Are malaria rapid diagnostic test results stable over time to support verification of surveillance data?

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Corine Ngufor, Kim A. Lindblade, Sunday Atobatele, Arthur Mpimbaza, and 9 more This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-6760274/v1 This work is licensed under a CC BY 4.0 License Status: Published Journal Publication published 22 Oct, 2025 Read the published version in Malaria Journal → Version 1 posted 10 You are reading this latest preprint version Abstract Background Rapid diagnostic tests (RDTs) have improved malaria case management by enabling point-of-care confirmation of infection, particularly in low-resource settings. In addition to clinical use, RDT results recorded in health facility registers are a critical component of national malaria surveillance systems. Recently, national programs have explored using stored RDT cassettes to validate register data. However, manufacturers caution that results should be read within 15 - 30 minutes, raising concerns about result validity after this period. This study evaluated the stability of RDT results over a one-month period to assess whether stored cassettes can reliably reflect initial test outcomes. Methods We conducted a prospective, observational study in 48 health facilities across Benin, Nigeria, and Uganda from June to September 2023. A digital artificial intelligence (AI)-powered RDT reader (HealthPulse, Audere, Seattle WA USA) was used to photograph RDTs immediately after interpretation by health workers and again at one week and one month. Images were independently interpreted by a trained panel, with results categorized as positive, negative, invalid, or indeterminate. Only RDTs with valid interpretations at all three time points were included in the final analysis. Positive and negative predictive values (PPV and NPV) were calculated to measure the accuracy of results from stored RDTs relative to the initial interpretation. Results Out of 54,251 RDTs captured, 45,155 (83.2%) met inclusion criteria. At one month, 95.1% of initially positive RDTs remained positive, and 95.3% of initially negative RDTs remained negative. The PPV of a positive result at one month was 96.3% (95% CI 96.1, 96.5), while the NPV of a negative result was 93.8% (95% CI 93.4, 94.1). Most result changes occurred within the first week. Faint lines were associated with higher rates of change in both directions; 26.8% changing from positive to negative and 48.1% changing from negative to positive. Stability of results also varied across RDT products and specific test lines. Conclusions Stored RDT cassettes maintain high result stability over one month and can serve as a reliable reference to verify health facility records. Result changes were linked to premature interpretation, faint lines or product- or line-specific characteristics. Adherence to manufacturer-recommended read times may reduce the proportion of RDTs that change from negative to positive. These findings support the utility of stored RDTs in improving data quality and rational antimalarial use in malaria-affected settings. malaria rapid diagnostic tests surveillance Benin Nigeria Uganda stability Figures Figure 1 Figure 2 Figure 3 Figure 4 BACKGROUND Rapid diagnostic tests (RDTs) have transformed malaria case management in resource-limited settings by enabling point-of-care confirmation of infection prior to treatment. They require minimal expertise, no refrigeration or specialized equipment and no electricity, making them suitable for use in a wide range of environments. RDT results recorded in health facility registers also underpin malaria surveillance systems in endemic countries, providing essential data for tracking disease trends, guiding resource allocation, and assessing the effectiveness of control measures [ 1 ]. Malaria RDTs are lateral flow immunoassays that use antibodies to detect specific antigens produced by malaria parasites in the bloodstream. These antibodies are immobilized on a nitrocellulose strip housed within a plastic cassette. A few drops of blood from a finger prick are placed into one well of the cassette and buffer solution is added to a second well. The buffer lyses red blood cells, releasing any parasite proteins. Dye-labeled antibodies specific for one or more Plasmodium species then bind to parasite antigens. Capillary action moves the blood and antigen-antibody complexes along the membrane, where they are captured by one or more lines of fixed antibodies (the T, or test, lines), forming visible colored bands in the results window. The control (C) line, located further down the membrane, captures excess dye-labeled antibodies and forms a visible colored band that indicates the test has functioned correctly. Malaria RDTs primarily target two antigens: histidine-rich protein 2 (HRP2) and Plasmodium lactate dehydrogenase (pLDH), with aldolase used less commonly. HRP2 is specific to Plasmodium falciparum , while pLDH is produced by all human-infecting Plasmodium species. [ 2 ]. The most commonly used RDTs feature a single test line that detects HRP2, followed by formats with two test lines, where the second line detects either pan-pLDH or P. vivax -specific pLDH [ 3 ]. Both HRP2 and pLDH lines can appear faint at low parasite densities; however, for a given parasite density, the pLDH line is typically less intense than the HRP2 line. Interestingly, HRP2 lines may also appear weak at very high parasite densities [ 4 , 5 ]. More than 328 million RDTs were performed globally in 2023 [ 6 ]. The vast majority took place in the World Health Organization (WHO) African Region (266 million, 81%) and the South-East Asia Region (44 million, 14%). In Africa, RDTs are used nearly four times more often than microscopy for malaria diagnosis. Despite this heavy reliance on RDTs for confirming infection, many healthcare workers (HCWs) do not consistently base treatment decisions on RDT results [ 7 – 9 ]. As a result, discrepancies between test outcomes and clinical decisions can lead to inaccurate recording of RDT results in health facility registers [ 10 ]. In 2023, the Ministry of Health of Benin launched monthly data validation meetings at the district level as part of a national strategy to enhance the accuracy, completeness, and timeliness of routine malaria data reporting [ 11 ]. A key innovation introduced through this process was the systematic verification of patient register entries by comparing them to the physical results still visible on used and archived RDT cassettes that were stored at health facilities. This verification method offered a practical means of cross-checking whether the test outcomes recorded in facility registers accurately reflected the actual diagnostic results. However, it also raised important technical questions regarding the stability and reliability of RDT results over time. RDT manufacturers typically recommend that results be interpreted within a short time window (typically 15 to 30 