Assessing three-decade HIV testing transformation: TOPSIS-radar evaluation of tiered health system performance in rural–urban Southwestern China

Abstract

Background

China's expanded HIV testing program (since 2015) necessitates longitudinal evaluation of healthcare institution (HCI) performance, which provides critical evidence to enhance the precision and effectiveness of focused testing interventions, to optimize service delivery.

Methods

We analyzed HIV surveillance data from 202 HCIs in Nanning, China (1989–2020), characterizing: 1) HCI infrastructure distribution, 2) newly reported HIV/AIDS cases and testing volumes, and 3) test positivity rates. Spatiotemporal trends were geo-visualized (ArcGIS 10.7). TOPSIS synthesized case reports and test positivity rates (2010–2020) into composite indices, with radar mapping identifying regional disparities.

Results

Reporting shifted from CDC-dominated systems (99.9%, 1989–2004) to hospital-led models (75.6%, 2015–2020). While annual testing volume and case reports increased significantly, test positivity rates declined. Distinct hospital-level stratification emerged: County/township HCIs served primarily local residents (96.2%) and older patients (≥ 50 years; 65.7%). Municipal/provincial HCIs reported higher non-local residents (48.6%) and younger patients (15–49 years; 53.4%). Geospatial diffusion showed progression from urban to rural areas. Regions with integrated AIDS treatment centers and robust primary care networks demonstrated enhanced case-finding performance.

Conclusions

Tiered hospitals and primary care centers may provide complementary case-finding functions. Geographically stratified interventions, which leveraged primary networks for localized epidemics and referral hospitals for mobile populations, represent evidence-based strategies for regions with suboptimal performance.

Supplementary Information

The online version contains supplementary material available at 10.1186/s12889-025-25649-x.

Background

Ending AIDS epidemic represents a critical global health imperative. Achieving the 95–95-95 targets by 2030 requires delivering HIV prevention services to priority populations [1, 2]. Globally, expanded HIV testing is recognized as the foundational intervention for case identification, with demonstrable preventive benefits at the population level [3, 4]. This evidence underscores the strategic imperative to optimize testing programs for maximal case-finding yield.

In response to this global imperative, international guidelines since 2015 have prioritized expanded testing implementation [5]. China has actively integrated this approach into its national HIV prevention strategy, notably within the 12th Five-Year Plan (2010–2015) and the 13th Five-Year Plan (2015–2020) [6]. Significant research efforts in China have focused on characterizing the epidemic and evaluating testing strategies, revealing substantial regional heterogeneity in program performance and outcomes [7]. The epidemic exhibits marked geographical concentration, with southwestern China bearing a disproportionately high burden.

Guangxi Zhuang Autonomous Region (Guangxi), a southwestern province bordering Vietnam, exemplifies this concentrated epidemic. It consistently ranks among China's highest in reported HIV/AIDS cases, incidence rates, AIDS incidence, and HIV/AIDS-related mortality [8–10]. A critical challenge identified in Guangxi, and reflected in broader Chinese research, is the persistently high rate of late diagnosis [11]. To address this, the Guangxi government implemented comprehensive provincial AIDS prevention and control programs (Phase 1: 2010–2015, Phase 2: 2015–2020). These programs featured government-led, multi-sectoral interventions explicitly incorporating expanded HIV testing as a core strategy to curb transmission [6]. Consequently, rigorous post-implementation evaluation of testing program performance is needed to inform resource allocation resource allocation and maximizing case-finding efficiency within existing constraints. While national and regional evaluations exist, significant knowledge gaps persist in the Chinese context, particularly concerning Guangxi: 1) limited evidence on multidimensional assessment integrating diverse performance indicators, 2) insufficient understanding of the drivers behind significant regional heterogeneity in program performance [7], 3) methodological challenges in conducting robust comparative evaluations of performance across diverse healthcare institutions (HCIs) and over extended time periods.

To address these critical gaps, the Technique for Order Preference by Similarity to Ideal Solution (TOPSIS) was selected as one applicable method for multidimensional evaluation. TOPSIS is a dynamic multi-criteria decision analysis (MCDA) model enabling the simultaneous evaluation of multiple performance indicators [12]. It identifies optimal solutions by calculating minimal Euclidean distance to the positive ideal solution (optimal benchmark) and maximal distance from the negative ideal solution (suboptimal benchmark) [13]. Although TOPSIS has been extensively validated in diverse healthcare evaluations globally (e.g., hospital performance [14], diagnostic capacity [15], and disaster preparedness [16]), its application in evaluating large-scale HIV testing programs, particularly in China, remains limited. To overcome inherent visualization limitations of TOPSIS, we integrated radar chart mapping [17], enabling enhanced multidimensional performance gap analysis across both spatial and temporal dimensions.

