Comparison of World Health Organization and Demographic and Health Surveys data to estimate sub-national deworming coverage in pre-school aged children

Nathan C Lo; Ribhav Gupta; David G Addiss; Eran Bendavid; Sam Heft-Neal; Alexei Mikhailov; Antonio Montresor; Pamela Sabina Mbabazi

doi:10.1371/journal.pntd.0008551

. 2020 Aug 17;14(8):e0008551. doi: 10.1371/journal.pntd.0008551

Comparison of World Health Organization and Demographic and Health Surveys data to estimate sub-national deworming coverage in pre-school aged children

Nathan C Lo ^1,^*, Ribhav Gupta ², David G Addiss ³, Eran Bendavid ^4,⁵, Sam Heft-Neal ⁶, Alexei Mikhailov ⁷, Antonio Montresor ⁷, Pamela Sabina Mbabazi ⁷

Editor: David Joseph Diemert⁸

PMCID: PMC7462292 PMID: 32804925

Abstract

Background

The key metric for monitoring the progress of deworming programs in controlling soil-transmitted helminthiasis (STH) is national drug coverage reported to the World Health Organization (WHO). There is increased interest in utilizing geographically-disaggregated data to estimate sub-national deworming coverage and equity, as well as gender parity. The Demographic and Health Surveys (DHS) offer a potential source of sub-national data. This study aimed to compare deworming coverage routinely reported to WHO and estimated by DHS in pre-school aged children to inform global STH measurement and evaluation.

Methodology

We compared sub-national deworming coverage in pre-school aged children reported to WHO and estimated by DHS aligned geospatially and temporally. We included data from Burundi (2016–2017), Myanmar (2015–2016), and the Philippines (2017) based on data availability. WHO provided data on the date and sub-national coverage per mass drug administration reported by Ministries of Health. DHS included maternally-reported deworming status within the past 6 months for each child surveyed. We estimated differences in sub-national deworming coverage using WHO and DHS data, and performed sensitivity analyses.

Principal findings

We compared data on pre-school aged children from 13 of 18 districts in Burundi (N = 6,835 in DHS), 11 of 15 districts in Myanmar (N = 1,462 in DHS) and 16 of 17 districts in the Philippines (N = 7,594 in DHS) following data exclusion. The national deworming coverages estimated by DHS in Burundi, Myanmar, and the Philippines were 75.5% (95% CI: 73.7%-77.7%), 47.0% (95% CI: 42.7%-51.3%), and 48.0% (95% CI: 46.0%-50.0%), respectively. The national deworming coverages reported by WHO in Burundi, Myanmar, and the Philippines were 80.1%, 93.6% and 75.7%, respectively. The mean absolute differences in district-level coverage reported to WHO and estimated by DHS in Burundi, Myanmar, and the Philippines were 9.5%, 41.5%, and 24.6%, respectively. Across countries, coverage reported to WHO was frequently higher than DHS estimates (32 of 40 districts). National deworming coverage from DHS estimates were similar by gender within countries.

Conclusions and significance

Agreement of deworming coverage reported to WHO and estimated by DHS data was heterogeneous across countries, varying from broadly compatible in Burundi to largely discrepant in Myanmar. DHS data could complement deworming data reported to WHO to improve data monitoring practices and serve as an independent sub-national source of coverage data.

Author summary

Soil-transmitted helminths are parasitic worms that infect an estimated 1 billion people globally, primarily in low- and middle-income countries. One of the main strategies to reduce the prevalence of these parasitic infections is through preventive chemotherapy—the periodic widespread treatment of an entire at-risk population (e.g. children) with albendazole or mebendazole. The World Health Organization (WHO) tracks the progress of mass deworming programs by estimating the national coverage of these programs through aggregation of local health district program reports, which represent the proportion of at-risk people in a country that are reported to have received deworming treatment. In this study, we used an alternative source of data from the Demographic and Health Surveys (DHS) that provides maternally-reported child deworming treatment to estimate coverage in pre-school aged children and was subsequently compared to data reported to WHO. We conducted this comparison of deworming coverage at a sub-national level for three countries with available data: Burundi, Myanmar, and the Philippines. Coverage reported to WHO and estimated by DHS were broadly consistent in Burundi, highly discrepant in Myanmar, and varied in agreement for the Philippines. These differences may be explained by variability in the medication distribution monitoring practices, quality of national validation systems, and mis-reporting due to albendazole receipt through non-STH related public health campaigns (e.g. lymphatic filariasis treatment). This analysis suggests that in specific settings, DHS data can complement deworming coverage data reported to WHO, serving as an independent source of data to improve monitoring and evaluation of deworming programs globally.

Introduction

Soil-transmitted helminthiasis (STH) affects an estimated one billion people worldwide, and is caused by infection with Ascaris lumbricoides, Trichuris trichiura, or either of the hookworm species, Ancylostoma duodenale or Necator americanus [1,2]. Globally, the disease burden of STH disproportionately affects children in low- and middle-income countries, but STH also affects adolescents and adults [2–5]. The burden of STH is associated with a range of clinical morbidities including abdominal pain, anemia, malnutrition, and in cases of heavy STH infection, physical (e.g. growth) and possible developmental effects, some of which may be addressed through treatment with albendazole or mebendazole [1,6–9]. Preventive chemotherapy, which involves the periodic, coordinated, large-scale administration of single-dose albendazole or mebendazole (i.e., deworming treatment) to groups at risk, is the primary public health strategy to control STH burden by both treating existing infections and decreasing worm burden to prevent future morbidity [4,10]. School-aged child deworming is typically conducted through the existing school-based infrastructure [11,12]. However, since pre-school aged children cannot access these platforms, pre-school aged deworming is primarily conducted through campaign-style Child Health Days [13,14]. In 2017 alone, deworming campaigns distributed an estimated 600 million doses of medication against STH to children in endemic countries, where approximately 188 million of the doses were delivered to pre-school aged children (1–4 years) [15].

The key metric for monitoring the progress of deworming programs remains national drug coverage (%) in school-age and pre-school aged children, as reported to the World Health Organization (WHO) [3,4,16]. The ongoing public health goal set against STH by WHO is to achieve a minimum of 75% annual national coverage in both school-aged and pre-school aged children by 2020 for all countries where STH remains endemic [4,10]. Deworming coverage is reported to WHO from national Ministries of Health and published at the national-level in the Preventive Chemotherapy and Transmission Control (PCT) databank, the Preventive Chemotherapy Data Portal and the Global Health Observatory (GHO) [17–19]. There is a growing interest in utilizing sub-national data to develop programmatic metrics to better understand geographic variation, gender parity, and other equity metrics. In addition, there is an unmet need to integrate secondary datasets for data quality control into routine measurement and evaluation efforts [20–23].

The Demographic and Health Surveys (DHS) provide routinely collected survey data that may provide a complementary source of sub-national deworming data in regions endemic to STH [24]. DHS are large, nationally representative surveys of person-level data on a wide range of health measures from low- and middle-income countries designed in coordination with governmental organizations, such as a National Statistical Office or Ministry of Health [24]. As DHS are standardized across countries and are routinely conducted approximately every five years, they allow for the systematic tracking and comparison of key indicators of deworming program success over time and geography [25]. In particular, DHS collect maternally-reported data on recent deworming receipt for their pre-school aged children [24,25]. Recently, empirical studies have analyzed sub-national coverage and equity of deworming programs using DHS data [6,23,26].

Although prior analyses have compared coverage reported to WHO at the national-level with coverage estimated from specialized validation surveys by external public health institutes, no studies have evaluated the use of sub-national coverage [27,28]. The DHS offer a potential source of routinely collected, sub-national deworming data to improve coverage metrics from existing deworming program evaluation data, although questions remain as to how these DHS coverage estimates will compare to data reported to WHO. To inform future measurement and evaluation efforts for deworming programs, we performed an empirical analysis comparing sub-national deworming coverage of pre-school aged children using data routinely reported to WHO annually and as estimated by DHS.

Methods

Overview

We calculated deworming coverage in pre-school aged children at the primary administrative level, referred to as “district” throughout the study, using data reported to WHO and measured by DHS. As deworming coverage is campaign- and time-specific, we refer to the deworming campaign coverage for a specific year and geographic area as the “deworming coverage” throughout the study (e.g. 2016 Burundi national deworming campaign coverage is referenced as “Burundi national-level deworming coverage”). We included data from three countries (Burundi, Myanmar, and the Philippines) based on availability of temporally overlapping sub-national data reported to WHO and by DHS. The coverage data were matched geospatially (at district-level) and temporally (aligning DHS administration dates with the recall periods following campaigns reported to WHO). Recall period, the time period during which reports of treatment in DHS are likely to align with a specific deworming campaign, was defined as 6 months, since in the DHS, mothers were asked if their child received deworming during the previous 6 months. This produced a pair of coverage estimates (one DHS estimated and one reported to WHO) only for the subset of districts in each country where 1) a campaign was reported to WHO and 2) any DHS responses were collected within the recall period following the campaign reported to WHO. The coverage analysis was calculated independently for each country and compared across data sources at the district-level.