minutes after sample application), because the chemical reactions that produce the visible lines may degrade or change beyond this window, potentially leading to misinterpretation of results [ 12 ]. Given the potential usefulness of Benin’s retrospective malaria data validation approach using archived RDT cassettes, we were interested in assessing whether RDT results could be reliably interpreted up to one month after testing. To guide future data validation efforts using archived RDT cassettes, we conducted a dedicated study as part of a broader multicountry evaluation on the accuracy of RDT records in health facilities. The objective of our study was to assess the stability and interpretability of RDT results over a one-month period of storage under typical health facility conditions. METHODS This evaluation was conducted from June to September 2023 in the context of a larger study that has been described elsewhere [ 10 ]. Briefly, a prospective, observational study was performed in selected health facilities across Benin, Nigeria, and Uganda (Côte d’Ivoire was included in the main study but did not participate in this assessment due to resource constraints). Trained research assistants were present during principal operating hours in 16 health facilities in each country and used a digital artificial intelligence (AI)-powered RDT reader (HealthPulse, Audere, Seattle, WA USA) to take photographs of all RDTs performed in the study facilities. The HealthPulse RDT reader has an image quality assurance component that leverages computer vision and machine learning processes to assess the quality of images of RDTs, immediately flagging those that do not meet quality standards (such as those with blur or multiple RDTs) and prompting the user to retake the photo. The HealthPulse application includes an AI algorithm running on the mobile device that interprets the RDT result from the image. However, in this study, the AI result was not shared with research assistants or any other study personnel (except SC) until the end of the study and was not used in this evaluation. Research assistants photographed RDTs as soon as possible after they were interpreted by HCWs; however, the timing of interpretation was determined by the HCWs, and the time elapsed between sample application and result interpretation was not recorded. Each RDT was labeled with a unique, preprinted barcode placed on the back. A paired label was placed against the patient data in the health facility register and research assistants recorded the HCW interpretation of the RDT. During the first three months of the study, used RDT cassettes were placed in paper envelopes and stored in cardboard boxes at health facilities under ambient temperature and humidity conditions. The RDTs were re-photographed at one week and again at one month following the initial interpretation. At each time point, the barcode on the back of the cassette was scanned to allow images to be matched over time. The HealthPulse application recorded the date and time of each image capture. After the final image capture, RDT cassettes were discarded in accordance with national disposal guidelines. All RDT images were stripped of metadata and sent to an external, quality-controlled panel trained in interpreting RDT results from images. For RDT products with two test lines, the HRP2 line is referred to as T0 while the pLDH line is referred to as T1. The panel classified each line on the test as present, absent, or obstructed from view, and a simple algorithm categorized each RDT result as positive (C line present and one or more T lines present), negative (C line present and no T lines present and no T lines obstructed from view), invalid (C line absent and not obstructed from view), or indeterminate (lines obstructed from view). The panel noted the RDT product name (i.e. brand and model, Supplemental Table 1), flagged images of RDTs with excess blood in the result window and noted the presence of faint test lines, although they did not note which test line was faint. All RDT result classifications and observations on presence of lines at each time point were determined by the external panel. Data management and analysis As this was an exploratory, descriptive study, no sample size calculations were performed. Observations were excluded if the initial RDT result was interpreted as indeterminate, as these were considered to reflect problems with the image rather than true test outcomes. For analyses involving individual test lines, RDTs in which one or more test lines were judged to be obstructed from view were excluded. Images taken between five and 10 days (inclusive) after the initial image were classified as ‘one week,’ while those taken between 25 and 35 days (inclusive) were classified as ‘one month.’ If multiple images were captured within the same time window, the first image was retained. The final analytical data set included only records with results for all time points. Simple descriptive statistics were conducted in R (R Foundation for Statistical Computing, Vienna, Austria). The positive and negative predictive values (PPV and NPV, respectively) for results at one month were calculated to measure the probability that they accurately reflected the initial results, and 95% confidence intervals were calculated using the Wilson score method for binomial proportions using the binom package in R [ 13 ]. Ethical issues No patients were consented by the study team as the RDT images were recorded after patient consultation was concluded and without any accompanying personal identifying information. The PATH institutional review board approved the multi-country study protocol. In Benin, the Comité National d’Ethique pour la Recherche en Santé provided approval. In Nigeria, approval was received from Oyo State Ministry of Health Research Ethics Committee, Sokoto State Health Research Ethics Committee and the National Health Research Ethics Committee of Nigeria. The Uganda National Council for Science and Technology and Vector Control Division-Research & Ethics Committee both reviewed and approved the study in Uganda. RESULTS From June through September 2023, 154,141 RDT images representing 54,251 RDTs were collected. After excluding 9130 RDT images that were missing one or more follow-up images, the analytical database included 44,605 RDTs (82.2%). Exclusion due to missing follow-up images Among the RDT images excluded due to missing follow-up images, the majority (5374, 58.9%) were initially photographed in September and were scheduled for follow-up after the evaluation had concluded. To assess whether there was any bias associated with missing follow-up images, we analyzed the 31,907 RDTs photographed before August, of which 1336 (4.2%) were missing one or more follow-up images. RDTs initially interpreted as negative were more likely to miss follow-up images (748, 5.7%) than those initially interpreted as positive (581, 3.1%). The proportion of RDTs missing follow-up images was higher in