Nanning, the capital and most populous city of Guangxi (approximately 7.9 million residents/Guangxi total ~ 57 million), was selected as the study site. This choice is grounded in its profound epidemiological significance: Nanning has exhibited the highest regional HIV burden since the index case in Guangxi was reported (1989) [6] and hosts the province's most comprehensive HCI network, spanning 15 administrative regions. Therefore, this study aimed to: (1) characterize spatiotemporal patterns in HCIs, regionally-reported HIV cases, testing volumes, and test positivity rates, and (2) evaluate the performance of the expanded HIV testing program in Nanning through a novel multidimensional assessment framework. While this study focuses on Nanning, its design intentionally captures systemic tensions universal to decentralized HIV testing, such as hospital-PHC, urban–rural resource gradients, and aging epidemics. The findings provide recommendable experience to inform precision resource allocation towards underperforming regions and advance equitable progress towards achieving the first 95 target.

Methods

Study setting

This retrospective analysis utilized electronic records from 202 HCIs in Nanning, China (1989–2020), sourced from the Comprehensive Response Information Management System (CRIMS). Institutions were stratified through a dual classification framework: 1) Organizational hierarchy (hospitals, center for disease control and prevention (CDC), specialized institutions), and 2) Administrative level (provincial, municipal, county, township/community). The study comprised 180 hospitals (26 provincial, 30 municipal, 21 county, 98 township, 5 community health centers), 15 CDC (1 provincial, 1 municipal, 13 county/district), and 7 specialized institutions (5 blood transfusion services, 1 border quarantine facility, 1 provincial dermatology prevention center).

Data collection

Data were extracted from Nanning's Comprehensive Response Information Management System (CRIMS) database. From 67,109 initial entries, we applied inclusion criteria requiring: 1) laboratory-confirmed HIV positivity (ELISA with Western Blot verification), 2) definitive verification by Centers for Disease Control (CDC) authorities, and 3) reported diagnosis within Nanning City. After de-duplication through CDC identity verification, 41,047 unique cases were retained. Standardized definitions included: 1) Middle and older adults (MOs, ≥ 50 years at diagnosis), and 2) Current residence (most recent residential address documented during follow-up services).

Annual HIV testing data (2010–2020) were sourced through stratified surveillance systems: Nanning-specific metrics originated from CRIMS, while provincial (Guangxi) and national (China) aggregates were extracted from the Chinese Center for Disease Control and Prevention's official repository (https://www.chinacdc.cn). Healthcare institution (HCI) metadata were obtained from the Guangxi Health Statistical Yearbook 2021 edition.

Statistical analysis

Descriptive analysis

HIV test program performance evaluated system efficiency through HCIs distribution hierarchies, testing volumes, case-finding yield (newly reported cases), and resource-yield alignment [HIV test positivity rates = (number of HIV-positive tests/total number of HIV tests performed) × 100%]. Descriptive analyses characterized temporal variations. These indicators were stratified across four policy-defined epochs (1989–2004, 2005–2009, 2010–2014, 2015–2020) reflecting national and provincial HIV program evolution. All analyses were performed using SPSS Statistics 22.0 (IBM Corp., Armonk, NY, USA).

Annual spatial distributions (2010–2020) were mapped at the county/district level using ArcGIS 10.7 (ESRI, Redlands, CA). Administrative boundaries were derived from a 1:1,000,000 vector map of Nanning's 15 administrative zones (Chinese Academy of Sciences Resource and Environment Data Cloud Platform, http://www.resdc.cn), projected in GCS_WGS_1984. Healthcare institution (HCI) locations were geocoded from address data using Baidu Maps Coordinate Retrieval System (http://api.map.baidu.com/lbsapi/getpoint), with resulting GPS coordinates plotted as point features.

