# Socioeconomic drivers of human Brucellosis in Ningxia, China: A one health and spatiotemporal analysis for targeted intervention

## TL;DR

This study identifies socioeconomic and livestock factors driving brucellosis in Ningxia, China, showing how targeted interventions can reduce outbreaks.

## Contribution

The study introduces a dual-strategy approach combining immediate economic development and long-term veterinary measures for brucellosis control.

## Key findings

- Brucellosis incidence in Ningxia increased 167-fold from 2007 to 2022.
- Sheep and cattle inventory were the strongest spatial drivers of brucellosis risk.
- Higher GDP was immediately linked to lower brucellosis risk, while cattle stocking showed a 3-year lagged effect.

## Abstract

This study aimed to investigate the spatiotemporal heterogeneity of human brucellosis and quantify the exposure-lag-response relationships of key socioeconomic and livestock production drivers in Ningxia, China, from 2007 to 2022. The goal was to generate evidence for developing targeted, integrated interventions in this high-burden pastoral region.

We conducted a retrospective ecological study integrating human brucellosis surveillance data with county-level socioeconomic and livestock production statistics. A multi-analytic framework was employed: Joinpoint regression analyzed long-term trends; spatiotemporal scan statistics identified high-risk clusters; GeoDetector quantified the explanatory power of potential drivers on spatial heterogeneity; and Distributed Lag Nonlinear Models (DLNMs) were constructed to assess the nonlinear and lagged effects of significant drivers on monthly incidence.

The human brucellosis incidence rate in Ningxia increased 167-fold, from 0.52 to 86.83 per 100,000 population between 2007 and 2022. Spatiotemporal analysis revealed a persistent high-risk cluster (Relative Risk, RR = 4.22, P < 0.001) in 11 eastern counties. GeoDetector identified livestock-related factors as primary spatial drivers, with sheep inventory (q = 0.96) and cattle inventory (q = 0.92) showing the highest explanatory power. DLNM results indicated a significant 3-year lagged risk associated with low cattle stocking levels (RR = 2.75), while sheep stocking exhibited a complex, non-linear U-shaped lag effect. In contrast, higher regional Gross Domestic Product (GDP) was associated with an immediate lower risk (RR = 0.81).

The brucellosis epidemic in Ningxia is characterized by intense spatial clustering and is associated with distinct, lagged effects of livestock production structures coupled with immediate economic influences. The findings underscore that livestock production metrics can serve as effective proxies for risk mapping even in the absence of direct animal infection data. Our study highlights the necessity for a dual-strategy intervention: implementing risk-based veterinary public health measures in high-incidence clusters while leveraging economic development to strengthen long-term prevention and control capacities.

Brucellosis is a zoonotic disease that spreads from animals to humans, posing a significant public health threat, especially in pastoral regions like Ningxia, China. Understanding the complex drivers behind its resurgence is critical for designing effective control measures. In this study, we analyzed human brucellosis cases in Ningxia from 2007 to 2022 using an integrated approach that combines spatial mapping, statistical detection of driving factors, and time-lag modeling. We found that the epidemic intensified dramatically and became concentrated in eastern counties. The number of sheep and cattle was the strongest factor determining where cases occurred. Our models revealed that risks operate on different timelines: higher economic development was immediately associated with lower risk, likely by enabling better prevention. In contrast, a higher risk was observed three years after an increase in cattle numbers in low-stock areas, suggesting a delayed spillover from smallholder farms where infections may go undetected. This lag provides a crucial window for early intervention. Our study demonstrates that routinely collected livestock and economic data can serve as powerful proxies to map disease risk and forecast outbreaks, even in the absence of comprehensive animal testing. The results argue for targeted, dual-track strategies: intensifying animal vaccination in high-risk clusters while using economic incentives to improve farm biosecurity and launching early warnings based on livestock trends to prevent future surges.