Abstract
Data on population health are vital to evidence-based decision making but are rarely adequately localized or updated in continuous time. They also suffer from low ascertainment rates, particularly in rural areas where barriers to healthcare can cause infrequent touch points with the health system. Here, we demonstrate a novel statistical method to estimate the incidence of endemic diseases at the community level from passive surveillance data collected at primary health centers. The zerocorrected, gravity-model (ZERO-G) estimator explicitly models sampling intensity as a function of health facility characteristics and statistically accounts for extremely low rates of ascertainment. The result is a standardized, real-time estimate of disease incidence at a spatial resolution nearly ten times finer than typically reported by facility-based passive surveillance systems. We assessed the robustness of this method by applying it to a case study of field-collected malaria incidence rates from a rural health district in southeastern Madagascar. The ZERO-G estimator decreased geographic and financial bias in the dataset by over 90% and doubled the agreement rate between spatial patterns in malaria incidence and incidence estimates derived from prevalence surveys. The ZERO-G estimator is a promising method for adjusting passive surveillance data of common, endemic diseases, increasing the availability of continuously updated, high quality surveillance datasets at the community scale. Health metrics are vital to public health efforts, allowing decision makers to better understand the state of population health and evaluate the impact of health interventions 1,2. Many of these metrics are based on routine passive disease surveillance from facility-based health management information systems (HMIS), which record the number of disease cases received at each facility at a regular frequency. Health records are then aggregated, digitized, and transferred to the district and, eventually, national health offices 3. While the exact structure differs by country, the scale of spatial aggregation of the data in an HMIS corresponds to the specific level of the health system and its corresponding health infrastructure. For example, national-level data are used by international organizations to monitor long-term, multi-country trends and inform policy; regional-and district-level surveillance data may be used by national public health offices to allocate resources within the country; and individual health facility information is used by district health offices for program management.