Data source

We reviewed a complete list of countries with available district-level deworming coverage data available from WHO (using PCT and GHO databanks) and DHS (using Phase VII surveys). We included countries based on the following inclusion criteria: i) country was endemic for STH (determined by WHO recommendations for a national deworming program); ii) availability of national and district-level deworming campaign coverage data reported to WHO; and iii) availability of individual-level DHS data for the corresponding time period (defined by recall period of interviews conducted within the 6 months following campaigns reported to WHO as per DHS question format used in all countries surveyed) for at least a subset of districts reporting to WHO. Based on these criteria, we included data from each state of Burundi for 2016–2017, province of Myanmar for 2015–2016 and administrative region of the Philippines for 2017.

WHO publishes age-stratified, campaign coverage data at the administrative level one (equivalent to district-level) for preventive chemotherapy programs against STH, lymphatic filariasis, onchocerciasis, schistosomiasis, and trachoma in the Preventive Chemotherapy and Transmission Control databank, Preventive Chemotherapy Data Portal and Global Health Observatory [17–19,29]. WHO data were reported from each country’s Ministry of Health, and utilize programmatic data derived from district health facilities records that are reported to their corresponding districts [17]. For each campaign, WHO data provided deworming coverage by age group (e.g. pre-school aged children), the date of campaign initiation, and the district covered. Coverage reported to WHO was computed as the total number of individuals reported to have received deworming medications over the total population of targeted pre-school aged children per district for a single deworming campaign with adjustments to align with DHS data [30]. We assumed all deworming medications reported to WHO for a deworming campaign were received within the month of deworming campaign initiation. We excluded data from any deworming campaigns with a reported coverage to WHO over 95% from the calculation of district-level coverage given concerns regarding the data reliability, although we varied this reliability threshold in the sensitivity analysis [27]. We established the data reliability threshold value of 95% from a previous analysis of validation surveys by the U.S. Centers for Disease Control and Prevention for deworming campaigns from 2000–2011, where 0 of 16 surveys conducted had estimated coverage values above 95% despite reports to WHO for 7 of the 16 campaigns [27]. In addition, WHO provided data on the presence of and dates for campaigns initiated for lymphatic filariasis at a sub-district-level in countries endemic to lymphatic filariasis, as treatment campaigns for both STH and lymphatic filariasis utilize albendazole (applicable to children 2–5 years).

DHS are nationally representative surveys conducted across countries at the household- and individual-level amongst women and men with women 15–49 years of age as the primary respondents. We used a question asked of mothers in the DHS women’s module on whether each of their children received “drugs for intestinal parasites in the last 6 months”, which served as a maternally-reported metric to estimate pre-school aged child deworming coverage receipt [31]. The DHS question did not distinguish between deworming due to programmatic (i.e., deworming campaigns) and non-programmatic sources (e.g., local clinic, pharmacy, informal health practitioners) [32,33]. We excluded child deworming receipt data for children below one year of age at the estimated time of the deworming campaign (based on WHO definition of a pre-school child as 1–4 years) or when age was not specified. As surveys were conducted over an extended period in each country, we also excluded DHS observations if the date of survey administration fell outside the recall period following any campaigns reported to WHO for the district of residence, since these observations were not associated with a known source of programmatic coverage. The maternally-reported response to child deworming status was coded as “yes”, “no”, or “maybe”; the “maybe” status was assigned a 0.5 probability of receipt in the main analysis and varied in the sensitivity analysis.

Statistical analysis

The primary study outcome was the reported deworming coverage by data source. We estimated pairs of deworming coverages and the mean absolute difference in district-level coverage reported to WHO and estimated by DHS for the subset of districts per country with both DHS data and data reported to WHO. We estimated the district-level coverage reported to WHO with the following procedure. First, for each deworming campaign reported to WHO, we determined if any children in the DHS dataset were eligible to have been exposed based on the date and location from where mothers were surveyed. Any campaigns with no eligible DHS observations were excluded from data reported to WHO. Second, we estimated the treatment probability for each child targeted for treatment based on coverage data reported to WHO (e.g. 75% coverage would equal 0.75 probability). For districts where only one deworming campaign reported to WHO overlapped with the recall period following DHS sampling, we used the corresponding campaign coverage as the treatment probability for each child targeted for treatment. For districts where the recall period for two deworming campaigns reported to WHO overlapped with DHS administration dates, we used a “treatment correlation” parameter, α, to combine coverage reported to WHO from all applicable campaigns into a single treatment probability for all children targeted for treatment (See Appendix). This ensured that we generated one coverage estimate reported to WHO for each district, regardless of the number of campaigns that met inclusion criteria. This “treatment correlation” parameter, α, represents the correlation between coverage in two sequential deworming campaigns. The “treatment correlation” parameter was calibrated to each country to minimize the difference in district-level coverage reported to WHO and estimated by DHS to provide an optimistic assumption of compatibility. Given that this treatment correlation parameter is unknown due to the absence of prior estimates in the literature, we varied this parameter in sensitivity analyses. Third, to represent the annual district-level deworming coverage reported to WHO, we aggregated child treatment probabilities at a district-level divided by the total number of children reported to WHO to have been targeted for deworming. Finally, to calculate the national-level deworming coverage using data reported to WHO, we utilized a population-weighted average of coverage across all districts.

We estimated the district-level deworming coverage by DHS data with the following procedure. First, we assigned a treatment probability to each child in the DHS dataset based on maternal report of her child’s deworming status. Second, we estimated the district-level coverage by calculating the mean probability of deworming receipt using maternally-reported child deworming status per district. We estimated the standard deviation of the district-level DHS coverage estimation by assuming a binomial distribution of deworming receipt given the binary treatment of all survey response options for deworming receipt status. Finally, we estimated the national-level deworming coverage and standard deviation by calculating the mean probability of deworming receipt using DHS data across all districts. For national-level coverage estimates, we applied DHS-provided survey weights, due to the multi-stage cluster sampling procedure, which are intended to estimate statistics representative at the national-level from sample data [24]. We estimated gender-stratified, national-level and district-level coverages using the same methodologies outlined.

This study relied upon data de-identified during collection by DHS, and data provided at the district-level from WHO. The study met all requirements for review exemption by the University of California, San Francisco Institutional Review Board. All analyses were conducted using the R programming language version 3.8.0 (R Foundation for Statistical Computing, Vienna, Austria) and the Stata/IC Software version 15.1 (StataCorp LP, College Station, United States). The analysis files are available online [34].

Sensitivity analyses

We conducted a series of sensitivity analyses on key parameters and assumptions to determine the robustness of our findings and test potential explanations for differences across countries. We adjusted DHS survey inclusion by varying the recall period assumed for maternally-reported response (base case: 6 months) across a short-term period of 4 to 8 months that we believed would still yield reliable DHS responses to child deworming receipt to evaluate scenarios when the true maternal recall might have been shorter or longer than designated by the actual survey question. We also varied the campaign reliability threshold (base case: exclude WHO data with >95% coverage) to test exclusion of campaigns with >90% coverage to no exclusion based on reported coverage. We varied the “treatment correlation” parameter to test scenarios with a 70% correlation between the populations targeted by overlapping deworming campaigns reported to WHO. We performed an analysis of coverage differences across data sources, after exclusion of children surveyed by DHS below 2 years of age, stratified by district-level treatment of lymphatic filariasis using albendazole and Diethylcarbamazine (applicable to children 2–5 years) to serve as a proxy to assess the effect of misclassification due to non-deworming public health programs also distributing albendazole (see Appendix). Finally, we varied the probability of deworming receipt for “maybe” responses in the DHS (base case: +0.5) to test +0 to +1 deworming receipt probability and assess the effect on DHS coverage estimates.

Results

Descriptive results

We included DHS observations collected in Burundi from October 2016 to March 2017, Myanmar from December 2015 to July 2016, and the Philippines from August 2017 to October 2017 (see Fig 1). DHS observation periods corresponded to deworming campaigns reported to WHO for Burundi during June 2016 and November 2016, for Myanmar during August 2015, and for the Philippines during January 2017 and July 2017 (Fig 1).

