,8992900 (3.8)1 
 ≥416,813863 (5.1)1.44 (1.31–1.57)<0.0001
Latrine typec    
 Improved16,040382 (2.4)1<0.0001
 Unimproved36,6841160 (3.2)1.21 (1.07–1.37)
 No facilities, bush or field40,9882221 (5.4)1.46 (1.30–1.64)
Latrine accessd    
 Shared latrine6681172 (2.6)1.05 (0.86–1.29)<0.0001
 Private latrine42,9201182 (2.8)1
 No structure, outside near the house, in the bush or field44,1112409 (5.5)1.25 (1.13–1.38)
Time to source of water for face-washingd    
 All face-washing done at source3076 (1.9)1.51 (0.60–3.78)0.2226
 <30 mins round-trip43,2511486 (3.4)1
 ≥30 mins round-trip50,1542271 (4.5)1.09 (0.98–1.22)
Time to source of water for drinkingd    
 <30 mins round-trip43,0841485 (3.4)1 
 ≥30 mins round-trip50,6282278 (4.5)1.08 (0.97–1.21)0.1576
Source of water for drinking    
 Improved water source53,5611911 (3.6)10.112
 Unimproved water source17,930934 (5.2)1.11 (0.95–1.30)
 Surface water (river, lake, dam)22,221918 (4.1)1.16 (0.99–1.36)
Source of water for face-washing    
 Improved water source52,3671872 (3.6)10.0944
 Unimproved water source18,240949 (5.2)1.12 (0.96–1.31)
 Surface water (river, lake, dam)22,835942 (4.1)1.17 (1.00–1.35)

aUnivariable odds ratio and 95% confidence interval against the outcome trachomatous inflammation—follicular in children aged 1–9 years. Analysis carried out at individual level.

bWald’s test

cDirect observation by recorders

dSelf-reported estimates from household head.

Table 3.

Multivariable mixed-effects logistic regression analysis of the outcome trachomatous inflammation–follicular (TF) in children aged 1–9 years against putative risk factors, using data from 91 population-based prevalence surveys, Global Trachoma Mapping Project, Mozambique, 2012–2015.

VariableOdds ratio (95% CI)ap-valueb
Age
1–42.19 (2.03–2.36)<0.0001
5–91 
Number of children aged1–9 years in household
1–31 
≥41.51 (1.38–1.65)<0.0001
Latrine typec
Improved1<0.0001
Unimproved1.24 (1.03–1.49)
No facilities, bush or field1.45 (1.22–1.73)

aMultivariable odds ratio and 95% confidence interval against the outcome trachomatous inflammation–follicular in children aged 1–9 years. Analysis carried out at individual level.

bLikelihood ratio test of inclusion/exclusion in the full multivariable model.

cDirect observation by recorders.

Univariable mixed-effects logistic regression analysis of the outcome trachomatous inflammation–follicular (TF) in children aged 1–9 years against putative risk factors, using data from 91 population-based prevalence surveys, Global Trachoma Mapping Project, Mozambique, 2012–2015. aUnivariable odds ratio and 95% confidence interval against the outcome trachomatous inflammation—follicular in children aged 1–9 years. Analysis carried out at individual level. bWald’s test cDirect observation by recorders dSelf-reported estimates from household head. Multivariable mixed-effects logistic regression analysis of the outcome trachomatous inflammation–follicular (TF) in children aged 1–9 years against putative risk factors, using data from 91 population-based prevalence surveys, Global Trachoma Mapping Project, Mozambique, 2012–2015. aMultivariable odds ratio and 95% confidence interval against the outcome trachomatous inflammation–follicular in children aged 1–9 years. Analysis carried out at individual level. bLikelihood ratio test of inclusion/exclusion in the full multivariable model. cDirect observation by recorders.