Comprehensive assessment

We implemented the Technique for Order Preference by Similarity to Ideal Solution (TOPSIS) to evaluate multidimensional testing program performance across 15 regions (2010–2020), integrating two core indicators: annual newly reported cases and HIV test positivity rates. This multicriteria decision analysis method identifies optimal solutions through Euclidean distance minimization to the positive ideal solution (PIS—maximizing benefit criteria) while maximizing distance from the negative ideal solution (NIS—minimizing cost criteria) [18]. Resultant normalized values informed radar chart visualizations that quantitatively mapped regional performance strengths and limitations across 22 dimensional indicators. The specific steps for combining TOPSIS and the radar chart are as follows:

Step 1: To ensure all indicators exhibited a uniform directionality (homogenization), their attribute types were assessed. In this study, both the number of new reported case and HIV test positivity rates were identified as positive indicators (where higher values signify better performance), inherently aligning with the desired trend. Consequently, directional transformation was unnecessary. The normalization procedure employed vector normalization to eliminate dimensional differences, calculated as follows:

where represents the normalized value of the j^th indicator (j = 1,2,…,m) for the i^th region (i = 1,2,…,n). a_ij is the original value of the j^th indicator for the i^th region. n = 15 denotes the number of evaluated regions, m = 22 represents the total number of evaluation indicators [19].

Step 2: the positive ideal solution (PIS,) and negative ideal solution (NIS, ) were determined following matrix normalization. The PIS represents the optimal performance scenario and was defined as the vector containing the maximum observed value for each indicator across all evaluated regions. Conversely, the NIS represents the least desirable performance and comprises the minimum observed value for each indicator. These solutions are mathematically expressed as:

For the 22 indicators in this study, the PIS and NIS were derived as:

Step 3: he Euclidean distances from each alternative (region) to the positive ideal solution (PIS,) and negative ideal solution (NIS, ) were computed using weighted Euclidean norms. For region i, the separation measures were calculated as:

where and represent the distances to PIS and NIS, respectively. denotes the weight assigned to indicator j. Z_ij is the normalized value from Step 1. and are the PIS and NIS vectors from Step 2. Given equal importance of all indicators in this study, uniform weights were applied for j = 1,2,…,m.

Step 4: The relative closeness coefficient () for each region was derived as:

where (0–1) quantifies performance proximity to the ideal state:

indicates optimal performance (closest to PIS),

indicates optimal performance (closest to NIS).

Step 5: The normalized decision matrix was utilized to construct radar charts, enabling multidimensional comparison of the 15 regions across all indicators.

Results

Epidemiological characteristics

HIV/AIDS case reporting varied significantly across healthcare tiers: 52.0% of tertiary hospitals (77/148), 81.7% of township facilities (98/120), 8.8% of community health centers (5/57), 100% of provincial/municipal CDCs (2/2), and 86.7% of county-level CDCs (13/15) documented cases. This heterogeneous reporting landscape coincided with a sustained upward trajectory in newly diagnosed cases (NDCs) from 2005 onward (Fig. 1), reflecting intensified case ascertainment amid program expansion.

Fig. 1.

The trends and temporal distribution of HCIs and newly reported cases in Nanning City, Guangxi, China (1989–2020). a the number of HCIs reporting HIV/AIDS cases, b the number of newly reported cases. The classification of stages based on a shift stages of national and regional HIV/AIDS policies

Surveillance trends in Nanning (1989–2020) evolved through four distinct phases: provincial CDC dominance characterized 1989–2004 (99.9% case reporting), followed by modest expansion of HCIs and newly reported cases during 2005–2009 alongside persistent CDC reporting predominance (65.6%). The transition to hospital-led reporting emerged in 2010–2014 (69.7%), marked by volatile HCI growth and stable newly reported cases increases. By 2015–2020, stabilized HCI expansion and accelerated newly reported cases ascertainment cemented hospital reporting dominance (75.6%). Cumulatively, HCIs increased 1.8-fold while number of newly reported cases rose 0.7-fold, with township hospitals demonstrating the most pronounced growth (15.7-fold institutional expansion; 19.5-fold case increase) despite contributing minimally to total caseloads (7.1%) (Fig. 1 and Supplemental Table 1).

