Background
The Global Polio Eradication Initiative (GPEI) has seen great success since its launch in 1988. At the time of this writing, only Pakistan, Afghanistan, and Nigeria remain endemic for polio with only 37 cases of wild poliovirus serotype 1 (WPV1) recorded in 2016. The 20 cases reported in Pakistan in 2016 represent a historically low case count for a calendar year and a 63% reduction in cases compared to 2015. However, transmission is still occurring on a considerable geographic scale, with four of eight provinces reporting WPV1 cases in 2016. Pakistan has approximately 25 million children under the age of 5 years [
1], which presents challenges for allocating limited resources. As the program approaches the goal of zero cases, identifying the districts that are most likely to be infected and prioritizing those districts for interventions is a priority of the program and should accelerate the path towards eradication.
In the Eastern Mediterranean Region of the World Health Organization (WHO) key strategies of the of GPEI included (1) achieving high coverage of at least three doses of oral polio vaccine (OPV), (2) implementation of supplementary immunization activities (SIAs), and (3) the development of sensitive epidemiological and laboratory surveillance using standard WHO definitions [
2]. Eradication efforts began in Pakistan in 1994, when the first SIA was conducted, and in 1995, when acute flaccid paralysis (AFP) surveillance commenced [
3]. The case burden in Pakistan has dramatically decreased since the 1990s, but sustained implementation of the first two GPEI strategies has been challenging due to poor rates of routine immunization (RI) and security issues. As of 2012, only 53% of children in Pakistan were receiving all basic vaccines, including Bacillus Calmette–Guerin, measles and three doses of polio and diphtheria, pertussis, and tetanus, with provincial rates as low as 16% and 29% in Balochistan and Sindh provinces, respectively [
4]. Additionally, vaccination bans and security limitations in the Federally Administered Tribal Areas (FATA) and Khyber Pakhtunkhwa (KP) and violence against vaccinators in FATA, Balochistan, and Sindh provinces have periodically limited the program’s efforts to consistently implement SIAs with high population coverage in conflict-affected areas since 2008 [
5]. These programmatic challenges have resulted in pockets of underimmunized (fewer than three OPV doses) children and have allowed transmission to persist.
Since 2011, the Pakistan program has been implementing and enhancing a National Emergency Action Plan (NEAP) for polio eradication to improve management and accountability strategies, highlight core reservoirs of transmission, and to ensure the program is creating and using high quality data [
5,
6]. To aid in the prioritization of sub-national areas for programmatic interventions, we developed a spatial model to estimate the risk of future WPV1 cases for the 155 districts of Pakistan. Previous studies highlight the utility of spatial risk models for guiding programmatic interventions for polio, such as the 86% accuracy for predicting districts at risk for future WPV1 cases in Nigeria [
7]. These models guided the prioritization of sub-national areas for immunization planning and allocation of technical and administrative field personnel. The use of a spatial risk model, which is statistically evaluated based on its accuracy for predicting locations of cases, represents a methodological departure from the common approach of compiling programmatic indicators of disease risk, assigning weights based on expert opinion, and linearly combining into a risk score [
8,
9]. Unfortunately, a spatial risk model has not previously been applied to model the risk of WPV1 in Pakistan. In this paper, we will describe our efforts to model the risk of future WPV1 cases in districts of Pakistan and describe how these efforts have been incorporated by the National Emergency Operating Centre (N-EOC) in Islamabad to the 2016–2017 NEAP to prioritize the districts of Pakistan.
Discussion
Our results indicate that seasonality, immunity, underimmunization rate, recent cases, and recent cases in a neighboring district are most predictive of at least one WPV1 case. The number of cases given at least one case is similarly predicted by seasonality, underimmunization rate, zero RI dose rate, recent cases, and recent neighboring cases. The point estimates of all associations were in the expected direction of lower immunity and dose history as well as recent cases being associated with increased risk of cases and larger outbreaks.