Discussion

The GTMP may be the largest field-based infectious disease mapping project ever attempted, and the work conducted in Mozambique was its largest single constituent project. Altogether, 91 separate standardised population-based trachoma prevalence surveys, involving the individual examination of a total of more than 200,000 people of all ages, were carried out by trained and certified field teams; complementing five trachoma prevalence surveys previously carried out in 2012. The successful completion of this work is an excellent indication of the Mozambique programme’s readiness to proceed to national elimination of trachoma as a public health problem. Slightly more women than men were examined. This reflects the demographic structure of the Mozambican population, in which there are more women than men, but also a higher probability of women being at home at the time of data collection. (In the areas surveyed, during the day, the majority of men work in the fields.) Most of the surveys reported in this paper were baseline surveys, i.e., conducted prior to the initiation of any community-based interventions against trachoma. The exceptions were the nine surveys done in Niassa Province, where a previous (July 2011) province-level survey had estimated the TF prevalence in 1–9-year-olds to be 32% [unpublished Ministry of Health data]. On the basis of that estimate, mass treatment with azithromycin was undertaken in 10 of Niassa’s 16 districts in 2013 (with 9/10 districts achieving >80% coverage), and in all 16 districts in 2014 (with 6/16 districts achieving >80% coverage). Whether the 2011 estimates were overestimates or the effect of those rounds of mass treatment was large – or both – is difficult to say, but it is notable that each of the Niassa EUs for which we conducted impact surveys in 2015 (this study) returned estimated TF prevalences of <5%. Surveillance surveys should be undertaken in these EUs in 2017. In total, of 96 EUs, 12 had TF prevalences ≥10% among children aged 1–9 years. These 12 EUs, composed of 20 districts, should have mass treatment with azithromycin annually for the next 3 years.[15] In addition, 17 EUs, composed of 28 districts, had TF prevalences between 5.0 and 9.9% and may be considered for one round of azithromycin treatment before re-survey, following informal recommendations from the International Trachoma Initiative. All 29 of these EUs also need implementation of the F&E components of the SAFE strategy. Overall, 67 EUs had a TF prevalence in children aged 1–9 years below the WHO elimination threshold, yet eight of these EUs had a trichiasis prevalence ≥0.2% of those aged 15 years and above, suggesting that active trachoma is likely to have been a public health problem there in the past. A total of 77% of the EUs surveyed (74/96) had a trichiasis (or TT) prevalence below the threshold defined by WHO to indicate a public health problem: 0.2% in adults aged 15 years and above.[14] The EU comprising Nangade and Palma districts in Cabo Delgado Province had a trichiasis prevalence in those aged 15 years and above of >1%. The reason for the much higher burden of trichiasis in this area is unclear. In the 22 EUs with trichiasis prevalences ≥0.2% in adults, resources should be made available to ensure that individuals who need surgery have access to a high quality service provided, if possible, at no cost to the recipient. We found that younger children, and children sharing a household with larger numbers of children had higher odds of TF in this population. This is in keeping with the belief that younger children act as the reservoir of infection for ocular C. trachomatis,[16] and that close contact with more children is likely to facilitate spread.[17-20] In addition, we found that unimproved latrine facilities, or living in a household with no latrine facilities, was an independent association of TF in children. Similar associations have been noted elsewhere, both prior to[21-24] and within the GTMP,[20,25-27] and are in keeping with the belief that latrine facilities of a given standard limit the reproductive potential of the flies associated with transmission.[5,28,29] At larger scale, it is noted that active trachoma exists as a public health problem in Mozambique in an arc of endemicity stretching from Cabo Delgado in the country’s north-east to Tete in the west. We are unsure as to the reasons for this phenomenon, and will work with local epidemiologists and international experts to try to generate an explanation. Active trachoma is a public health concern for 48 districts of Mozambique. 6.2 million people live in those districts. It is essential to undertake distribution of azithromycin, improve sanitation services, push the importance of facial cleanliness and improve water access in these areas in order to eliminate trachoma. In addition, there is a significant backlog of individuals with trichiasis who urgently need corrective eyelid surgery in order to preserve their remaining vision.
  24 in total

Review 1.  Environmental sanitary interventions for preventing active trachoma.