Nigeria (638, 16.2%) compared to Uganda (534, 3.2%) and Benin (164, 1.5%). However, the presence of faint lines was not associated with missing follow-up images (with faint line: 131, 4.2%; without faint line: 1205, 4.2%). RDT characteristics Uganda contributed 22,553 (50.6%), Benin 15,334 (33.4%) and Nigeria 6718 (15.1%) RDTs (Table 1 ). The panel interpreted 25,129 (56.3%) RDTs as positive at the time of administration, with variation across countries: the proportion positive was lower in Nigeria (39.4%) and higher in Uganda (60.4%). The number of positive results at one week was 25,022 (56.1%) and at one month was 24,864 (55.7%). Invalid results were rare with 56 (0.1%) at the time of administration, declining to 8 (0.0%) at both follow-up time points. Table 1 Characteristics of rapid diagnostic tests stored and followed up to one month Characteristic Total N = 44,605 n (%) Benin N = 15,334 n (%) Nigeria N = 6718 n (%) Uganda N = 22,553 n (%) Initial result Positive 25,129 (56.3) 8868 (57.8) 2645 (39.4) 13,616 (60.4) Negative 19,420 (43.5) 6461 (42.1) 4070 (60.6) 8889 (39.4) Invalid 56 (0.1) 5 (0.0) 3 (0.0) 48 (0.2) One-week result Positive 25,022 (56.1) 8691 (56.7) 2968 (44.2) 13,363 (59.3) Negative 19,575 (43.9) 6640 (43.3) 3750 (55.8) 9185 (40.7) Invalid 8 (0.0) 3 (0.0) 0 (0.0) 5 (0.0) One-month result Positive 24,864 (55.7) 8697 (56.7) 2855 (42.5) 13,312 (59.0) Negative 19,733 (44.2) 6633 (43.3) 3863 (57.5) 9237 (41.0) Invalid 8 (0.0) 4 (0.0) 0 (0.0) 4 (0.0) RDT product AdvDx Malaria Pf 6711 (15.0) 0 (0.0) 6704 (99.8) 7 (0.0) Bioline Malaria Pf 27,846 (62.4) 15,127 (98.7) 14 (0.2) 12,705 (56.3) Bioline Malaria Pf (HRP2/pLDH) 201 (0.5) 0 (0.0) 0 (0.0) 201 (0.9) First Response Malaria Pf 1395 (3.1) 0 (0.0) 0 (0.0) 1395 (6.2) First Response Malaria Pf Ag (pLDH/HRP2) 1951 (4.4) 0 (0.0) 0 (0.0) 1951 (8.7) ParaHIT Pf 322 (0.7) 1 (0.0) 0 (0.0) 321 (1.4) Standard Q Pf 626 (1.4) 206 (1.3) 0 (0.0) 420 (1.9) Other 322 (0.7) 1 (0.0) 0 (0.0) 321 (1.4) Faint line on initial result Yes 4246 (9.5) 651 (4.2) 644 (9.6) 2951 (13.1) No 40,359 (90.5) 14,683 (95.8) 6074 (90.4) 19,602 (86.9) Blood obscuring results window on initial result Yes 6 (0.0) 0 (0.0) 0 (0.0) 6 (0.0) No 44,599 (100) 15,334 (100) 6718 (100) 22,547 (100) HRP2: histidine-rich protein 2; Pf: Plasmodium falciparum; pLDH: Plasmodium lactate dehydrogenase; RDT: rapid diagnostic test Bioline Malaria Pf (Abbott, IL USA) was the most commonly used RDT product (27,846, 62.4%), followed by AdvDx Malaria Pf (Advy Chemical, Mumbai, India; 6711, 15.0%) (Table 1 ). Benin and Nigeria used Bioline Malaria Pf and AdvDx Malaria Pf RDTs, respectively, almost exclusively whereas the majority of RDTs in Uganda were Bioline Malaria Pf but the country also used a number of other RDT products. There were 322 (0.7%) RDT products that were not recognized by the panel. Faint lines were observed on 4246 (9.5%) RDTs at their initial administration (Table 1 ). The proportion varied substantially by country, with Benin reporting a much lower proportion (4.2%) and Uganda a much higher proportion (13.1%) of RDTs with faint lines. The occurrence of faint lines was also associated with the RDT product, and was more frequent among the two products with two test lines (28.9–29.9%) than among those with a single test line (7.5–12.3%). Only 6 RDTs were flagged as having blood obscuring the results window. Stability of results Over the one-month evaluation period, 42,408 (95.1%) RDTs retained their original result. This included 23,901 (95.1%) of 25,129 initially positive RDTs and 18,499 (95.3%) of 19,420 initially negative RDTs. Among the 56 RDTs originally classified as invalid, 8 (14.3%) remained invalid. After one month of storage, the probability that a cassette with a positive result was originally positive (PPV) was 96.1% (95% confidence interval [CI] 95.9, 96.4) while the probability that a cassette showing a negative result was initially negative (NPV) was 93.7% (95% CI 93.4, 94.1). The equivalent probability for an invalid test result was 100% (95% CI 67.5, 100). The proportions of RDTs that converted from positive to negative (4.9%) and negative to positive (4.7%) over one month were comparable (Table 2 ). No results classified initially as positive or negative changed to invalid, but 75% of the invalid tests converted to positive and 10.7% converted to negative over one month. Table 2 Characteristics of rapid diagnostic tests and changes in results one week and one month after the initial interpretation (N = 44,605) Original to one month Original to one week One week to one month No change in result N (%) Positive to negative n (%) Negative to positive n (%) Invalid to positive n (%) Invalid to negative n (%) Positive to negative n (%) Negative to positive n (%) Invalid to positive n (%) Invalid to negative n (%) Positive to negative n (%) Negative to positive n (%) Invalid to negative n (%) Overall 42,408 (95.1) 1228 (4.9) 921 (4.7) 42 (75.0) 6 (10.7) 1165 (4.6) 1016 (5.2) 42 (75.0) 7 (12.5) 766 (3.1) 609 (3.1) 1 (12.5) Country Benin 14,923 (97.3) 291 (3.3) 119 (1.8) 1 (20.0) 0 (0.0) 294 (3.3) 115 (1.8) 2 (40.0) 0 (0.0) 108 (1.2) 115 (1.7) 0 (0.0) Nigeria 6189 (92.1) 159 (6.0) 367 (9.0) 2 (66.7) 1 (33.3) 134 (5.1) 455 (11.2) 2 (66.7) 1 (33.3) 289 (9.7) 176 (4.7) 0 (0.0) Uganda 21,296 (94.4) 778 (5.7) 435 (4.9) 39 (81.2) 5 (10.4) 737 (5.4) 446 (5.0) 38 (79.2) 6 (12.5) 369 (2.8) 318 (3.5) 1 (20.0) RDT product 0 (0.0) 0 (0.0) AdvDx Malaria Pf 6183 (92.1) 158 (6.0) 367 (9.0) 2 (66.7) 1 (33.3) 134 (5.1) 455 (11.2) 2 (66.7) 1 (33.3) 288 (9.7) 176 (4.7) 0 (0.0) Bioline Malaria Pf 26,732 (96.0) 872 (5.4) 240 (2.1) 2 (25.0) 0 (0.0) 863 (5.3) 231 (2.0) 3 (37.5) 0 (0.0) 283 (1.8) 283 (2.3) 0 (0.0) Bioline Malaria Pf (HRP2/pLDH) 187 (93.0) 14 (9.9) 0 (0.0) 0 (0.0) 0 (0.0) 17 (12.0) 2 (3.4) 0 (0.0) 0 (0.0) 6 (4.7) 7 (9.5) 0 (0.0) First Response Malaria Pf 1328 (95.2) 10 (0.9) 49 (15.6) 7 (77.8) 1 (11.1) 7 (0.7) 51 (16.2) 7 (77.8) 1 (11.1) 17 (1.5) 12 (4.4) 0 (0.0) First Response Malaria Pf Ag (pLDH/HRP2) 1756 (90.0) 21 (1.5) 139 (25.9) 31 (88.6) 4 (11.4) 21 (1.5) 139 (25.9) 30 (85.7) 5 (14.3) 42 (2.7) 43 (10.2) 0 (0.0) ParaHIT Pf 605 (96.6) 9 (2.9) 12 (3.8) 0 (0.0) 0 (0.0) 11 (3.6) 10 (3.1) 0 (0.0) 0 (0.0) 6 (2.0) 10 (3.1) 0 (0.0) Standard Q Pf 5320 (95.8) 133 (4.3) 100 (4.0) 0 (0.0) 0 (0.0) 105 (3.4) 116 (4.7) 0 (0.0) 0 (0.0) 117 (3.8) 73 (3.0) 0 (0.0) Other 297 (92.2) 11 (5.4) 14 (12.1) 0 (0.0) 0 (0.0) 7 (3.4) 12 (10.3) 0 (0.0) 0 (0.0) 7 (3.3) 5 (4.5) 1 (50.0) Faint line Yes 3107 (73.2) 1120 (26.7) 15 (40.5) 4 (57.1) 0 (0.0) 1057 (25.2) 15 (40.5) 4 (57.1) 0 (0.0) 296 (9.4) 233 (21.6) 1 (25.0) No 39,301 (97.4) 108 (0.5) 906 (4.7) 38 (77.6) 6 (12.2) 108 (0.5) 1001 (5.2) 38 (77.6) 7 (14.3) 470 (2.2) 376 (2.0) 0 (0.0) HRP2: histidine-rich protein 2; Pf: Plasmodium falciparum; pLDH: Plasmodium lactate dehydrogenase; RDT: rapid diagnostic test Most changes in RDT results occurred within the first week (Table 2 and Fig. 1 ). Of the 25,129 RDTs initially interpreted as positive, 1165 (4.6%) converted