Fig 1 — We obtained sub-national deworming coverage data reported to WHO and estimated by DHS across comparable timelines. We used WHO data on programmatic deworming to determine national deworming campaign start dates (dates on left-hand side and time-axis in month/year, e.g. 06/2016 is June 2016) and estimated coverage for Burundi (panel A), Myanmar (panel B) and the Philippines (panel C). We used DHS data from each country to estimate deworming coverage by matching respondents to the corresponding deworming campaigns, based on the survey date and recall period in the orange/gray bars (base, 6-month recall; alternatively, 4- or 8-months based on the sensitivity analysis) that represents the accepted time period (months) after deworming campaign start.

We estimated deworming coverage using data reported to WHO and estimated by DHS for 13 of 18 districts of Burundi, 11 of 15 districts of Myanmar, and 16 of 17 districts of the Philippines. The data from remaining districts were excluded from analysis based on criteria for data unreliability defined as coverage reported to WHO > 95% (N = 5 districts in Burundi, N = 3 districts in Myanmar, N = 1 in the Philippines) or absence of any DHS data for a given district (N = 1 district in Myanmar). After data exclusion, the final study sample size for DHS maternally-reported data was 6,835 pre-school aged children in Burundi (50.2% female), 1,462 pre-school aged children in Myanmar (48.3% female), and 7,594 pre-school aged children in Philippines (47.6% female; See Figs 2 and 3 and Appendix).

Fig 2 — The data inclusions and exclusion are provided for the three study countries: (A)Burundi; (B) Myanmar; and (C) Philippines. Key definitions: N_OBS: Number of pre-school aged children sampled in DHS; N_CAMPAIGN: Number of deworming campaigns reported to WHO; Pre-SAC: Pre-school aged children (12–59 months). In DHS observations, the response of “yes/no” refers to children who were or were not dewormed, while “maybe” refers to mothers who were unable to recall.

Fig 3 — We determined the sample size of DHS respondents at a district-level and estimated the proportion eligible for one or two deworming campaigns based on the maternal recall period used for DHS observation inclusion. These data are visualized for Burundi (panel A), Myanmar (panel B), and the Philippines (panel C) each with a base case recall period of 6 months based on formulation of the original DHS question for deworming receipt.

Main analysis

In Burundi, using data reported to WHO, the estimated national-level deworming coverage was 80.1% with district-level estimates ranging from 55.1% (Bujumbura Mairie) to 91.1% (Bujumbura Rural) (See Fig 4). Using DHS deworming data, the estimated national-level deworming coverage was 75.5% (95% CI: 73.3%-77.7%) with district-level estimates ranging from 58.1% (Cibitoke) to 88.6% (Kayanza). Stratifying coverage by child gender, using DHS deworming data the estimated national-level deworming coverage for girls was 74.6% and was 76.5% for boys of pre-school age (See Fig 5). The mean absolute difference in district-level deworming coverage reported to WHO and estimated by DHS data was 9.5% with absolute differences ranging from 0.9% (Kirundo) to 32.4% (Cibitoke). Coverage estimates reported to WHO were higher in 9 of 13 districts included.

Fig 4 — We obtained WHO data on district-level deworming coverage reported by national Ministries of Health based on health facilities data. We estimated district-level deworming coverage from DHS data corresponding to the known WHO deworming campaigns using maternally-reported data on child deworming receipt. The comparison of deworming coverage using WHO and DHS data are shown for a base case recall period of 6-month in Burundi (panel A), Myanmar (panel B) and the Philippines (panel C) based on the DHS question formulation.

Fig 5 — We estimated the national-level deworming coverage using maternally-reported child deworming status from DHS stratified by child gender (girls and boys). We compared the estimated coverage by gender using a base recall period of 6 months in Burundi, Myanmar, and the Philippines as based on the DHS question formulation.

In Myanmar, using data reported to WHO, the estimated national-level deworming coverage was 93.6% with district-level estimates ranging from 88.4% (Kayah) to 94.9% (Mandalay). Using DHS deworming data, the estimated national-level deworming coverage was 47.0% (95% CI: 42.7%-51.3%) with district-level estimates ranging from 29.8% (Sagaing) to 71.1% (Kayah). Stratifying coverage by child gender, using DHS deworming data the estimated national-level deworming coverage was 49.2% for girls and 44.8% for boys of pre-school age. The mean absolute difference in district-level deworming coverage reported to WHO and estimated by DHS data was 41.5% with absolute differences ranging from 17.3% (Kayah) to 62.2% (Sagaing). Coverage estimates reported to WHO were higher in all 11 districts included.

In the Philippines, using data reported to WHO, the estimated national-level deworming coverage was 75.7% with district-level estimates ranging from 42.0% (Zamboanga Peninsula) to 92.9% (Central Luzon). Using DHS deworming data, the estimated national-level deworming coverage was 48.0% (95% CI: 46.0%-50.0%) with district-level estimates ranging from 24.1% (National Capital) to 82.9% (Cordillera). Stratifying coverage by child gender, using DHS deworming data the estimated national-level deworming coverage for girls was 48.4% and was 47.6% for boys of pre-school age. The mean absolute difference in district-level deworming coverage reported to WHO and estimated by DHS data was 24.6% with absolute differences ranging from 1.1% (Eastern Visayas) to 61.5% (Bangsamoro). Coverage estimates reported to WHO were higher in 12 of 16 districts included.

National-level deworming coverage reported to WHO was higher in all three countries studied—Burundi, Myanmar, and the Philippines—compared to coverages estimated using DHS data. Cumulatively across all three countries, district-level coverage reported to WHO was higher than coverage estimated using DHS data for 32 of the 40 districts included.

Sensitivity analyses

Varying the assumed maternal recall period for deworming receipt in DHS data, which served as inclusion criteria for DHS observations, had minimal impact on the sample size and the estimated coverage for Burundi and the Philippines, but had larger effects on the sample size and coverage estimates using DHS data in Myanmar (see Appendix). Varying the data reliability threshold, used to exclude WHO reported campaigns, had minimal effects on the mean absolute differences in district-level deworming coverage in Burundi and the Philippines but had large effects in Myanmar. Varying the “treatment correlation” parameter, used to estimate treatment probability for children exposed to multiple deworming campaigns reported to WHO, had large effects on the national-level coverage using data reported to WHO for Burundi but had no effect on coverage estimates for Myanmar and the Philippines, as in the latter countries all DHS observations aligned with a single campaign given a 6-month recall period. In Burundi, varying the “treatment correlation” parameter from 70% (optimized scenario of data compatibility) to 100% correlation between treatment by overlapping deworming campaigns reported to WHO changed the mean absolute difference in district-level deworming coverage from 10.1% to 9.6%.

Discussion

In this study, we used datasets from WHO and DHS to compare sub-national deworming coverage estimates for pre-school aged children in Burundi, Myanmar and the Philippines. We found the results to be heterogeneous; deworming coverage estimates by data source were broadly compatible for Burundi, largely discrepant for Myanmar, and varied in agreement for the Philippines. Sub-national differences in coverage across data sources presented no apparent geospatial clustering within each country. In addition, we found deworming coverage by gender to be broadly similar at the national-level within all three countries when using DHS data.

This study included data from three countries with diverse settings to highlight scenarios with different geographies, levels of STH prevalence, baseline deworming coverage, and lymphatic filariasis endemicities [35,36]. The study also highlighted key challenges to integrating DHS and WHO data for deworming coverage estimates including reliability of systems reporting to WHO, accuracy of DHS self-reported deworming receipt, and concurrent medication by other public health programs using albendazole, such as for lymphatic filariasis (where mothers may misclassify treatment as only preventive for lymphatic filariasis and not as a deworming treatment). This analysis highlighted opportunities to identify sub-national regions with low coverage that may merit further investigation. Variations in sub-national coverage per country may be attributed to the use of decentralized health systems where sub-national regions are left to apply their own methods for implementing deworming activities. Overall, the findings support the utilization of sub-national DHS deworming data under specific settings to complement data reported to WHO by serving as an independent source of geographically and demographically disaggregated coverage data for pre-school age children.