Authors:  Mansur Rabiu; Mahmoud B Alhassan; Henry O D Ejere; Jennifer R Evans
Journal:  Cochrane Database Syst Rev       Date:  2012-02-15

2.  The sanitation ladder, what constitutes an improved form of sanitation?

Authors:  Josephine L R Exley; Bernard Liseka; Oliver Cumming; Jeroen H J Ensink
Journal:  Environ Sci Technol       Date:  2015-01-06       Impact factor: 9.028

3.  Latrine ownership as a protective factor in inflammatory trachoma in Egypt.

Authors:  P Courtright; J Sheppard; S Lane; A Sadek; J Schachter; C R Dawson
Journal:  Br J Ophthalmol       Date:  1991-06       Impact factor: 4.638

4.  Chlamydia trachomatis infections among Yemeni school pupils in relation to environmental conditions.

Authors:  Talal A Sallam; Yahia A Raja'a; Tawfeek K Al-Zubiery; Khaled S Al-Shaibani; Adil A Al-Shamiri; Hameed S Thobala; Ahmed S Marett; Abdullah A Al-Abbasy; Mohammed A Alqubati
Journal:  Saudi Med J       Date:  2003-01       Impact factor: 1.484

5.  Household pit latrines as a potential source of the fly Musca sorbens--a one year longitudinal study from The Gambia.

Authors:  Paul M Emerson; Victoria M Simms; Pateh Makalo; Robin L Bailey
Journal:  Trop Med Int Health       Date:  2005-07       Impact factor: 2.622

6.  The geographical distribution and burden of trachoma in Africa.

Authors:  Jennifer L Smith; Rebecca M Flueckiger; Pamela J Hooper; Sarah Polack; Elizabeth A Cromwell; Stephanie L Palmer; Paul M Emerson; David C W Mabey; Anthony W Solomon; Danny Haddad; Simon J Brooker
Journal:  PLoS Negl Trop Dis       Date:  2013-08-08

7.  Population-Based Trachoma Mapping in Six Evaluation Units of Papua New Guinea.

Authors:  Robert Ko; Colin Macleod; David Pahau; Oliver Sokana; Drew Keys; Anthea Burnett; Rebecca Willis; Geoffrey Wabulembo; Jambi Garap; Anthony W Solomon
Journal:  Ophthalmic Epidemiol       Date:  2016-11-28       Impact factor: 1.648

8.  Associations between active trachoma and community intervention with Antibiotics, Facial cleanliness, and Environmental improvement (A,F,E).

Authors:  Jeremiah Ngondi; Fiona Matthews; Mark Reacher; Samson Baba; Carol Brayne; Paul Emerson
Journal:  PLoS Negl Trop Dis       Date:  2008-04-30

9.  The Global Trachoma Mapping Project: Methodology of a 34-Country Population-Based Study.

Authors:  Anthony W Solomon; Alexandre L Pavluck; Paul Courtright; Agatha Aboe; Liknaw Adamu; Wondu Alemayehu; Menbere Alemu; Neal D E Alexander; Amir Bedri Kello; Berhanu Bero; Simon J Brooker; Brian K Chu; Michael Dejene; Paul M Emerson; Rebecca M Flueckiger; Solomon Gadisa; Katherine Gass; Teshome Gebre; Zelalem Habtamu; Erik Harvey; Dominic Haslam; Jonathan D King; Richard Le Mesurier; Susan Lewallen; Thomas M Lietman; Chad MacArthur; Silvio P Mariotti; Anna Massey; Els Mathieu; Addis Mekasha; Tom Millar; Caleb Mpyet; Beatriz E Muñoz; Jeremiah Ngondi; Stephanie Ogden; Joseph Pearce; Virginia Sarah; Alemayehu Sisay; Jennifer L Smith; Hugh R Taylor; Jo Thomson; Sheila K West; Rebecca Willis; Simon Bush; Danny Haddad; Allen Foster
Journal:  Ophthalmic Epidemiol       Date:  2015       Impact factor: 1.648

10.  Prevalence of and Risk Factors for Trachoma in Oromia Regional State of Ethiopia: Results of 79 Population-Based Prevalence Surveys Conducted with the Global Trachoma Mapping Project.