Our modeling efforts suggest that the large outbreaks in 2014 and the recent improvements over the past 2 years can be described by population immunity driven primarily by SIAs in FATA and KP provinces. For example, the dramatic decrease in serotypes 1 and 2 immunities in KP beginning in 2010 were partially the result of declining SIA coverage rates (despite the high frequency of campaigns). Furthermore, type 2 immunity declined due to infrequent trivalent OPV campaigns, whereas type 1 immunity declined because SIAs were primarily using bivalent and trivalent OPV, which has a lower efficacy for type 1 than monovalent OPV type 1 vaccine. Finally, improvements beginning in 2015 were due to improved vaccinator access driven by military intervention in FATA [
33].
Based on the recommendations in the 2016–2017 NEAP, finalized in May of 2016, Tier 1–3 districts participated in four bOPV SIAs in addition to the five national bOPV SIAs that covered all districts. Additionally, Tier 1 districts scaled up the community-based vaccination strategy, which employs local individuals, primarily women who are thought to have better access to children within homes, as permanent vaccinators within their communities. In the 6-month period between July and December, 2016, only two of the 44 Tier 1 and 2 districts experienced cases (one each), which reflects well on the efforts focused on those districts. Two of the three other districts, which reported cases during this time frame, were classified as Tier 4 districts (four of five cases in Tier 4), although they were ranked in the top 30 per the risk model; the remaining Tier 4 district, in northern KP province, would be considered relatively surprising from a modeling and programmatic perspective. We emphasize that even Tier 4 districts received considerable programmatic attention, with five planned SIAs across 2016.
Our approach does have several limitations. We have developed a model on a 6-month time scale, which is programmatically relevant but does not align with the approximately 1 month infectious period estimated for poliovirus [
34]. Additionally, we have modeled observed WPV1 paralytic cases that only represent approximately 0.5% of WPV1 infections [
35]. This absence of cases could be misleading if circulation is silent due to surveillance failure, waned mucosal immunity among older children or adults [
36,
37] or, as observed in Israel, high rates of humoral immunity due to exclusive inactivated polio vaccine use leading to low mucosal immunity [
38]. Finally, areas that are not explained well by the covariates will have large residual risk captured by the random effects and, as these are invariant in time, we will likely overestimate risk in areas with a long history of WPV1 cases, despite improvements in indicators.
In 2009, the Pakistan program initiated environmental surveillance (ES) to compliment AFP surveillance. Since 2009, the program has grown from 47 samples across 6 sites to 648 samples across 62 sites covering 33 districts in 2016 [
14,
39]. Due to the selective and expanding deployment of ES over time and the unique interpretation of ES positives, which signify at least one infection, ES data it is not easily included as a predictor in our currently modeling framework. Similarly, as sampling locations have been selected based on a history of persistent infection [
39], we would expect positive samples to primarily reinforce the high-risk status of areas with a history of cases if included as an outcome. It is a limitation of our case-based statistical model that there is not a straightforward way to incorporate the ES data as either a predictor or an outcome. Alternative approaches, such as a transmission model that includes genetic information or a statistical model that incorporates the relative sensitivity of ES and AFP, may be better suited to incorporate the ES data. However, although it is not explicitly included in the risk model for WPV1 cases, ES is used extensively within the program to identify infected areas when transmission is low and is essential for assessing progress towards eradication.
Despite the limitations, our modeling approach provides a principled framework for ranking districts for risk classification that performs well as measured by AUC and sensitivity. In practice, the risk analysis generally identifies the same Tier 1 districts as the aggregation of programmatic knowledge and scientific intuition of the N-EOC members and the greatest impact of the modeling approach is the promotion of districts that appear susceptible based on immunity profile, but have not yet had WPV1 cases, to a lower (higher risk) tier. These promotions impacted the allocation of resources by deploying the community-based vaccination strategy in additional districts and broadening the geographic scope of the four sub-national SIAs. Additionally, the method provides a metric for quantifying the absolute risk and changes in risk over time, which is not always captured well by cases or intuition exclusively.
Acknowledgements
The authors would like to thank Bill and Melinda Gates for their sponsorship through the Global Good Fund. We would also like to thank all those in Pakistan involved in the AFP surveillance and laboratory testing.