The difference in agreement of coverage estimated using data reported to WHO and from DHS for Burundi, Myanmar and the Philippines may be partially explained by variation in the quality of national data systems reporting to WHO and recall inaccuracies in the DHS survey due to self-reporting or due to misclassification of albendazole receipt from other public health programs (e.g. albendazole for lymphatic filariasis) or from non-programmatic distribution (e.g. albendazole given in clinic). One explanation may be differences in quality of data reporting and health information management protocols, which can be driven by a host of factors (e.g., financial, robustness of reporting systems) across multiple levels of data processing (e.g. health facility, districts, country level) affecting data reported to WHO. All three countries reported greater than 95% and sometimes above 100% deworming coverage to WHO for some districts, which based on prior literature are unlikely to be accurate estimates [27,28]. As district-level deworming coverage estimates reported to WHO were typically higher than estimates by DHS (32 out of 40 districts, particularly for all 11 districts of Myanmar included), this could indicate a systemic cause of under-estimation in DHS datasets (e.g., maternal under-reporting) or of over-estimation in WHO datasets (e.g., albendazole medication delivered but not administered to a child). In the case of Myanmar, regional endemicity for lymphatic filariasis potentially increased misclassification of coverage estimates by DHS data as maternally-reported deworming receipt were much lower than reports to WHO, possibly due to only associating the treatment with its indication of lymphatic filariasis (applicable for children 2–5 years) and not for deworming. While DHS data may be biased from including unprogrammed sources of deworming receipt, not present in data reported to WHO, this would bias estimates by DHS to over-estimate coverage, which would not explain the observed pattern of discrepancies. On the other hand, in Burundi, exclusion of 30% of campaigns reported to WHO due to data reliability concerns (coverage >95%) may indicate an under-reporting of the target population size (e.g. use of outdated census data) or over-reporting of doses delivered (e.g. doses delivered outside target age range) to WHO.

Our study findings present some differences from prior analyses that compared coverage reported to WHO with estimates by independent surveys. Deworming coverage validation surveys have previously been conducted by the U.S. Centers for Disease Control and Prevention and Hellen Keller International immediately following national deworming campaigns [27,28]. Both series of validation surveys used multi-stage sampling methodologies for household data collection similar to methods employed by DHS and defined coverage through similar methods to those used for estimating coverage by DHS. Both validation survey analyses found that deworming coverage estimates reported to WHO were widely discrepant from their independent estimation efforts at the national-level. Yet, comparatively, our analysis found coverage reported to WHO and estimated by DHS data may be compatible in some settings. Although validation surveys are specifically designed to monitor deworming program success, the DHS offer a routine and standardized alternative that will likely prove cost-efficient given that deworming data are already collected as part of a larger effort (and there would not be additional cost to access this data), rather than through a specialized survey requiring additional resource allocation. Additionally, DHS would provide a dataset to track supplementary equity metrics (e.g. coverage by gender, urban/rural living, and socioeconomic status) currently unstudied given the format of data reported by national governments to WHO, and could be correlated with conventional metrics such as the Human Development Index. In settings with limited albendazole distribution outside of deworming campaigns (e.g. in districts non-endemic for lymphatic filariasis) and where districts endemic to STH reported a deworming coverage of below 90% to WHO, the likelihood for comparability with sub-national DHS data appears much higher. The broad compatibility of coverage estimated by DHS data with reports to WHO in Burundi was particularly notable given political unrest in the country at the time of the data reporting in 2016–2017 that may have disrupted public health efforts [37,38]. Gender-specific estimates of deworming coverage from DHS showed no marked differences (see Appendix), and as deworming coverage data reported to WHO becomes gender-stratified, further analyses can be undertaken to measure the agreement of gender equity metrics.

The deworming coverage data reported to WHO and estimated by DHS measure deworming using different mechanisms. Most notably, DHS deworming data are reported based on maternal recall and may include coverage by unprogrammed sources (e.g. local pharmacy, clinic) and albendazole receipt for non-STH related public health campaigns (e.g. as part of lymphatic filariasis treatment) in addition to coverage by programmed sources. As DHS data relied on maternally-reported child deworming receipt, they are potentially subject to misclassification of treatment arising from recall bias. However, recent work suggests maternally-reported child deworming data are a reliable estimate of programmatic coverage in the short-term with reported estimates within 5–10% of official campaign estimates, and with vaccine coverage estimates by WHO routinely incorporating maternally-reported survey data [39,40]. Additionally, as DHS coverage is estimated using survey sample data, non-response biases may affect the sample representativeness, which is not the case with data reported to WHO. However, as the response rates for all three countries were over 90% (with no indications of differential non-response) and survey weights were applied for national-level comparisons, we believe DHS data are likely representative at least at the national-level [41–43]. In aggregating data at multiple points from the individual health facility records up to the national-level, it is possible that records reported to WHO were re-counted or contained errors, which may both have over- or under-estimated coverage reported; however, this is not possible for DHS datasets which provided household level survey data. Similar differences in data collection methodologies have been noted in vaccine literature when comparing annual administrative and survey data on vaccine coverage. As both sources have their merits and limitations, agencies often rely on field experts to determine the most probable vaccine coverage with WHO recommendations for the periodic implementation of data quality and coverage evaluation surveys [40,44,45].

The study results should be taken in context with the limitations of the data and assumptions of the analysis (see Appendix). As only three countries were included, due to current challenges aligning DHS and WHO datasets available, the analysis presents unique scenarios of compatibility but is unlikely to be globally representative of the compatibility of data reported to WHO and by DHS. The proposed secondary source of deworming data, DHS, relied on maternally-reported child deworming receipt, which are potentially subject to recall bias as discussed above. In selecting data, we excluded deworming campaigns reporting >95% coverage to WHO based on literature suggesting such coverage values are often inaccurate and over-estimated [27,28] although the optimal threshold for data reliability remains unclear given the high variability in the number of districts in our analysis upon varying the threshold chosen. Additionally, WHO recommends semiannual treatment in countries with higher STH endemicity, assuming a 6-month separation between campaigns. However in some cases (i.e. select districts of Burundi) campaigns were conducted within a shorter time period of one another. This may be due to alignment with other public health campaigns or anticipated seasonal variation in campaign resources. To adjust treatment probabilities for repeat coverage over multiple campaigns, a “treatment correlation” parameter α was derived through a fitting process that was designed to minimize differences between district-level coverage reported to WHO and estimated by DHS (i.e. the optimistic scenario). The fit parameter’s value of α = 0.822 applied to coverage estimation for data reported to WHO from Burundi indicates we assumed 67.8% of all children covered were treated by both deworming campaigns—an assumption that is unlikely to have occurred as the parameter was fit for the most optimistic case of coverage estimate compatibility. Furthermore, sensitivity analysis found that this poorly characterized parameter will likely affect the study findings in settings with semiannual treatment and further empirical data are needed to understand these model inputs. As lymphatic filariasis coverage data are often reported for all age groups and are not specific to pre-school children, misclassification of albendazole receipt for only lymphatic filariasis may have a smaller effect than found given we assumed all districts receiving treatment will include pre-school aged children in coverage. Myanmar presents a case study for compatibility limitations due to a much smaller DHS sample size (<150 samples per district) than those used for Burundi or the Philippines which may limit the sub-national representation of deworming estimates from DHS data and have led to the wide discrepancies in coverage estimates by data source observed. This was due to a smaller DHS campaign, fewer children per mother interviewed, and a very limited alignment of the DHS sampling period with the recall period for deworming campaigns reported to WHO. Finally, as DHS samples are collected approximately every five years, this precludes their annual use as per conventional WHO reporting and with many DHS datasets only made available a year or longer following surveying, this limits the ability to provide real-time feedback for national deworming.

In this study, we found that district-level deworming coverage estimates for pre-school aged children reported to WHO and estimated by DHS were broadly consistent across Burundi, highly discrepant in Myanmar, and varied in alignment across the Philippines. As deworming coverage remains a key metric in monitoring and evaluating programs against STH globally, our study indicates that incorporation of DHS for deworming coverage for quality control, under specific settings, may improve data quality and resource allocation by systematically classifying the urgency of coverage validation surveys for each national reporting system. Additionally, where coverage estimates are compatible, DHS deworming data can support the development of novel deworming coverage equity metrics, such as by geography and gender.

Supporting information

S1 Checklist. STROBE Checklist.

(PDF)

Click here for additional data file.^{(532.2KB, pdf)}

S1 Technical Appendix. Additional methodologic details for estimating deworming coverage from data reported to the World Health Organization (WHO) and by using the Demographic and Health Surveys (DHS) data.

(DOCX)

Click here for additional data file.^{(92.8KB, docx)}

S1 Table. District-level deworming coverage in pre-school aged children using data reported to WHO and estimated by DHS in Burundi under base case analysis.

(DOCX)

Click here for additional data file.^{(16.2KB, docx)}

S2 Table. District-level deworming coverage in pre-school aged children using data reported to WHO and estimated by DHS in Myanmar under base case analysis.