Case ascertainment across surveillance periods revealed distinct geographic and demographic patterns: 94.8% of newly reported cases originated within Guangxi province, with Nanning residents comprising 58.6% versus 36.3% from other Guangxi municipalities. County-township hospitals demonstrated significantly higher local case ascertainment (96.2%) compared to provincial-municipal hospitals (51.3%) and CDC (51.3%) (Fig. 2a and Supplemental Table 2). The MOs (≥ 50 years) demonstrated a 39.6-fold increase from 210 to 8,520 cases between surveillance phases, with hospitals reporting 85.0% (7,241/8,520) of this demographic. Township facilities exhibited the highest proportion of older adult cases (71.2%) (Fig. 2b and Supplemental Table 3).

Fig. 2.

The trends and temporal distribution of origins and the middle and older aged cases in Nanning City, Guangxi, China (1989–2020). a the percentages of four origins among newly reported cases, b the percentages of the middle and older aged cases among newly reported cases. The classification of stages based on a shift stages of national and regional HIV/AIDS policies

Consistent with effective testing scale-up, annual testing volume increased 4.3-fold (2010–2020), while test positivity rates declined significantly, from peak rates 16.8/10,000 tests to 11.2/10,000 in 2020. This pattern suggests progressive saturation of testing coverage in key populations, though persistently elevated positivity in Xingning (88.3/10,000) indicates ongoing high transmission burden requiring intensified interventions (Table 1).

Table 1.

Testing and newly reported HIV/AIDS cases in Nanning City Guangxi, China from 2012 to 2020

Year	Nanning			Guangxi			China
	Newly reportedcase (thousand)	Testing no. (million)	Test positivity rate (per 10,000)	Newly reportedcase (thousand)	Testing no. (million)	Test positivity rate (per 10,000)	Newly reportedcase (thousand)	Testing no. (million)	Test positivity rate (per 10,000)
2012	2.2	1.3	16.8	12.2	8.3	14.7	82.4	101.3	8.1
2013	2.3	2.2	10.6	10.8	9.5	11.3	90.1	111.1	8.1
2014	2.2	1.6	13.4	9.5	9.0	10.6	103.5	127.6	8.1
2015	2.8	1.9	14.5	9.4	9.6	9.8	115.5	143.6	8.0
2016	2.6	1.7	15.6	9.2	9.2	10.0	124.6	169.0	7.4
2017	3.0	2.1	14.8	8.9	9.6	9.3	134.5	200.7	6.7
2018	2.9	2.1	13.4	9.2	10.1	9.1	148.6	240.9	6.2
2019	3.0	2.3	13.2	11.1	10.9	10.2	151.3	276.8	5.5
2020	2.6	2.3	11.2	10.5	11.1	9.5	131.7	284.3	4.6

Spatial characteristics

Spatiotemporal analysis at district/county scale revealed progressive dispersion of HCIs from urban cores to rural peripheries alongside expanding case distribution, achieving complete county coverage by 2020 (Fig. 3). Tertiary hospitals clustered centrally, while primary care facilities dispersed throughout rural regions. During 2005–2009, expansion focused on provincial-municipal-county hospitals with minimal township facility engagement (Fig. 3b). Post-2010, rural township hospitals proliferated rapidly, contrasting with sparse community health center establishment in urban zones (Fig. 3c). By 2015–2020, HCI growth decelerated despite accelerating HIV/AIDS case incidence (Fig. 3d).

Fig. 3.

The spatial distribution of HCIs and HIV/AIDS cases in 15 regions of Nanning City, Guangxi, China during five stages (including a: 1989–2004, b: 2005–2009, c: 2010–2014, d: 2015–2020, d: 1989–2020, based on a shift stages of national and regional HIV/AIDS policies)

Healthcare institution (HCI) case reporting exhibited distinct geographic stratification. Urban regions displayed hierarchical concentration: Qingxiu* predominantly handled provincial hospital/CDC cases, while Xingning*, Liangqin*, and Dongmeng* relied on municipal hospitals. Xixiangtang* demonstrated provincial-municipal hospital predominance, contrasting with Gaoxing* exclusive provincial hospital reporting. Rural counties showed differential patterns: Hengxian**, Longan**,and Shanglin** reported primarily through county hospitals, whereas Mashan**and Yongning** utilized county CDCs. Binyang**showed township hospital dominance with disproportionately higher caseloads, while Wuming** maintained equitable case distribution across county-level hospitals, CDCs, and township facilities (Fig. 3e). (*Urban regions: provincial-municipal HCI dominance; **Rural regions: county-township HCI predominance).