to negative over the first week whereas 1016 (5.2%) of the 19,420 originally negative RDTs converted to positive (Table 2 ). The conversion of invalids to positive (42, 75.0%) or negative (7, 12.5%) occurred almost entirely over the first week. The proportion of RDTs that retained their original result varied among the RDT products in the study (Fig. 2 ). ParaHIT Pf (Arkray Healthcare Prvt Ltd, Mumbai, India) demonstrated the most stability over one month with 96.6% of tests retaining their original result (Table 2 ). First Response Malaria Pf Ag (pLDH/HRP2) (Premier Medical Corporation Ltd, Gujarat, India) had the greatest proportion of tests change sign with only 90.0% retaining their original result. The Bioline Malaria Pf (HRP2/pLDH) test exhibited a higher rate of positive to negative changes (9.9%) over one month compared to the other RDTs (Table 2 ). In contrast, First Response Malaria Pf (0.9%) and First Response Malaria Pf Ag (pLDH/HRP2) (1.5%) tests demonstrated lower rates of conversion from positive to negative over one month. The proportion of negative RDTs converting to positive was much higher among First Response Malaria Pf Ag (pLDH/HRP2) (25.9%), First Response Malaria Pf (15.6%) and AdvDx Malaria Pf (9.0%) than other RDTs. Faint lines were associated with large proportions of RDTs changing from positive to negative (26.8%) and negative to positive (48.1%) and only 73.2% of RDTs noted to have faint lines retained their result over one month (Table 2 ). Although the proportion changing from positive to negative and vice versa was much higher within one week than between one week and one month, there remained a much higher rate of change between one week and one month for the RDTs where faint lines were noted compared to those without faint lines. We compared the proportion of initially negative RDTs that converted to positive in the first week by the minimum time recommended by the manufacturer for reading the result (Fig. 3 ). Three RDTs were recommended to be read between 15–30 minutes, another three between 20–30 minutes, and one test between 25–30 minutes. There was a trend toward higher proportions of tests converting to positive with longer minimum wait times, although the ParaHIT Pf test was a notable outlier with a long minimum wait time and small proportion of change. Stability of HRP2 and pLDH lines on two-line tests On the Bioline Pf (HRP2/pLDH) test, the T1 line detected P. falciparum -specific pLDH, whereas on the First Response Pf Ag (pLDH/HRP2) test, the T1 line detected pan-pLDH, which is indicative of P. falciparum , P. vivax , P. ovale , or P. malariae . For both products, changes in the T lines were more frequent within the first week than between the one-week and one-month time points (Fig. 4 ). Notably, the T0 (HRP2) line on the Bioline Pf test was more likely to change from present to absent during the first week, whereas the same line on the First Response test more often changed from absent to present (Table 3 ). This pattern was also observed for the T1 (pLDH) lines on both tests. Table 3 Change in presence of test lines over one week by rapid diagnostic test product and antigen, 2023 (N = 2143) Characteristic No change in line presence n (%) Present to absent n (%) Absent to present n (%) Bioline Malaria Pf (HRP2/pLDH) (n = 201) T0 (HRP2) 182 (90.5) 17 (12.0) 2 (3.4) T1 (Pf pLDH) 182 (90.5) 19 (22.4) 0 (0) First Response Malaria Pf Ag (pLDH/HRP2) (n = 1942) T0 (HRP2) 1778 (91.6) 19 (1.5) 143 (26.4) T1 (pan-pLDH) 1732 (89.2) 26 (3.9) 184 (14.4) HRP2: histidine-rich protein 2; Pf: Plasmodium falciparum; pLDH: Plasmodium lactate dehydrogenase DISCUSSION We found that more than 95% of stored used RDTs remained unchanged over a one-month period, with most changes occurring within the first week after test administration. Both the frequency and direction of changes in results were associated with the RDT product-type. Among products with a single test line, the AdvDx Malaria Pf demonstrated the highest overall rate of change and ParaHIT Pf showed the most stability. Both of the products with two test lines (Bioline Malaria Pf [HRP2/pLDH] and First Response Malaria Pf Ag [pLDH/HRP2]) also showed higher rates of change. AdvDx Malaria Pf and both Bioline RDT products showed higher rates of change from positive to negative compared to other products. Products showing higher rates of change from negative to positive included both First Response RDT products and AdvDx Malaria Pf. The observed changes from negative to positive may be linked to early reading of results as there was an association between the minimum recommended read time of the RDT product type (per manufacturer instructions) and the likelihood of a change from negative to positive. It is likely that many of these RDTs were interpreted and photographed before the minimum development period had been reached. The mechanisms underlying the transition from positive to negative are less clear, but may relate to characteristics of the test membrane or the monoclonal antibodies used to detect parasite antigens. Among RDT products with two test lines, there was no consistent association between result stability and the specific antigen target (HRP2 vs pLDH); rather, differences were more apparent at the product level. Both RDTs with two test lines observed in this study were associated with a higher proportion of tests flagged as having faint lines. Given that pLDH is generally less sensitive than HRP2 in infections where parasites express the HRP2 antigen, tests including pLDH lines may be more prone to faint bands [ 14 ]. Faint lines were associated with substantial changes in both directions, which may reflect an inherent difficulty in visualizing faint lines in photographs. Several factors may contribute to this discrepancy, including the visual acuity of the individual reading the test and lighting conditions, as photographs taken in low-light settings may benefit from camera flash, enhancing line visibility. However, the most critical factor appears to be the degree of attention devoted to interpretation: faint lines are more likely to be overlooked during a cursory glance but are often detected when the reader takes the time to carefully examine the result. RDTs are primarily designed to provide point-of-care parasitological confirmation of malaria and guide treatment decisions. Their widespread scale-up over the past decade has additionally strengthened malaria surveillance by expanding coverage of laboratory testing and standardizing diagnostic confirmation. Increasingly, RDTs are also used in research, including for parasite DNA extraction and genomic analysis [ 15 , 16 ]. However, to our knowledge, this is the first analysis of RDT result stability to assess their potential as a source record for cross-verifying data in health facility registers, laboratory registers, and national health information systems. National malaria programs have already begun to use stored RDTs to cross-check health facility