(DOCX)

Click here for additional data file.^{(15.8KB, docx)}

S3 Table. District-level deworming coverage in children 2–4 years of age using data reported to WHO and estimated by DHS in the Philippines under base case analysis.

(DOCX)

Click here for additional data file.^{(16.2KB, docx)}

S4 Table. District-level deworming coverage in pre-school aged children using data reported to WHO and estimated by DHS in Myanmar and the Philippines stratified by level of endemicity of lymphatic filariasis.

(DOCX)

Click here for additional data file.^{(15.8KB, docx)}

S1 Fig. District-level sample size of DHS observations used to estimate deworming coverage in study countries, varying maternal recall period.

We estimated the sample size of DHS respondents at the district-level and estimated the proportion eligible for one or two deworming campaigns based on a maternal recall period of 6 months. This data is visualized for Burundi (panel A-B), Myanmar (panel C-D), and the Philippines (panel E-F), each with a varying maternal recall period from 4 or 8 months.

(DOCX)

Click here for additional data file.^{(1.7MB, docx)}

S2 Fig. Correlation between district-level deworming coverage in pre-school aged children reported to WHO and estimated using DHS data for study countries.

We obtained WHO data on district-level deworming coverage reported by national Ministries of Health based on health facility records. We estimated district-level deworming coverage with DHS data corresponding to the known WHO deworming campaigns using maternally-reported data on child deworming receipt. The comparison of deworming coverage using WHO and DHS data are shown for the base case recall period of 6-month in Burundi (panel A), Myanmar (panel B) and the Philippines (panel C) based on the DHS question formulation.

(DOCX)

Click here for additional data file.^{(953.2KB, docx)}

S3 Fig. Comparison of district-level deworming coverage in pre-school aged children reported to WHO and estimated by DHS in study countries, varying maternal recall periods.

We obtained WHO data on district-level deworming coverage reported by national Ministries of Health based on health facility records. We estimated district-level deworming coverage with DHS data corresponding to the known WHO deworming campaigns using maternal-reported data on child deworming receipt. The comparison of deworming coverage using WHO and DHS data is shown with varying maternal recall period from 4 months to 8 months in Burundi (panel A-B), Myanmar (panel C-D), and the Philippines (panel E-F).

(DOCX)

Click here for additional data file.^{(1.8MB, docx)}

S4 Fig. Comparison of district-level deworming coverage in pre-school aged children reported to WHO and estimated by DHS in study countries, varying threshold for data reliability.

We obtained WHO data on district-level deworming coverage reported by national Ministries of Health based on health facility records. We estimated district-level deworming coverage with DHS data corresponding to the known WHO deworming campaigns using maternal-reported data on child deworming receipt. The comparison of deworming coverage using WHO and DHS data is shown with a varying data reliability threshold (base case of 95%) from > 90% to no threshold to change the districts meeting inclusion criteria in Burundi (panel A-B), Myanmar (panel C-D), and the Philippines (panel E-F).

(DOCX)

Click here for additional data file.^{(1.5MB, docx)}

S5 Fig. Comparison of district-level deworming coverage in pre-school aged children reported to WHO and estimated by DHS in study countries, varying the treatment correlation parameter.

We obtained WHO data on district-level deworming coverage reported by national Ministries of Health based on health facility records. We estimated district-level deworming coverage with DHS data corresponding to the known WHO deworming campaigns using maternal-reported data on child deworming receipt. The comparison of deworming coverage using WHO and DHS data for Burundi is shown with a varying treatment correlation (base case of 67.8%) value from 70% (panel A) to 100% (panel B).

(DOCX)

Click here for additional data file.^{(706.2KB, docx)}

S6 Fig. Comparison of district-level deworming coverage in pre-school aged children reported to WHO and estimated by DHS in study countries, varying treatment probability for “maybe” responses in DHS data.

We obtained WHO data on district-level deworming coverage reported by national Ministries of Health based on health facility records. We estimated district-level deworming coverage with DHS data corresponding to the known WHO deworming campaigns using maternal-reported data on child deworming receipt. The comparison of deworming coverage using WHO and DHS data is shown with varying receipt status for all “maybe” DHS responses (base case of +0.5) from +0 and +1 in Burundi (panel A-B), Myanmar (panel C-D), and the Philippines (panel E-F).

(DOCX)

Click here for additional data file.^{(1.8MB, docx)}

S7 Fig. Comparison of mean district-level deworming coverage in children 2–4 years of age reported to WHO and estimated by DHS for countries endemic to lymphatic filariasis stratified by level of endemicity.

Endemicity to lymphatic filariasis was determined by sub-district-level data reported to WHO on the record of a treatment campaign during the year of deworming analysis within each country. In Myanmar, districts were categorized as endemic to lymphatic filariasis if half of sub-districts received treatment (panel A) and in the Philippines, districts were categorized as endemic to lymphatic filariasis if any of their sub-districts received treatment (panel B). Note difference in endemicity definition used for Myanmar and the Philippines.

(DOCX)

Click here for additional data file.^{(437.5KB, docx)}

Data Availability

DHS data files are provided publicly by DHS MEASURE (https://dhsprogram.com/data) and WHO data can be accessed publicly from the Preventive Chemotherapy and Transmission Control databank, the Preventive Chemotherapy Data Portal or the Global Health Observatory (https://who.int/neglected_diseases/preventive_chemotherapy/sth/en). All files used to conduct the analysis and corresponding instructions will be made available online (https://github.com/NathanLo3/Publication-codes).

Funding Statement

NCL, RG, and DGA report funding from the World Health Organization (https://www.who.int/) for this project. World Health Organization provided previously collected data yet played no role in study design, data analysis, decision to publish, or preparation of the manuscript.

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PLoS Negl Trop Dis. doi: 10.1371/journal.pntd.0008551.r001

Decision Letter 0

David Joseph Diemert, Jennifer Keiser

12 Mar 2020

Dear Dr. Lo,

Thank you very much for submitting your manuscript "Comparison of World Health Organization and Demographic and Health Surveys data to estimate sub-national deworming coverage in pre-school aged children" for consideration at PLOS Neglected Tropical Diseases. As with all papers reviewed by the journal, your manuscript was reviewed by members of the editorial board and by several independent reviewers. In light of the reviews (below this email), we would like to invite the resubmission of a significantly-revised version that takes into account the reviewers' comments.

We cannot make any decision about publication until we have seen the revised manuscript and your response to the reviewers' comments. Your revised manuscript is also likely to be sent to reviewers for further evaluation.

When you are ready to resubmit, please upload the following:

[1] A letter containing a detailed list of your responses to the review comments and a description of the changes you have made in the manuscript. Please note while forming your response, if your article is accepted, you may have the opportunity to make the peer review history publicly available. The record will include editor decision letters (with reviews) and your responses to reviewer comments. If eligible, we will contact you to opt in or out.

[2] Two versions of the revised manuscript: one with either highlights or tracked changes denoting where the text has been changed; the other a clean version (uploaded as the manuscript file).

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Thank you again for your submission. We hope that our editorial process has been constructive so far, and we welcome your feedback at any time. Please don't hesitate to contact us if you have any questions or comments.

Sincerely,

David Joseph Diemert, M.D.

Associate Editor

PLOS Neglected Tropical Diseases

Jennifer Keiser

Deputy Editor

PLOS Neglected Tropical Diseases

***********************

Reviewer's Responses to Questions

Key Review Criteria Required for Acceptance?

As you describe the new analyses required for acceptance, please consider the following:

Methods

-Are the objectives of the study clearly articulated with a clear testable hypothesis stated?

-Is the study design appropriate to address the stated objectives?

-Is the population clearly described and appropriate for the hypothesis being tested?

-Is the sample size sufficient to ensure adequate power to address the hypothesis being tested?

-Were correct statistical analysis used to support conclusions?

-Are there concerns about ethical or regulatory requirements being met?

Reviewer #1: o Please clarify in the manuscript the inclusion criteria for DHS-campaign dataset ‘pairs’.

� Were campaigns reported to WHO included only if a DHS took place in the same district within 6 months after the campaign? Were DHSs included only if a campaign had taken place in the same district within the previous 6 months? Could a given district be included more than once if it had two different DHSs, each within six months of a different deworming campaign?

� What does a ‘base’ of 6 months that varied from 4-8 months mean? – what criteria were actually applied to yield this range?

� Since DHSs are not done instantly, did the time windows for inclusion correspond to the beginning or the end of the DHS data collection period in the district? What if a DHS was started in the district less than 6 months after a campaign, but finished more than 6 months?