Spatial clustering of elevated HIV test positivity rates (≥ 40 per 10,000 tests) occurred across seven regions in 2010, with peak rates in Xingning* (120.1), Hengxian** (106.6), Yongning** (100.1), Qingxiu* (60.1), and Mashan** (43.8). Subsequent surveillance periods demonstrated progressive rate reductions, leaving only Xingning* maintaining elevated positivity (88.3 per 10,000) by 2020, while others declined below 20 per 10,000. Significant annual fluctuations in rate distribution persisted across all 15 regions (Fig. 4).

Fig. 4.

The annual spatial distribution of HCIs, HIV/AIDS cases and test positivity rates in 15 regions of Nanning City, Guangxi, China during 2010 and 2020

Comprehensive evaluation of HIV test program performance

Radar maps visualized normalized proximity values for all regions according to TOPSIS rankings of HIV test program performance in 15 regions (Table 2). Proximity to the outer circumference indicates alignment with the positive-ideal solution (optimal HIV test program performance), while convergence toward the center reflects the negative-ideal solution (suboptimal performance). Smooth curves approximating the outer circumference demonstrate congruence between testing node distribution (newly reported case) and high testing yield (HIV test positivity rate). Conversely, concave-convex patterns indicate discordance between newly reported case and diminished yield, reflecting programmatic constraints such as insufficient testing nodes or suboptimal resource allocation among high-risk population. Thus, smoothly circumscribed curves represent peak program performance, while inwardly concave configurations denote systemic underperformance.

Table 2.

The HIV testing program performance of 15 regions in Nanning City, Guangxi, China from 2010 to 2020 analyzed by TOPSIS

Region	Di+	Di−	Ci	Rank (TOPSIS)	Rank (New cases)
Xingninga	3.52	0.53	0.87	1	2
Qingxiua	2.31	2.22	0.51	2	1
Hengxianb	1.41	2.51	0.36	3	3
Yongningb	0.7	3.45	0.17	4	11
Binyangb	0.62	3.18	0.16	5	5
Xixiangtanga	0.62	3.22	0.16	6	4
Longanb	0.44	3.34	0.12	7	6
Mashanb	0.34	3.45	0.09	8	8
Jiangnana	0.30	3.44	0.08	9	10
Shanglinb	0.23	3.50	0.06	10	9
Wumingb	0.23	3.56	0.06	11	7
Liangqinga	0.22	3.54	0.06	12	12
Jingkaia	0.16	3.62	0.04	13	14
Dongmenga	0.14	3.64	0.04	14	13
Gaoxina	0.09	3.67	0.02	15	15

D_i⁺: Separation from the positive ideal solution

D_i⁻: Separation from the negative ideal solution

Ci: The relative closeness to the ideal solution

^aThe urban regions reported HIV/AIDS cases mainly by the provincial-municipal HCIs

^bThe rural regions reported HIV/AIDS cases mainly by the county-township HCIs

TOPSIS  Technique for Order of Preference by Similarity to Ideal Solution

Our analysis revealed distinct patterns in HIV testing program performance across regions (Fig. 5). Urban centers reported substantial case detection volumes through provincial-municipal hospitals, yet demonstrated suboptimal testing yields between 2010 and 2016. Conversely, rural regions utilizing county-township health facilities achieved superior program performance compared to demographically equivalent urban areas. While Qingxiu* maintained the highest density of testing nodes (newly reported cases), its testing yield was significantly lower than both Xingning* and Hengxian**. Xingning* exhibited peak program performance, ranking second in distribution of newly reported case while achieving the highest regional yield (Fig. 5a). This performance differential persisted in comparative analyses: Qingxiu* underperformed relative to Xingning*, mirroring the significant deficit observed when comparing Xixiangtang* and Binyang** (Fig. 5b). Regions demonstrating optimal resource-yield alignment (Xingning*, Hengxian**, Binyang**, Longan**, Shanglin**) displayed smooth radar curves approximating the positive-ideal solution, indicative of efficient program implementation (Fig. 5a-d). In contrast, Yongnig** and Mashan** exhibited transient early performance followed by rapid yield deterioration (Fig. 5b, c), while suboptimal performance in remaining regions correlated directly with deficiencies of HIV case-finding (Fig. 5c-e). Notably, Xingning*, serving as Guangxi's primary HIV clinical hub, achieved benchmark performance. Rural outperformance was exemplified by Hengxian** and Binyang**, where high township hospital penetration and case-finding efficiency drove superior yields despite lower resource allocation (Fig. 5a, b). Regions with optimal TOPSIS scores (e.g., Xingning, Hengxian) demonstrated contextually appropriate performance, such as high case reports with declining positivity (Hengxian) or sustained high positivity justifying targeted resource allocation (Xingning).