Authors:  Berhanu Bero; Colin Macleod; Wondu Alemayehu; Solomon Gadisa; Ahmed Abajobir; Yilikal Adamu; Menbere Alemu; Liknaw Adamu; Michael Dejene; Addis Mekasha; Zelalem Habtamu Jemal; Damtew Yadeta; Oumer Shafi; Genet Kiflu; Rebecca Willis; Rebecca M Flueckiger; Brian K Chu; Alexandre L Pavluck; Anthony W Solomon
Journal:  Ophthalmic Epidemiol       Date:  2016-11-07       Impact factor: 1.648

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  13 in total

1.  The cost of mapping trachoma: Data from the Global Trachoma Mapping Project.

Authors:  Guillaume Trotignon; Ellen Jones; Thomas Engels; Elena Schmidt; Deborah A McFarland; Colin K Macleod; Khaled Amer; Amadou A Bio; Ana Bakhtiari; Sarah Bovill; Amy H Doherty; Asad Aslam Khan; Mariamo Mbofana; Siobhain McCullagh; Tom Millar; Consity Mwale; Lisa A Rotondo; Angela Weaver; Rebecca Willis; Anthony W Solomon
Journal:  PLoS Negl Trop Dis       Date:  2017-10-18

2.  The Burden of and Risk Factors for Trachoma in Selected Districts of Zimbabwe: Results of 16 Population-Based Prevalence Surveys.

Authors:  Isaac Phiri; Portia Manangazira; Colin K Macleod; Takafira Mduluza; Tinashe Dhobbie; Shorai Grace Chaora; Chriswell Chigwena; Joshua Katiyo; Rebecca Willis; Ana Bakhtiari; Peter Bare; Paul Courtright; Boniface Macheka; Nicholas Midzi; And Anthony W Solomon
Journal:  Ophthalmic Epidemiol       Date:  2017-05-22       Impact factor: 1.648

3.  How much trachomatous trichiasis is there? A guide to calculating district-level estimates.

Authors:  Anthony W Solomon; Assumpta Lucienne Françoise Bella; Nebiyu Negussu; Rebecca Willis; Hugh R Taylor
Journal:  Community Eye Health       Date:  2019

Review 4.  A diagnostic instrument to help field graders evaluate active trachoma.

Authors:  Anthony W Solomon; Richard T Le Mesurier; William J Williams
Journal:  Ophthalmic Epidemiol       Date:  2018-08-01       Impact factor: 1.648

5.  The global burden of trichiasis in 2016.

Authors:  Rebecca M Flueckiger; Paul Courtright; Mariamo Abdala; Amza Abdou; Zaid Abdulnafea; Tawfik K Al-Khatib; Khaled Amer; Olga Nelson Amiel; Sossinou Awoussi; Ana Bakhtiari; Wilfried Batcho; Assumpta Lucienne Bella; Kamal Hashim Bennawi; Simon J Brooker; Brian K Chu; Michael Dejene; Djore Dezoumbe; Balgesa Elkheir Elshafie; Aba Ange Elvis; Djouma Nembot Fabrice; Fatma Juma Omar; Missamou François; Drabo François; Jambi Garap; Michael Gichangi; André Goepogui; Jaouad Hammou; Boubacar Kadri; George Kabona; Martin Kabore; Khumbo Kalua; Mathias Kamugisha; Biruck Kebede; Kaba Keita; Asad Aslam Khan; Genet Kiflu; Makoy Yibi; Garae Mackline; Colin Macleod; Portia Manangazira; Michael P Masika; Marilia Massangaie; Takafira Mduluza; Nabicassa Meno; Nicholas Midzi; Abdallahi Ould Minnih; Sailesh Mishra; Caleb Mpyet; Nicholas Muraguri; Upendo Mwingira; Beido Nassirou; Jean Ndjemba; Cece Nieba; Jeremiah Ngondi; Nicholas Olobio; Alex Pavluck; Isaac Phiri; Rachel Pullan; Babar Qureshi; Boubacar Sarr; Do Seiha; Gloria Marina Serrano Chávez; Shekhar Sharma; Siphetthavong Sisaleumsak; Khamphoua Southisombath; Gretchen Stevens; Andeberhan Tesfazion Woldendrias; Lamine Traoré; Patrick Turyaguma; Rebecca Willis; Georges Yaya; Souleymane Yeo; Francisco Zambroni; Jialiang Zhao; Anthony W Solomon
Journal:  PLoS Negl Trop Dis       Date:  2019-11-25