� Related, what does ‘assumed recall’ of 6 months in the DHS mean? If it refers to the time period specifically listed in the DHS question asked of the respondent, there’s nothing ‘assumed’ about it. I’d recommend getting rid of or defining the term. If the length of time respondents were asked about varied by country, I would state that clearly when you quote the DHS question, and since there were just three countries, perhaps state which it was for each. Later the term ‘recall period’ recurs – similarly please define this if you need to keep it. It’s not clear to me that there’s a role here for worrying about the length of a respondent’s recall at all since the time window is clearly defined.

o The sentence “we excluded any deworming campaigns with a reported coverage to WHO over 95%...” is not clear – does this mean you excluded the involved districts from the analysis completely, or that you used the DHS data for those districts but not the WHO data somehow? Please specify in the manuscript.

o Please state the source for the population estimates (including age-specific estimates) used as the denominators for the WHO coverage calculations, as well as the sources of the population estimates used in the DHS study designs.

o Please clarify what level of “location” campaigns were conducted on – were they district-level, multidistrict, did they sometimes include only part of a district, etc? the reader needs to have confidence that the areas covered are identical when comparing coverage values across the two data sets.

o DHSs are not conducted primarily in women. There is a men’s questionnaire, a women’s questionnaire, and a household questionnaire. It appears the deworming question is asked in the women’s questionnaire, which I think may be what you meant. Please correct.

o Data analysis: you discuss using weighting to calculate estimated national-level coverage, but there is no discussion of district-level weighting to account for DHS survey design and actual nonresponse. Please specify whether weighting was used for district-level coverage estimates and whether it was your own or DHS weights. It seems unlikely the DHS participants in each district were perfectly representative of its general population.

o The term ‘deworming coverage’, as used throughout, is problematic without further qualification. It implies, like ‘vaccine coverage’, that at a given moment in time each individual in the country is either covered or not covered, when in truth people fall along a distribution of how long ago they were last dewormed. Deworming coverage is campaign-specific; it doesn’t make sense to talk about e.g. Burundi’s current national deworming coverage, but about its 2017 deworming campaign’s coverage – and even that only if the entire country did a campaign. More specific consistent terminology is needed. Please consider replacing throughout with something like ‘X year national deworming campaign coverage’, “Y year District Z deworming campaign coverage”.

o I’m not clear on why an area would possibly have more than one official deworming campaign within 6 months of the same DHS. The highest WHO-recommended deworming frequency is biannual (https://www.who.int/elena/titles/deworming/en/), in areas with baseline prevalence over 50%. If you truly found many such situations, this requires some further explanation

Reviewer #2: Methodology was found in order.

--------------------

Results

-Does the analysis presented match the analysis plan?

-Are the results clearly and completely presented?

-Are the figures (Tables, Images) of sufficient quality for clarity?

Reviewer #1: o School-age deworming programs are commonly done in schools. Please consider discussing the implications of this in light of gender-specific school attendance statistics. If these countries have significant gender gaps in school attendance but no gaps in deworming coverage, what is your interpretation of this? https://data.unicef.org/topic/education/primary-education/

o It’s not clear which dataset you’re attributing ‘recall bias’ from albendazole distribution by non-deworming programs to (although I think it’s misclassification bias, not recall bias, no?) Please specify- it seems more intuitive that maternally reported DHS data would be subject to this, not official district deworming records, so I’d expect that to have led to DHS data overestimating coverage, not the other way around. Unless you’re saying that you think mothers are systematically misclassifying deworming campaigns as LF campaigns? Would state this the first time you discuss this source of bias (in Methods) if so.

o Please fix ‘Center for Disease Control’ – it is ‘U.S. Centers for Disease Control and Prevention’.

o The discussion of maternal recall and recall bias seems problematic – the gestalt one gets is that mothers are being blamed for giving the ‘wrong’ answer, but their answers appear in the examples given to either not be wrong at all (correct knowledge that their own child got unprogrammed/unintended deworming) or be misclassification bias rather than recall bias. I suspect an individual mother’s recall of her child’s deworming status is likely to be much more accurate than official aggregate figures. Certainly in the vaccination world, surveys of individual immunization documentation yield far lower coverages than values reported to WHO (“administrative coverage”) and I think are considered more accurate.

o From what I can tell, MDAs for LF do not routinely include all pre-school-aged children (https://www.cdc.gov/mmwr/preview/mmwrhtml/mm6223a3.htm ; http://www.tropicalparasitology.org/article.asp?issn=2229-5070;year=2013;volume=3;issue=1;spage=67;epage=71;aulast=Ghosh – children under 2 years excluded). Please consider what effect this has on your calculations and interpretations.

o Not clear what your statement about ‘compatibility limitations’ in Myanmar means – are you just saying the DHS coverage estimates there had wide confidence intervals due to the small sample size and the WHO estimates fell within those, so you don’t know if the discrepancies are real? Would clarify.

o Please expand the final conclusion if possible. The reader has learned that in some countries, DHS and WHO coverage values were quite close, and in others they weren’t. Authors state that that means DHS data should perhaps be used going forward, but how exactly? Is your idea that if discrepancies are over a certain threshold, a more thorough quality review of the national reporting system is needed?

Reviewer #2: Results are clearly presented.

--------------------

Conclusions

-Are the conclusions supported by the data presented?

-Are the limitations of analysis clearly described?

-Do the authors discuss how these data can be helpful to advance our understanding of the topic under study?

-Is public health relevance addressed?

Reviewer #1: o School-age deworming programs are typically done in schools. Please consider discussing the implications of this in light of gender-specific school attendance statistics. If these countries have significant gender gaps in school attendance but no gaps in deworming coverage, what is your interpretation of this? https://data.unicef.org/topic/education/primary-education/

o Please fix ‘Center for Disease Control’ – it is ‘U.S. Centers for Disease Control and Prevention’.

Reviewer #2: Conclusions are appropriate, and limitations are highlighted. May I suggest a mention of variability in reporting resulting from a decentralized health system, where districts are largely left on their own to implement mass deworming strategy. Variable MDA coverage may well be used as part of advocacy in enhancing service delivery, outputs and outcomes of peripheral health units.

--------------------

Editorial and Data Presentation Modifications?

Use this section for editorial suggestions as well as relatively minor modifications of existing data that would enhance clarity. If the only modifications needed are minor and/or editorial, you may wish to recommend “Minor Revision” or “Accept”.

Reviewer #1: (No Response)

Reviewer #2: Minor revision in the discussion is recommended to include the comment made above.

--------------------

Summary and General Comments

Use this section to provide overall comments, discuss strengths/weaknesses of the study, novelty, significance, general execution and scholarship. You may also include additional comments for the author, including concerns about dual publication, research ethics, or publication ethics. If requesting major revision, please articulate the new experiments that are needed.

Reviewer #1: General: this is a useful, relevant paper for advancing determination of deworming coverage.

Abstract

- Would state explicitly what time frame national deworming coverage reported to WHO is intended to represent (ideally would be the past 6 months, for comparability to the maternal report question, but regardless readers will need some sense of how these two sources would be compared)

Introduction

- Optionally, authors may also wish to consider mentioning the neurodevelopmental effects of heavy STH infestation

- The basic issue authors are raising in the NTD world, of conflict between official coverage figures based on dose numbers administered (“administrative coverage”) and population-based survey results, has a long history in the world of immunization as well. Authors may find it useful to explore this and optionally mention parallels, since there is vast experience in that field with the issue. One link is https://www.who.int/immunization/monitoring_surveillance/routine/coverage/en/ . Also, the discussion of the advantages and disadvantages of each data source found here https://www.who.int/bulletin/volumes/87/7/08-053819/en/ may be useful.

- Use of terms

o Deworming is not a strategy for ‘eliminating’ the disease burden of STH – ‘elimination’ has specific public health definitions which I don’t believe is the goal of these programs. Perhaps ‘controlling’ / ‘minimizing’?

o Mass chemotherapy is not preventive – it’s intended to treat existing infestations in individuals

o WHO does not have an ongoing goal to achieve national coverage at any threshold in ‘all countries’ - please check WHO deworming recommendations. Probably ‘all endemic countries’ or ‘all highly endemic countries’ etc – maybe all countries above the baseline 20% prevalence threshold in the link that follows. Also, it may be important to clarify here that WHO recommendations for frequency of deworming (annual vs biannual) depend on the baseline prevalence (https://www.who.int/elena/titles/full_recommendations/deworming/en/

- Introduction should please make clear what this analysis provides that has not been done before. Have other studies sought to compare coverage from these two data sources, or other similar data sources, in other countries? Is this the first to do it on a subnational level? If, as implied in your discussion, this is the first attempt to triangulate using data already routinely collected for a different purpose rather than a purpose-built deworming coverage survey and you think its primary value is in the potential resources saved, would say that.