Fig. 5.

The normalized values of HIV/AIDS cases and the test positivity rates calculated by TOPSIS in 15 regions of Nanning City, Guangxi, China (2010–2020). In radar maps, the names of indicators (number: Years, a newly reported cases, b the test positivity rate) located in the outer circles. The hierarchies of circles showed the levels of the normalized values. Each radar map was composed of normalized values of the three regions according to the adjacent TOPSIS rank. In radar maps, closer approach to the outer circle indicates positive-ideal solution, whereas closer approach to the inner point indicates negative-ideal solution. The smooth curve with the approach to the outer circle indicates best, whereas the concave-convex curve with the approach to the inner point indicates worst. Label * indicated urban regions mainly reported cases from provincial-municipal HCIs, whereas label ** indicated rural regions mainly reported cases from county-township HCIs

Discussion

The evolution of healthcare institutions (HCIs) and testing node distribution across four temporal phases closely aligned with landmark developments in Guangxi's HIV surveillance system, including: 1) structural reforms in case reporting protocols, 2) implementation of integrated prevention-control initiatives, and 3) maturation of China's three-tiered health delivery infrastructure.

During the foundational phase (1989–2004), surveillance predominantly relied on provincial CDC sentinel sites [20], with minimal case confirmation occurring at peripheral HCIs. The 2004 introduction of national strategies, notably the "Four Frees and One Care" policy [20], catalyzed rapid HIV testing scale-up, triggering an exponential surge in case detection. This necessitated urgent surveillance system reform beyond provincial/municipal CDC capacities to manage the expanded diagnostic burden [21–23], thereby accelerating decentralization of testing networks.

In 2004, Nanning pioneered Guangxi's first city-wide HIV surveillance strategy through implementation of the "Physician Reporting Duty at First Clinical Presentation" protocol. This was reinforced in 2006 by the national rollout of the web-enabled HIV/AIDS Case Reporting Information Management System (CRIMS), which streamlined data collection and epidemiological reporting [9, 23, 24]. During the subsequent phase (2005–2009), provincial-municipal hospitals demonstrated a marked surge in case reporting volume. Despite this expansion, CDC facilities maintained their predominance as the primary reporting source, accounting for 65.6% of total notifications.

China's 2010 healthcare reform established a primary healthcare (PHC)-oriented integrated delivery system [25, 26], catalyzing transformative changes in Guangxi's HIV response. Provincial authorities intensified comprehensive prevention programs while prioritizing development of a hierarchical laboratory network. This strategic initiative enhanced diagnostic capabilities across the three-tier health system to decentralize testing burden from overextended CDC laboratories [6]. Consequently, hospitals emerged as primary reporting venues for newly reported cases. As laboratory coverage expanded to all rural regions, surveillance responsibility transitioned fundamentally from CDC dominance (99.97%, 1989–2004) to hospital-based reporting (75.6%, 2015–2020), marking a systemic shift in HIV case report infrastructure.

Implementation of national and provincial AIDS programs culminated in substantial infrastructure expansion, evidenced by a 3.8-fold increase in testing nodes and 202 additional healthcare institutions (HCIs), with pronounced growth in township hospitals. This development established the foundational data infrastructure for evaluating citywide testing scale-up effectiveness while achieving transformative progress within the primary healthcare (PHC)-oriented delivery system. Concurrently, it accelerated deployment of targeted interventions addressing severe HIV epidemiology in rural populations, particularly aging demographics [27–29]. These coordinated efforts enhanced clinical treatment accessibility, thereby reducing community transmission in rural settings [30] and mitigating preventable morbidity associated with delayed diagnosis, including opportunistic infections, complications, and chronic comorbidities [11, 31, 32].