records to rationalize use of antimalarial medicines and strengthen data quality [ 11 , 17 ]. This study demonstrates that there is a 96.1% probability that a cassette indicating a positive result after being stored for one month was initially positive and a 93.7% probability that a cassette indicating negative at one month was initially negative, providing reasonable confidence that RDT cassettes can be used as a reference standard to compare with health facility records of RDT results. The findings from this study therefore generally support the use of malaria RDTs stored for up to one month under typical ambient conditions in health facilities for data validation purposes, in line with the approach implemented in Benin. However, the specific RDT product used will affect the validity of this approach and countries are recommended to review these results and take the RDT product into account when deciding whether to validate health facility data with stored RDT cassettes. Results from this study can also be used to determine the sample size that would be needed to evaluate other RDT products not observed in this study. Finally, further research assessing the stability of RDT results beyond one month (such as over a three-month period), could help determine the feasibility of longer storage durations, potentially providing a more practical and cost-effective frequency for routine data verification. A key strength of this study was the large number of RDTs observed and the inclusion of multiple RDT products across three countries. The use of barcodes to track individual RDTs enabled systematic follow-up and image capture over a one-month period. However, a notable limitation was the absence of follow-up images for over 9,000 RDTs. While many of these were likely missing due to the time constraints of the study, there was evidence that RDTs initially interpreted as negative were more likely to be missing follow-up images. This may reflect a tendency among health facility staff to prioritize the retention of positive tests, although we do not believe this would systematically bias the assessment of result stability over time as the rate of change was similar. Additionally, faint test lines continue to pose a challenge for visual interpretation of RDTs. It is likely that a proportion of RDTs with faint lines were misclassified, which could have affected the results for certain RDT products, particularly those with pLDH lines. CONCLUSIONS This study demonstrated that more than 95% of stored RDT cassettes retained their original result. Close to half of the changes may be due to reading RDTs before their period of development has concluded. Health officials should emphasize the need to follow manufacturer’s guidelines and ensure that tests are only interpreted as negative after the minimum development time has elapsed. Despite this finding, RDT cassettes stored for one month provide reliable information on the original RDT result and could be used as part of a strategy to evaluate surveillance data quality and the rational use of antimalarials in health facilities in Africa. ABBREVIATIONS C Control line CI Confidence interval HRP2 Histidine-rich protein 2 LTFU Lost to follow-up Pf Plasmodium falciparum pLDH Plasmodium lactate dehydrogenase PMI President’s Malaria Initiative RDT Rapid diagnostic test T Test line WHO World Health Organization Declarations ACKNOWLEDGEMENTS Many staff members of the organizations implementing MaCRA made important contributions: Manfred Accrombessi, Hospice Avanon, Corneille Hueha (CREC, Benin); Hilary Okagbue, Evelyn Orya, Shiva Gab-Deedam, Olufisayo Bademosi, (Sydani Group, Nigeria); Anne Katahoire, Jane Frances Namuganga, Jenipher Musoke (CHDC, Uganda). We are grateful to the national malaria programs in each country for their support. We received excellent research support from Saadjo Sow (PMI Insights). Megan Littrell, Kim Vu and Taj Munson provided direction and administrative support to the PMI Insights project. Sasha Frade, Sam Smedinghoff, and the Audere development team (Audere, Seattle, WA USA) supported customisation of the HealthPulse application and provided the data sets used in this evaluation. We gratefully acknowledge the contributions of Audere’s artificial intelligence data creation and labeling teams at the Centre for HIV-AIDS Prevention Studies (South Africa) and Indivillage (India). We are grateful to Stephanie Zobrist (PATH) for comments on a draft of this paper. FUNDING This evaluation was co-funded by PMI Insights and the Bill & Melinda Gates Foundation (INV-043942). PMI Insights was the global operational research and program evaluation project of the U.S. President’s Malaria Initiative (PMI). Funding for this evaluation is made possible by the generous support of the American people through the United States Agency for International Development (USAID) through cooperative agreement No. 7200AA20CA00031. The contents are the responsibility of the authors and do not necessarily reflect the views of USAID or the United States Government. AUTHOR INFORMATION Authors and Affiliations Centre de Recherche Entomologique de Cotonou, Cotonou, Benin Idelphonse Ahogni Corine Ngufor PMI Insights Project/PATH, Geneva, Switzerland Kim A. Lindblade Sydani Group, Abuja, Nigeria Ese Akpiroroh Sunday Atobatele Child Health and Development Centre, Makerere University, Kampala, Uganda Arthur Mpimbaza Nelson Ssewante Audere, Seattle, WA USA Shawna Cooper U.S. President’s Malaria Initiative, United States Agency for International Development, Washington, DC USA Kevin Griffith Michael Humes National Malaria Control Division, Ministry of Health, Kampala, Uganda Bosco Agaba Programme National de Lutte contre le Paludisme, Cotonou, Benin Augustin Kpemasse National Malaria Elimination Programme, Abuja, Nigeria Onyebuchi Okoro Contributions KL, MH and KG conceived and designed the evaluation with contributions from CN, AM and SA. CN, SA, AM, IA, NS, EA, AK, OO and BA oversaw data collection activities. SC oversaw the development of the HealthPulse application used in the study. CN and KL drafted the manuscript. KL analyzed the data. All authors critically reviewed the manuscript. All authors read and approved the final manuscript. Ethical approval and consent to participate Ethical approval was obtained from: the Comité National d’Ethique pour la Recherche en Santé (Benin); the National Health Research Ethics Committee (Nigeria); the Oyo State Health Research Ethics Committee (Nigeria); the Sokoto State Health Research Ethics Committee (Nigeria); the Uganda National Council on Science and Technology (Uganda); the Vector Control Division Research and Ethics Committee (Uganda); and the WGC IRB in the USA. All participants provided written, informed consent to participate. Consent for publication Not applicable. Availability of data and materials The datasets used and/or analyzed during the current evaluation can be provided by the corresponding author on reasonable request. Competing interests The authors declare that they have no competing interests. References Fountain A, Ye Y, Roca-Feltrer A, Rowe AK, Camara A, Fofana A, et al. Surveillance as a Core Intervention to Strengthen Malaria Control Programs in Moderate to High Transmission Settings. Am J Trop Med Hyg. 2023;108:8–13. Kavanaugh MJ, Azzam SE, Rockabrand DM. Malaria Rapid Diagnostic Tests: Literary Review and Recommendation for a Quality Assurance, Quality Control Algorithm. Diagnostics. 