Reviewer #2: This paper is a highly relevant one and calls attention to the limitation of reported MDA coverage to WHO as basis for indicating progress of morbidity control in STH infections. The methods are found to be generally rigorous, and the results provide an excellent view of DHS data to complement the use of reported MDA data to WHO to help track progress.

--------------------

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Reviewer #1: No

Reviewer #2: No

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PLoS Negl Trop Dis. 2020 Aug 17;14(8):e0008551. doi: 10.1371/journal.pntd.0008551.r002

Author response to Decision Letter 0

22 Apr 2020

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Submitted filename: 2020 PNTD Response to Editor- Lo et al.docx

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PLoS Negl Trop Dis. doi: 10.1371/journal.pntd.0008551.r003

Decision Letter 1

David Joseph Diemert, Jennifer Keiser

12 May 2020

Dear Dr. Lo,

Thank you very much for submitting your manuscript "Comparison of World Health Organization and Demographic and Health Surveys data to estimate sub-national deworming coverage in pre-school aged children" for consideration at PLOS Neglected Tropical Diseases. As with all papers reviewed by the journal, your manuscript was reviewed by members of the editorial board and by several independent reviewers. The reviewers appreciated the attention to an important topic. Based on the reviews, we are likely to accept this manuscript for publication, providing that you modify the manuscript according to the review recommendations.

Please prepare and submit your revised manuscript within 30 days. If you anticipate any delay, please let us know the expected resubmission date by replying to this email.

When you are ready to resubmit, please upload the following:

[1] A letter containing a detailed list of your responses to all review comments, and a description of the changes you have made in the manuscript.

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[2] Two versions of the revised manuscript: one with either highlights or tracked changes denoting where the text has been changed; the other a clean version (uploaded as the manuscript file).

Important additional instructions are given below your reviewer comments.

Thank you again for your submission to our journal. We hope that our editorial process has been constructive so far, and we welcome your feedback at any time. Please don't hesitate to contact us if you have any questions or comments.

Sincerely,

David Joseph Diemert, M.D.

Associate Editor

PLOS Neglected Tropical Diseases

Jennifer Keiser

Deputy Editor

PLOS Neglected Tropical Diseases

***********************

Reviewer's Responses to Questions

Key Review Criteria Required for Acceptance?

As you describe the new analyses required for acceptance, please consider the following:

Methods

-Are the objectives of the study clearly articulated with a clear testable hypothesis stated?

-Is the study design appropriate to address the stated objectives?

-Is the population clearly described and appropriate for the hypothesis being tested?

-Is the sample size sufficient to ensure adequate power to address the hypothesis being tested?

-Were correct statistical analysis used to support conclusions?

-Are there concerns about ethical or regulatory requirements being met?

Reviewer #1: Methods, Overview: Despite the definition now provided for “recall period”, it remains confusing throughout, because the definition doesn’t distinguish between “how far back we asked the respondent to think back in answering the question” and “how far back the respondent actually thought in answering the question”, which is the critical issue. To make things harder, I believe the actual most common understanding of that phrase is neither, but is more like “how far back the respondent can reliably remember things”. Please revise the definition to clarify that (if I’m correct) you mean the first option. Perhaps consider whether you need to create distinct terms for this and the other plausible meaning(s) you use later in the ‘sensitivity analysis’ section. E.g. “requested recall period” vs “actual recall period” etc.

Methods, “Data source” – is the assumption that all deworming meds were received within the month of campaign initiation justified? I don’t know how long these campaigns typically last, but if you can provide any reference or data to support the expectation that they typically last less than one month, would briefly to so here so it doesn’t seem so arbitrary.

Data source: While inclusion criteria are clearer, we’re not all the way there yet. The “Data Source” paragraph does not answer: 1) Were countries included if they had matching DHS and WHO data for only part of the country? 2) Could a given district be included more than once if it had matched pairs of DHS and WHO data for two different years? (e.g. for one DHS and also its next DHS) .

Optionally, you could also consider additionally stating (since it appears this is what happened with the Philippines) something like: “Data on different subnational regions from a single national DHS could potentially be matched with WHO data from one national campaign in one region but the next national campaign (six months later) in another region, if the DHS data collection took several months to progress from the first region to the second.”

Also optionally, for maximal clarity, you might also consider reorganizing this paragraph to state first the country-level inclusion criteria already provided, then the district-level inclusion criteria, then the individual-level criteria (currently provided later in the Methods section).

Data source: Optionally, it would be of interest to tell us how many countries have deworming coverage data available from WHO, and how many from DHS. This would better contextualize the ultimate inclusion of only three.

Reviewer #2: Objectives and methods are clearly described. Statistical methods and analysis are well noted.

--------------------

Results

-Does the analysis presented match the analysis plan?

-Are the results clearly and completely presented?

-Are the figures (Tables, Images) of sufficient quality for clarity?

Reviewer #1: (No Response)

Reviewer #2: Results are clearly laid out and of sufficient clarity.

--------------------

Conclusions

-Are the conclusions supported by the data presented?

-Are the limitations of analysis clearly described?

-Do the authors discuss how these data can be helpful to advance our understanding of the topic under study?

-Is public health relevance addressed?

Reviewer #1: Discussion: “the difference in data compatibility” – confusing; I think you mean “different in coverage estimate values” or something.

Discussion: please briefly discuss the size and direction of bias potentially introduced by the individual-level DHS observation inclusion criteria – what were the response rates for these three DHSs? Did the DHS report authors record any concerns about their representativeness? Are households who were found by and agreed to participate in DHS likely/known to be different in key demographic ways from those not found/refusing? Would those e.g. have made them more likely to have also been “found” by deworming campaigns, maybe biasing both sets of estimates upward, or not?

Discussion: In the limitations, please briefly address the inclusion of only three countries. This seems inevitable, given you need a close time match and DHS happen only every 5 years, and you were somewhat lucky to get countries in three different regions, but are these three likely to be representative of the many other countries where deworming happens? Optionally, does this difficulty finding time matches suggest that perhaps STH campaigns could be deliberately timed to closely precede an upcoming DHSs in countries where MoHs want to use DHS data for coverage validation?

Biannual – I think you mean semiannual (twice per year, not once every two years).

Reviewer #2: Conclusions are well supported by data presented, and public health relevance of the study is included.

--------------------

Editorial and Data Presentation Modifications?

Reviewer #1: N/A

Reviewer #2: (No Response)

--------------------

Summary and General Comments

Reviewer #1: The authors have addressed most of my comments. I have a few additional comments, mostly based on the new information provided, some relating to previous issues that are not completely addressed yet.

General: Please read over for grammar, syntax and precise meaning. E.g. constructions like in the abstract, “the progress of deworming programs to control” (should be “in controlling”). Or in the abstract conclusion section, “deworming coverage…was heterogeneous across countries”. I think you mean something more like “Matching between deworming coverages reported to WHO…was heterogeneous…” Or in the introduction, “There is growing interest to utilize” – should be “in utilizing”. Or in the Discussion: “our analysis found coverage differences…may be compatible”. (The differences aren’t compatible; perhaps the estimates are compatible or comparable.) Etc.

Introduction (and wherever else this term appears): “prior” infections – perhaps “existing” instead? Deworming is not treating infections that happened in the past and have resolved, but rather current ongoing infections.

Introduction: “the key metric…remains national treatment coverage” – perhaps ‘national campaign coverage”, since the prior paragraph was all about the campaigns and you’re referring to them here? Otherwise could be misunderstood as newly introducing the idea of clinical treatment based on symptoms.

Reviewer #2: Generally an acceptable paper for dissemination.

--------------------

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Reviewer #2: No

Figure Files:

Data Requirements:

Reproducibility:

To enhance the reproducibility of your results, PLOS recommends that you deposit laboratory protocols in protocols.io, where a protocol can be assigned its own identifier (DOI) such that it can be cited independently in the future. For instructions see http://journals.plos.org/plosntds/s/submission-guidelines#loc-materials-and-methods

PLoS Negl Trop Dis. 2020 Aug 17;14(8):e0008551. doi: 10.1371/journal.pntd.0008551.r004

Author response to Decision Letter 1

30 Jun 2020

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PLoS Negl Trop Dis. doi: 10.1371/journal.pntd.0008551.r005

Decision Letter 2

David Joseph Diemert, Jennifer Keiser

1 Jul 2020

Dear Dr. Lo,

We are pleased to inform you that your manuscript 'Comparison of World Health Organization and Demographic and Health Surveys data to estimate sub-national deworming coverage in pre-school aged children' has been provisionally accepted for publication in PLOS Neglected Tropical Diseases.