Evaluating HIV prevention programs through isolated indicator comparisons risks generating limited or misleading conclusions [33–35], a constraint equally applicable to surveillance sensitivity assessments in expanded testing initiatives. Our analysis revealed significant spatiotemporal heterogeneity in the rankings of annual tests, newly reported case, and HIV test positivity rates across regions and periods [36]. Crucially, aggregated indicator averages may obscure critical periodic and geographic limitations in program performance. Furthermore, discordant trajectories among multiple metrics complicate comprehensive health systems evaluation, precluding generalized assessments across diverse regions, hierarchical institutions, and temporal phases.

Consequently, we integrated annual newly reported HIV cases and test positivity rates (2010–2020) using TOPSIS with radar visualization. This methodology revealed a critical paradox: urban centers with maximal case reporting from provincial-municipal hospitals failed to demonstrate proportional yield efficiency despite elevated test positivity rates. Qingxiu* (case rank 1) and Xixiangtang* (rank 4), predominantly serviced by these tertiary facilities, exhibited suboptimal alignment between testing node density and epidemiological yield, manifesting as depressed positivity rates. Specifically, Qingxiu* underperformed relative to rural Hengxian** (rank 3; primarily county-hospital-sourced), while Xixiangtang* demonstrated significantly lower positivity than Binyang** (rank 5; township-hospital-driven).

Urban regions* relied predominantly on provincial-municipal hospitals for expanded HIV testing. While these tertiary facilities leveraged superior resources to attract large clinical populations, enabling passive surveillance through preoperative screening, their untargeted approach often diluted the HIV test positivity rate due to redundant testing of low-risk individuals [25]. This contrasts with the cost-efficiency imperative of achieving high yield (new diagnoses per test) through focused screening in key populations. Notably, top-ranked Xingning (TOPSIS Rank 1), southern Guangxi's primary HIV hub, exemplified strategic efficiency. Its elevated test positivity rate reflected both high underlying burden and precision targeting: specialized referral networks systematically attracted high-risk populations, maximizing alignment between new case reports and peak positivity rates. The observed migration to provincial hospitals underscores the need for regional coordination [37, 38] to replicate such efficiency beyond high-burden clusters.

In rural regions, expanded testing relied upon the decentralized primary healthcare (PHC) infrastructure, leveraging its extensive accessibility [39, 40] to achieve high case-finding efficiency, particularly among localized middle-aged and elderly demographics [22, 41]. County hospitals functioning as clinical treatment hubs, exemplified by Hengxian and Binyang, demonstrated superior programmatic efficiency by attracting substantial local high-risk populations. This outcome could be attributed to sustained pilot interventions initiated through international partnerships since 2003. Specifically, these programs cultivated advanced implementation expertise by enhancing township hospital capabilities via strategic workforce development and dedicated funding, thereby catalyzing service integration between public health and clinical medicine [26]. This synergy cultivated a more coordinated PHC network that maximized HIV prevention efficacy through comprehensive system utilization [42]. However, rural outperformance in Hengxian/Binyang reflects localized program maturity, not universal applicability. PHC integration strategies should be tailored to regional epidemic profiles.

Our analysis further revealed suboptimal sustainability in regions failing to integrate primary health institutions into HIV surveillance systems. Yongnig** and Mashan** demonstrated transiently elevated testing yields followed by rapid deterioration due to insufficient PHC functional engagement. Initially, county CDC-led case management achieved effective early performance. However, disparities in PHC system implementation subsequently generated significant geographical heterogeneity in case-finding efficiency. Conversely, Longan** and Shanglin** achieved balanced expanded test program performance through proactive enhancement of primary health institution capabilities.

Despite its potential, the three-tier health system remains underutilized for expanded HIV testing services [22, 43]. In urban settings, 91.2% (52/57) of community health centers (CHCs) and 48.0% (71/148) of hospitals failed to implement HIV case reporting. CHCs provide critical infrastructure for delivering essential public health services, including HIV prevention, with particular accessibility advantages for elderly populations through community-based health management [22, 40, 41]. Evidence confirms the cost-effectiveness of integrating testing into primary care systems [44]. However, restricted laboratory network coverage (limited to tertiary and secondary hospitals) prevents CHC engagement due to diagnostic capacity deficits. Furthermore, HIV-related stigma presents substantial barriers to integrating HIV services within community-based primary care. Optimizing urban primary healthcare networks for expanded testing thus requires strategic prioritization to enable earlier diagnosis.