2021;11:768. UNITAID. Malaria diagnostics market and technology landscape [Internet]. Geneva, Switzerland: World Health Organization; 2022. Available from: https://unitaid.org/uploads/Malaria-Diagnostics-Market-and-Technology-Landscape.pdf Gillet P, Mukadi P, Vernelen K, Van Esbroeck M, Muyembe J-J, Bruggeman C, et al. External quality assessment on the use of malaria rapid diagnostic tests in a non-endemic setting. Malar J. 2010;9:359. Gatton ML, Rees-Channer RR, Glenn J, Barnwell JW, Cheng Q, Chiodini PL, et al. Pan-Plasmodium band sensitivity for Plasmodium falciparum detection in combination malaria rapid diagnostic tests and implications for clinical management. Malar J. 2015;14:115. WHO. World Malaria Report 2024. Geneva, Switzerland: World Health Organization; 2024. Davlantes E, Camara A, Guilavogui T, Fofana, Balde. Quality of Malaria Case Management and Reporting at Public Health Facilities in Six Health Districts in Guinea, 2018. Am J Trop Med Hyg. 2019;101:148–56. Zurovac D, Githinji S, Memusi D, Kigen S, Machini B. Major Improvements in the Quality of Malaria Case-Management under the “Test and Treat” Policy in Kenya. PLoS One. 2014;9. Plucinski, Ferreira. Evaluating malaria case management at public health facilities in two provinces in Angola. Malar J. 2017;16. Lindblade, KA, Mpimbaza, A, Ngufor, C, Yavo, W, Atobatele, S, Akpiroroh, E, et al. Assessing the accuracy of the recording and reporting of malaria rapid diagnostic test results in four African countries: Methods and key results. under review. Ngufor, C, Ahogni, I. Documentation of the monthly validation of malaria rapid diagnostic test (RDTs) results in Benin. Cotonou, Benin; 2025 Apr. WHO. WHO prequalification of in vitro diagnostics programme: AdvDx Malaria Pf Rapid Malaria Ag Detection Test [Internet]. Geneva, Switzerland: WHO; 2019. Available from: https://extranet.who.int/prequal/sites/default/files/whopr_files/PQDx_0345-101-00_AdvDxMalariaDetectionTest_v2.pdf Dorai-Raj, S. Package “binom” [Internet]. 2022. Available from: https://cran.r-project.org/web/packages/binom/binom.pdf Mouatcho JC, Goldring JPD. Malaria rapid diagnostic tests: challenges and prospects. J Med Microbiol. 2013;62:1491–505. Veron V, Carme B. RECOVERY AND USE OF PLASMODIUM DNA FROM MALARIA RAPID DIAGNOSTIC TESTS. Am J Trop Med Hyg. 2006;74:941–3. Ishengoma DS, Lwitiho S, Madebe RA, Nyagonde N, Persson O, Vestergaard LS, et al. Using rapid diagnostic tests as source of malaria parasite DNA for molecular analyses in the era of declining malaria prevalence. Malar J. 2011;10:6. Ayandipo EO, Fagbola M, Gbolade A, Okpokpolom JT, Ojo A, Abikoye O, et al. Decline in malaria test positivity rates following capacity building and archiving of malaria rapid diagnostic test cassettes in Oyo State, Nigeria: a retrospective review of records. Malar J. 2025;24:132. Additional Declarations No competing interests reported. Supplementary Files Supplementarytable1.docx Cite Share Download PDF Status: Published Journal Publication published 22 Oct, 2025 Read the published version in Malaria Journal → Version 1 posted Editorial decision: Revision requested 01 Aug, 2025 Reviews received at journal 25 Jul, 2025 Reviews received at journal 24 Jul, 2025 Reviewers agreed at journal 07 Jul, 2025 Reviewers agreed at journal 04 Jul, 2025 Reviewers agreed at journal 16 Jun, 2025 Reviewers invited by journal 13 Jun, 2025 Editor assigned by journal 27 May, 2025 Submission checks completed at journal 27 May, 2025 First submitted to journal 27 May, 2025 You are reading this latest preprint version Research Square lets you share your work early, gain feedback from the community, and start making changes to your manuscript prior to peer review in a journal. As a division of Research Square Company, we’re committed to making research communication faster, fairer, and more useful. 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Also discoverable on Platform About Our Team In Review Editorial Policies Advisory Board Help Center Resources Author Services Accessibility API Access RSS feed Manage Cookie Preferences © Research Square 2026 | ISSN 2693-5015 (online) Privacy Policy Terms of Service Do Not Sell My Personal Information {"props":{"pageProps":{"initialData":{"identity":"rs-6760274","acceptedTermsAndConditions":true,"allowDirectSubmit":false,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":470692117,"identity":"c6ca69e4-5b3b-4799-b706-7f7f7d7b376c","order_by":0,"name":"Corine Ngufor","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAABBklEQVRIiWNgGAWjYJCCAxCK8QEDQ4UFgwFMWIKwFmag4jMSDAZsRGhhgGthbCNCC39778PDBRUMefIRyYyPK+dJ2JvLdycw/KhhSJzZgF2LxJnjBodnnGEoNryRzGx4dptE4s423g2MPccYEmfjsMVAIo3hMG8bQ+LGGfnHJBu3SSQYHOPdwMDbwJA4D6+WfyAtyWySjXMk7EFaGP8S1AJUMF8CpKVBgnEDUAszSASXwyTOHGM4zHNMInEDz2NmwwYQ41juhsMyxySMcXmfv72N+TNPjU3i/PZkxocNNTb2BofPbnz4psZGdsYBHNZALWMwQFZwgKiIlMfhjFEwCkbBKBgFDAB3cFdDGKjDYgAAAABJRU5ErkJggg==","orcid":"","institution":"Centre de Recherche Entomologique de Cotonou","correspondingAuthor":true,"prefix":"","firstName":"Corine","middleName":"","lastName":"Ngufor","suffix":""},{"id":470692118,"identity":"2f49d56c-30c8-4619-a519-ee8ab0833a4e","order_by":1,"name":"Kim A. 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14:08:36","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-6760274/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-6760274/v1","draftVersion":[],"editorialEvents":[{"content":"https://doi.org/10.1186/s12936-025-05595-0","type":"published","date":"2025-10-22T16:16:13+00:00"}],"editorialNote":"","failedWorkflow":false,"files":[{"id":84810583,"identity":"210478d6-5065-402a-beb0-bc4fbab5f7a4","added_by":"auto","created_at":"2025-06-17 14:49:20","extension":"png","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":61371,"visible":true,"origin":"","legend":"\u003cp\u003eAlluvial plot showing changes in malaria rapid diagnostic test results after one week and one month of storage, 2023 (N=44,605)\u003c/p\u003e","description":"","filename":"1.png","url":"https://assets-eu.researchsquare.com/files/rs-6760274/v1/3c9b9f1132eb21e3bf767ea0.png"},{"id":84810585,"identity":"de44e9c4-f7ac-4f3c-af99-1cdfe60d50c0","added_by":"auto","created_at":"2025-06-17 14:49:20","extension":"png","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":130393,"visible":true,"origin":"","legend":"\u003cp\u003eAlluvial plot showing changes in rapid diagnostic test results over one month of storage by RDT product, 2023 (N=44,605)\u003c/p\u003e","description":"","filename":"2.png","url":"https://assets-eu.researchsquare.com/files/rs-6760274/v1/1e945d95662698d2263f3ae8.png"},{"id":84812690,"identity":"c61bee1e-8e26-464a-a3d1-07bc2214e659","added_by":"auto","created_at":"2025-06-17 15:05:20","extension":"png","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":60806,"visible":true,"origin":"","legend":"\u003cp\u003ePercent change of rapid diagnostic tests from negative to positive over the first week by the minimum reading time\u003c/p\u003e","description":"","filename":"3.png","url":"https://assets-eu.researchsquare.com/files/rs-6760274/v1/c2f7569ece2a3e163974ed79.png"},{"id":84812694,"identity":"a389f52f-1557-42a1-b127-1796a6133859","added_by":"auto","created_at":"2025-06-17 15:05:20","extension":"png","order_by":4,"title":"Figure 4","display":"","copyAsset":false,"role":"figure","size":80531,"visible":true,"origin":"","legend":"\u003cp\u003eAlluvial plot showing the changes in the presence or absence of test lines in two RDT products at one week and one month\u003c/p\u003e","description":"","filename":"4.png","url":"https://assets-eu.researchsquare.com/files/rs-6760274/v1/6b8f7ca2c6cfa7198dbbd6b2.png"},{"id":94489904,"identity":"5793cd47-b53d-48be-a251-f640e3b6ff05","added_by":"auto","created_at":"2025-10-27 17:06:28","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":1335157,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-6760274/v1/0693747f-5831-454f-9f81-51cfbcce9249.pdf"},{"id":84810587,"identity":"187fd070-c01e-447d-b514-ed9e355016b8","added_by":"auto","created_at":"2025-06-17 14:49:20","extension":"docx","order_by":1,"title":"","display":"","copyAsset":false,"role":"supplement","size":89947,"visible":true,"origin":"","legend":"","description":"","filename":"Supplementarytable1.docx","url":"https://assets-eu.researchsquare.com/files/rs-6760274/v1/085b012fc135f6153dc0d371.docx"}],"financialInterests":"No competing interests reported.","formattedTitle":"Are malaria rapid diagnostic test results stable over time to support verification of surveillance data?","fulltext":[{"header":"BACKGROUND","content":"\u003cp\u003eRapid diagnostic tests (RDTs) have transformed malaria case management in resource-limited settings by enabling point-of-care confirmation of infection prior to treatment. They require minimal expertise, no refrigeration or specialized equipment and no electricity, making them suitable for use in a wide range of environments. RDT results recorded in health facility registers also underpin malaria surveillance systems in endemic countries, providing essential data for tracking disease trends, guiding resource allocation, and assessing the effectiveness of control measures [\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eMalaria RDTs are lateral flow immunoassays that use antibodies to detect specific antigens produced by malaria parasites in the bloodstream. These antibodies are immobilized on a nitrocellulose strip housed within a plastic cassette. A few drops of blood from a finger prick are placed into one well of the cassette and buffer solution is added to a second well. The buffer lyses red blood cells, releasing any parasite proteins. Dye-labeled antibodies specific for one or more \u003cem\u003ePlasmodium\u003c/em\u003e species then bind to parasite antigens. Capillary action moves the blood and antigen-antibody complexes along the membrane, where they are captured by one or more lines of fixed antibodies (the T, or test, lines), forming visible colored bands in the results window. The control (C) line, located further down the membrane, captures excess dye-labeled antibodies and forms a visible colored band that indicates the test has functioned correctly.\u003c/p\u003e \u003cp\u003eMalaria RDTs primarily target two antigens: histidine-rich protein 2 (HRP2) and Plasmodium lactate dehydrogenase (pLDH), with aldolase used less commonly. HRP2 is specific to \u003cem\u003ePlasmodium falciparum\u003c/em\u003e, while pLDH is produced by all human-infecting \u003cem\u003ePlasmodium\u003c/em\u003e species. [\u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e]. The most commonly used RDTs feature a single test line that detects HRP2, followed by formats with two test lines, where the second line detects either pan-pLDH or \u003cem\u003eP. vivax\u003c/em\u003e-specific pLDH [\u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e]. Both HRP2 and pLDH lines can appear faint at low parasite densities; however, for a given parasite density, the pLDH line is typically less intense than the HRP2 line. Interestingly, HRP2 lines may also appear weak at very high parasite densities [\u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e, \u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eMore than 328\u0026nbsp;million RDTs were performed globally in 2023 [\u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e]. The vast majority took place in the World Health Organization (WHO) African Region (266\u0026nbsp;million, 81%) and the South-East Asia Region (44\u0026nbsp;million, 14%). In Africa, RDTs are used nearly four times more often than microscopy for malaria diagnosis. Despite this heavy reliance on RDTs for confirming infection, many healthcare workers (HCWs) do not consistently base treatment decisions on RDT results [\u003cspan additionalcitationids=\"CR8\" citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e]. As a result, discrepancies between test outcomes and clinical decisions can lead to inaccurate recording of RDT results in health facility registers [\u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eIn 2023, the Ministry of Health of Benin launched monthly data validation meetings at the district level as part of a national strategy to enhance the accuracy, completeness, and timeliness of routine malaria data reporting [\u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e]. A key innovation introduced through this process was the systematic verification of patient register entries by comparing them to the physical results still visible on used and archived RDT cassettes that were stored at health facilities. This verification method offered a practical means of cross-checking whether the test outcomes recorded in facility registers accurately reflected the actual diagnostic results. However, it also raised important technical questions regarding the stability and reliability of RDT results over time. RDT manufacturers typically recommend that results be interpreted within a short time window (typically 15 to 30 minutes after sample application), because the chemical reactions that produce the visible lines may degrade or change beyond this window, potentially leading to misinterpretation of results [\u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e12\u003c/span\u003e]. Given the potential usefulness of Benin\u0026rsquo;s retrospective malaria data validation approach using archived RDT cassettes, we were interested in assessing whether RDT results could be reliably interpreted up to one month after testing. To guide future data validation efforts using archived RDT cassettes, we conducted a dedicated study as part of a broader multicountry evaluation on the accuracy of RDT records in health facilities. The objective of our study was to assess the stability and interpretability of RDT results over a one-month period of storage under typical health facility conditions.