Before your manuscript can be formally accepted you will need to complete some formatting changes, which you will receive in a follow up email. A member of our team will be in touch with a set of requests.

Please note that your manuscript will not be scheduled for publication until you have made the required changes, so a swift response is appreciated.

IMPORTANT: The editorial review process is now complete. PLOS will only permit corrections to spelling, formatting or significant scientific errors from this point onwards. Requests for major changes, or any which affect the scientific understanding of your work, will cause delays to the publication date of your manuscript.

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Thank you again for supporting Open Access publishing; we are looking forward to publishing your work in PLOS Neglected Tropical Diseases.

Best regards,

David Joseph Diemert, M.D.

Associate Editor

PLOS Neglected Tropical Diseases

Jennifer Keiser

Deputy Editor

PLOS Neglected Tropical Diseases

***********************************************************

PLoS Negl Trop Dis. doi: 10.1371/journal.pntd.0008551.r006

Acceptance letter

David Joseph Diemert, Jennifer Keiser

10 Aug 2020

Dear Dr. Lo,

We are delighted to inform you that your manuscript, "Comparison of World Health Organization and Demographic and Health Surveys data to estimate sub-national deworming coverage in pre-school aged children," has been formally accepted for publication in PLOS Neglected Tropical Diseases.

We have now passed your article onto the PLOS Production Department who will complete the rest of the publication process. All authors will receive a confirmation email upon publication.

The corresponding author will soon be receiving a typeset proof for review, to ensure errors have not been introduced during production. Please review the PDF proof of your manuscript carefully, as this is the last chance to correct any scientific or type-setting errors. Please note that major changes, or those which affect the scientific understanding of the work, will likely cause delays to the publication date of your manuscript. Note: Proofs for Front Matter articles (Editorial, Viewpoint, Symposium, Review, etc...) are generated on a different schedule and may not be made available as quickly.

Soon after your final files are uploaded, the early version of your manuscript will be published online unless you opted out of this process. The date of the early version will be your article's publication date. The final article will be published to the same URL, and all versions of the paper will be accessible to readers.

Thank you again for supporting open-access publishing; we are looking forward to publishing your work in PLOS Neglected Tropical Diseases.

Best regards,

Shaden Kamhawi

co-Editor-in-Chief

PLOS Neglected Tropical Diseases

Paul Brindley

co-Editor-in-Chief

PLOS Neglected Tropical Diseases

Associated Data

This section collects any data citations, data availability statements, or supplementary materials included in this article.

Supplementary Materials

S1 Checklist. STROBE Checklist.

(PDF)

Click here for additional data file.^{(532.2KB, pdf)}

(DOCX)

Click here for additional data file.^{(92.8KB, docx)}

S1 Table. District-level deworming coverage in pre-school aged children using data reported to WHO and estimated by DHS in Burundi under base case analysis.

(DOCX)

Click here for additional data file.^{(16.2KB, docx)}

S2 Table. District-level deworming coverage in pre-school aged children using data reported to WHO and estimated by DHS in Myanmar under base case analysis.

(DOCX)

Click here for additional data file.^{(15.8KB, docx)}

S3 Table. District-level deworming coverage in children 2–4 years of age using data reported to WHO and estimated by DHS in the Philippines under base case analysis.

(DOCX)

Click here for additional data file.^{(16.2KB, docx)}

(DOCX)

Click here for additional data file.^{(15.8KB, docx)}

S1 Fig. District-level sample size of DHS observations used to estimate deworming coverage in study countries, varying maternal recall period.

(DOCX)

Click here for additional data file.^{(1.7MB, docx)}

S2 Fig. Correlation between district-level deworming coverage in pre-school aged children reported to WHO and estimated using DHS data for study countries.

(DOCX)

Click here for additional data file.^{(953.2KB, docx)}

S3 Fig. Comparison of district-level deworming coverage in pre-school aged children reported to WHO and estimated by DHS in study countries, varying maternal recall periods.

We obtained WHO data on district-level deworming coverage reported by national Ministries of Health based on health facility records. We estimated district-level deworming coverage with DHS data corresponding to the known WHO deworming campaigns using maternal-reported data on child deworming receipt. The comparison of deworming coverage using WHO and DHS data is shown with varying maternal recall period from 4 months to 8 months in Burundi (panel A-B), Myanmar (panel C-D), and the Philippines (panel E-F).

(DOCX)

Click here for additional data file.^{(1.8MB, docx)}

S4 Fig. Comparison of district-level deworming coverage in pre-school aged children reported to WHO and estimated by DHS in study countries, varying threshold for data reliability.

We obtained WHO data on district-level deworming coverage reported by national Ministries of Health based on health facility records. We estimated district-level deworming coverage with DHS data corresponding to the known WHO deworming campaigns using maternal-reported data on child deworming receipt. The comparison of deworming coverage using WHO and DHS data is shown with a varying data reliability threshold (base case of 95%) from > 90% to no threshold to change the districts meeting inclusion criteria in Burundi (panel A-B), Myanmar (panel C-D), and the Philippines (panel E-F).

(DOCX)

Click here for additional data file.^{(1.5MB, docx)}

S5 Fig. Comparison of district-level deworming coverage in pre-school aged children reported to WHO and estimated by DHS in study countries, varying the treatment correlation parameter.

We obtained WHO data on district-level deworming coverage reported by national Ministries of Health based on health facility records. We estimated district-level deworming coverage with DHS data corresponding to the known WHO deworming campaigns using maternal-reported data on child deworming receipt. The comparison of deworming coverage using WHO and DHS data for Burundi is shown with a varying treatment correlation (base case of 67.8%) value from 70% (panel A) to 100% (panel B).

(DOCX)

Click here for additional data file.^{(706.2KB, docx)}

We obtained WHO data on district-level deworming coverage reported by national Ministries of Health based on health facility records. We estimated district-level deworming coverage with DHS data corresponding to the known WHO deworming campaigns using maternal-reported data on child deworming receipt. The comparison of deworming coverage using WHO and DHS data is shown with varying receipt status for all “maybe” DHS responses (base case of +0.5) from +0 and +1 in Burundi (panel A-B), Myanmar (panel C-D), and the Philippines (panel E-F).

(DOCX)

Click here for additional data file.^{(1.8MB, docx)}

(DOCX)

Click here for additional data file.^{(437.5KB, docx)}

Attachment

Submitted filename: 2020 PNTD Response to Editor- Lo et al.docx

Click here for additional data file.^{(55KB, docx)}

Attachment

Submitted filename: Lo- PNTD Response to Editor R2.docx

Click here for additional data file.^{(39.2KB, docx)}

Data Availability Statement

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PERMALINK

Comparison of World Health Organization and Demographic and Health Surveys data to estimate sub-national deworming coverage in pre-school aged children

Nathan C Lo

Ribhav Gupta

David G Addiss

Eran Bendavid

Sam Heft-Neal

Alexei Mikhailov

Antonio Montresor

Pamela Sabina Mbabazi

Roles

Abstract

Background

Methodology

Principal findings

Conclusions and significance

Author summary

Introduction

Methods

Overview

Data source

Statistical analysis

Sensitivity analyses

Results

Descriptive results

Fig 1. Timeline of national deworming campaigns reported to WHO for pre-school aged children and DHS data collection periods in study countries.

Fig 2. Inclusion and exclusion criteria for DHS and WHO deworming data of pre-school aged children for study countries.

Fig 3. District-level sample size of DHS observations used to estimate deworming coverage in study countries.

Main analysis

Fig 4. Comparison of district-level deworming coverage in pre-school aged children reported to WHO and estimated by DHS data for study countries.

Fig 5. Comparison of national-level deworming coverage amongst girls and boys of pre-school age estimated by DHS data in study countries.

Sensitivity analyses

Discussion

Supporting information

Data Availability

Funding Statement

References

Decision Letter 0

David Joseph Diemert

Jennifer Keiser

Roles

Author response to Decision Letter 0

Decision Letter 1

David Joseph Diemert

Jennifer Keiser

Roles

Author response to Decision Letter 1

Decision Letter 2

David Joseph Diemert

Jennifer Keiser

Roles

Acceptance letter

David Joseph Diemert

Jennifer Keiser

Roles

Associated Data

Supplementary Materials

Data Availability Statement

ACTIONS

PERMALINK

RESOURCES

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