6.  Progress toward Elimination of Trachoma as a Public Health Problem in Seven Localities in the Republic of Sudan: Results from Population-Based Surveys.

Authors:  Angelia M Sanders; Zeinab Abdalla; Belgesa E Elshafie; Mazin Elsanosi; Andrew W Nute; Nabil Aziz; E Kelly Callahan; Scott D Nash
Journal:  Am J Trop Med Hyg       Date:  2019-12       Impact factor: 2.345

7.  Estimating the Intracluster Correlation Coefficient for the Clinical Sign "Trachomatous Inflammation-Follicular" in Population-Based Trachoma Prevalence Surveys: Results From a Meta-Regression Analysis of 261 Standardized Preintervention Surveys Carried Out in Ethiopia, Mozambique, and Nigeria.

Authors:  Colin K Macleod; Robin L Bailey; Michael Dejene; Oumer Shafi; Biruck Kebede; Nebiyu Negussu; Caleb Mpyet; Nicholas Olobio; Joel Alada; Mariamo Abdala; Rebecca Willis; Richard Hayes; Anthony W Solomon
Journal:  Am J Epidemiol       Date:  2020-01-31       Impact factor: 4.897

Review 8.  Strengthening the links between mapping, planning and global engagement for disease elimination: lessons learnt from trachoma.

Authors:  Paul Courtright; Lisa A Rotondo; Chad MacArthur; Iain Jones; Angela Weaver; Biruck Kebede Negash; Nicholas Olobio; Kamal Binnawi; Simon Bush; Mariamo Abdala; Danny Haddad; Astrid Bonfield; Paul Emerson; Virginia Sarah; Anthony W Solomon
Journal:  Br J Ophthalmol       Date:  2018-06-15       Impact factor: 4.638

9.  Quality Assurance and Quality Control in the Global Trachoma Mapping Project.

Authors:  Anthony W Solomon; Rebecca Willis; Alexandre L Pavluck; Wondu Alemayehu; Ana Bakhtiari; Sarah Bovill; Brian K Chu; Paul Courtright; Michael Dejene; Philip Downs; Rebecca M Flueckiger; Danny Haddad; P J Hooper; Khumbo Kalua; Biruck Kebede; Amir Bedri Kello; Colin K Macleod; Siobhain McCullagh; Tom Millar; Caleb Mpyet; Jeremiah Ngondi; Benjamin Nwobi; Nicholas Olobio; Uwazoeke Onyebuchi; Lisa A Rotondo; Boubacar Sarr; Oumer Shafi; Oliver Sokana; Sheila K West; Allen Foster
Journal:  Am J Trop Med Hyg       Date:  2018-10       Impact factor: 2.345

10.  Impact Survey Results after SAFE Strategy Implementation in 15 Local Government Areas of Kebbi, Sokoto and Zamfara States, Nigeria.

Authors:  Caleb Mpyet; Nasiru Muhammad; Mohammed Dantani Adamu; Mohammad Ladan; Rebecca Willis; Murtala Muhammad Umar; Joel Alada; Aliyu Attahiru Aliero; Ana Bakhtiari; Rebecca Mann Flueckiger; Nicholas Olobio; Christian Nwosu; Marthe Damina; Anita Gwom; Abdullahi A Labbo; Sophie Boisson; Sunday Isiyaku; Adamani William; Mansur M Rabiu; Alexandre L Pavluck; Bruce A Gordon; Anthony W Solomon
Journal:  Ophthalmic Epidemiol       Date:  2018-12       Impact factor: 1.648

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