Optimized primary healthcare (PHC) networks achieve superior HIV testing performance through efficient resource-yield alignment, maximizing test positivity rates for targeted screening—particularly among localized aging populations. These findings yield three universally applicable lessons for tiered health systems. Firstly, the complementary case-finding roles observed between provincial hospitals (high testing volume) and primary care networks (superior resource-yield alignment) provide a blueprint for decentralizing testing in regions with concentrated epidemics. This is particularly relevant for Southeast Asia, where hospital-centric systems struggle with late diagnosis rates [45]. Secondly, Guangxi’s experience in reducing test positivity rates through township hospital expansion demonstrates how resource-constrained settings can leverage primary care integration to achieve equitable progress toward the first 95 target. Similar PHC-led scale-up in Malawi and Nigeria increased case-finding efficiency while halving costs per new diagnosis [46, 47]. Thirdly, TOPSIS-radar methodology transcends local evaluation, which offers a propositional framework for monitoring spatial equity in testing access globally. As countries adopt differentiated service delivery models, this approach can identify subnational ‘hotspots’ where PHC integration could avert late-stage diagnoses [45]. Consequently, regions with suboptimal performance require prioritized integration of evidence-based PHC interventions, guided by context-tailored strategies to accelerate progress toward epidemic control targets.

Limitations

This study acknowledges several methodological. Firstly, minor administrative boundary adjustments over the 31-year surveillance period may introduce geographical misclassification. Secondly, standardized reporting via CRIMS (established 2010) precluded comprehensive pre-2010 data inclusion. Facility-level operational efficiency metrics (e.g., testing throughput) remain unquantified. Finally, equal TOPSIS indicator weighting may not reflect regional priority variations. While TOPSIS accommodated regional epidemic phases, test positivity rates have complex interpretations, that sustained high rates may indicate either successful high-risk targeting or inadequate coverage. Restricted data availability limited indicator comprehensiveness. Future research should incorporate granular efficiency analyses with sensitivity testing of weighting schemes, integrate incidence trends and undiagnosed burden estimates, and expand the evaluation framework through fieldwork.

Conclusions

Integrated tiered health systems show potential for complementary HIV case-finding function, though facility-level heterogeneity persists, which provincial-municipal hospitals maximized test volume while county-township networks demonstrated optimal resource alignment for their epidemic context, achieving declining positivity through accessible testing. PHC integration on select settings appeared to enhance epidemiological congruence in regions with robust township networks. This systemic optimization, such as prioritizing PHC-integrated testing in underserved regions, strengthening referral coordination, and implementing TOPSIS-guided resource allocation from Guangxi experience, is recommendable for achieving 95–95-95 targets in high-burden settings. Generalizability to non-high-burden regions warrants further study.

Abbreviations

AIDS

Acquired Immune Deficiency Syndrome

HIV

Human Immunodeficiency Virus

HCIs

Health Care Institutions

TOPSIS

Technique for Order of Preference by Similarity to Ideal Solution

CRIMS

Comprehensive Response Information Management System

CDCs

Centers for Disease Control and prevention

MOs

Middle and Older aged cases

Guangxi

Guangxi Zhuang Autonomous Region

PHC

Primary Health Care

Authors' contributions

S.S.L., X.Y.T., and P.H.Z. contributed equally to this work. S.S.L. and Y.M.S. take responsibility for the study design and result interpretation. S.S.L., P.H.Z., M.L., M.C., and Q.L. analyzed the data. S.S.L., X.Y.T. and P.H.Z. drafted the manuscript. B.Y.L., H.Y.T., Y.F. and Y.M.S. reviewed the manuscript. All authors approved the final version for submission.

Funding

This study was supported by grants from Science Priority Grant 2019SKLID602 of the State Key Laboratory of Infections Disease Prevention and Control, Guangxi Bagui Honor Scholarship, and National Nature Science Foundation International/Inter-Organization Cooperation and Exchange Study-NSFC-VR Project (China and Switzerland).

Data availability

All data supporting the findings of this study are available from the corresponding author but restrictions apply to the availability of these data, which were used under license from Chinese Center of Disease Control and Prevention for the current study, and so are not publicly available. Data are, however, available from the corresponding author upon reasonable request and with permission from Chinese Center of Disease Control and Prevention.

Declarations

Ethics approval and consent to participate

Not applicable.

Consent for publication

Not applicable.

Competing interests

The authors declare no competing interests.