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Abstract Decentralization reform has both advantages and risks. Bringing service delivery “closer to the people” can improve information flows and strengthen accountability, but it may also leave systems vulnerable to elite capture and corruption by municipal government officials. While past research has acknowledged the possibility of corruption under decentralization, relatively little work has connected those risks to features of these reforms or specific local institutional arrangements. To explore the conditions that can help mitigate the risks of corruption under decentralization, we study the case of health sector reform in Honduras where municipal governments, associations, and NGOs each serve as intermediary-managing organizations under a common decentralized health service delivery model. We argue that three types of institutional arrangements reflecting local accountability practices serve as checks on the authority granted through decentralization and can help guard against corruption: external supervision, civil society engagement, and public participation. Empirically, we draw on data from more than 600 street-level bureaucrats, valuable but under-utilized informants about municipal corruption, across a matched sample of 65 municipalities with contrasting forms of administration. We find that reported corruption is highest under decentralization led by municipal governments, as compared to association- or NGO-led varieties. Both external supervision and civil society engagement help attenuate the positive association between decentralization and corruption, but public participation does not. Overall, this research highlights the importance of considering reform features and local conditions when designing policies to help manage risks and support effective social sector decentralization. 

Public health agencies routinely collect time-referenced records to describe and compare foodborne outbreak characteristics. Few studies provide comprehensive metadata to inform researchers of data limitations prior to conducting statistical modeling. We described the completeness of 103 variables for 22,792 outbreaks publicly reported by the United States Centers for Disease Control and Prevention’s (US CDC’s) electronic Foodborne Outbreak Reporting System (eFORS) and National Outbreak Reporting System (NORS). We compared monthly trends of completeness during eFORS (1998–2008) and NORS (2009–2019) reporting periods using segmented time series analyses adjusted for seasonality. We quantified the overall, annual, and monthly completeness as the percentage of outbreaks with blank records per our study period, calendar year, and study month, respectively. We found that outbreaks of unknown genus (n = 7401), Norovirus (n = 6414), Salmonella (n = 2872), Clostridium (n = 944), and multiple genera (n = 779) accounted for 80.77% of all outbreaks. However, crude completeness ranged from 46.06% to 60.19% across the 103 variables assessed. Variables with the lowest crude completeness (ranging 3.32–6.98%) included pathogen, specimen etiological testing, and secondary transmission traceback information. Variables with low (<35%) average monthly completeness during eFORS increased by 0.33–0.40%/month after transitioning to NORS, most likely due to the expansion of surveillance capacity and coverage within the new reporting system. Examining completeness metrics in outbreak surveillance systems provides essential information on the availability of data for public reuse. These metadata offer important insights for public health statisticians and modelers to precisely monitor and track the geographic spread, event duration, and illness intensity of foodborne outbreaks.