\u003c/p\u003e"},{"header":"METHODS","content":"\u003cp\u003eThis evaluation was conducted from June to September 2023 in the context of a larger study that has been described elsewhere [\u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e]. Briefly, a prospective, observational study was performed in selected health facilities across Benin, Nigeria, and Uganda (C\u0026ocirc;te d\u0026rsquo;Ivoire was included in the main study but did not participate in this assessment due to resource constraints).\u003c/p\u003e \u003cp\u003eTrained research assistants were present during principal operating hours in 16 health facilities in each country and used a digital artificial intelligence (AI)-powered RDT reader (HealthPulse, Audere, Seattle, WA USA) to take photographs of all RDTs performed in the study facilities. The HealthPulse RDT reader has an image quality assurance component that leverages computer vision and machine learning processes to assess the quality of images of RDTs, immediately flagging those that do not meet quality standards (such as those with blur or multiple RDTs) and prompting the user to retake the photo. The HealthPulse application includes an AI algorithm running on the mobile device that interprets the RDT result from the image. However, in this study, the AI result was not shared with research assistants or any other study personnel (except SC) until the end of the study and was not used in this evaluation.\u003c/p\u003e \u003cp\u003eResearch assistants photographed RDTs as soon as possible after they were interpreted by HCWs; however, the timing of interpretation was determined by the HCWs, and the time elapsed between sample application and result interpretation was not recorded. Each RDT was labeled with a unique, preprinted barcode placed on the back. A paired label was placed against the patient data in the health facility register and research assistants recorded the HCW interpretation of the RDT.\u003c/p\u003e \u003cp\u003eDuring the first three months of the study, used RDT cassettes were placed in paper envelopes and stored in cardboard boxes at health facilities under ambient temperature and humidity conditions. The RDTs were re-photographed at one week and again at one month following the initial interpretation. At each time point, the barcode on the back of the cassette was scanned to allow images to be matched over time. The HealthPulse application recorded the date and time of each image capture. After the final image capture, RDT cassettes were discarded in accordance with national disposal guidelines.\u003c/p\u003e \u003cp\u003eAll RDT images were stripped of metadata and sent to an external, quality-controlled panel trained in interpreting RDT results from images. For RDT products with two test lines, the HRP2 line is referred to as T0 while the pLDH line is referred to as T1. The panel classified each line on the test as present, absent, or obstructed from view, and a simple algorithm categorized each RDT result as positive (C line present and one or more T lines present), negative (C line present and no T lines present and no T lines obstructed from view), invalid (C line absent and not obstructed from view), or indeterminate (lines obstructed from view). The panel noted the RDT product name (i.e. brand and model, Supplemental Table\u0026nbsp;1), flagged images of RDTs with excess blood in the result window and noted the presence of faint test lines, although they did not note which test line was faint. All RDT result classifications and observations on presence of lines at each time point were determined by the external panel.\u003c/p\u003e \u003cdiv id=\"Sec3\" class=\"Section2\"\u003e \u003ch2\u003eData management and analysis\u003c/h2\u003e \u003cp\u003eAs this was an exploratory, descriptive study, no sample size calculations were performed. Observations were excluded if the initial RDT result was interpreted as indeterminate, as these were considered to reflect problems with the image rather than true test outcomes. For analyses involving individual test lines, RDTs in which one or more test lines were judged to be obstructed from view were excluded.\u003c/p\u003e \u003cp\u003eImages taken between five and 10 days (inclusive) after the initial image were classified as \u0026lsquo;one week,\u0026rsquo; while those taken between 25 and 35 days (inclusive) were classified as \u0026lsquo;one month.\u0026rsquo; If multiple images were captured within the same time window, the first image was retained. The final analytical data set included only records with results for all time points.\u003c/p\u003e \u003cp\u003eSimple descriptive statistics were conducted in R (R Foundation for Statistical Computing, Vienna, Austria). The positive and negative predictive values (PPV and NPV, respectively) for results at one month were calculated to measure the probability that they accurately reflected the initial results, and 95% confidence intervals were calculated using the Wilson score method for binomial proportions using the \u003cem\u003ebinom\u003c/em\u003e package in R [\u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e13\u003c/span\u003e].\u003c/p\u003e \u003c/div\u003e\n\u003ch3\u003eEthical issues\u003c/h3\u003e\n\u003cp\u003eNo patients were consented by the study team as the RDT images were recorded after patient consultation was concluded and without any accompanying personal identifying information. The PATH institutional review board approved the multi-country study protocol. In Benin, the Comit\u0026eacute; National d\u0026rsquo;Ethique pour la Recherche en Sant\u0026eacute; provided approval. In Nigeria, approval was received from Oyo State Ministry of Health Research Ethics Committee, Sokoto State Health Research Ethics Committee and the National Health Research Ethics Committee of Nigeria. The Uganda National Council for Science and Technology and Vector Control Division-Research \u0026amp; Ethics Committee both reviewed and approved the study in Uganda.\u003c/p\u003e"},{"header":"RESULTS","content":"\u003cp\u003eFrom June through September 2023, 154,141 RDT images representing 54,251 RDTs were collected. After excluding 9130 RDT images that were missing one or more follow-up images, the analytical database included 44,605 RDTs (82.2%).\u003c/p\u003e\n\u003ch3\u003eExclusion due to missing follow-up images\u003c/h3\u003e\n\u003cp\u003eAmong the RDT images excluded due to missing follow-up images, the majority (5374, 58.9%) were initially photographed in September and were scheduled for follow-up after the evaluation had concluded. To assess whether there was any bias associated with missing follow-up images, we analyzed the 31,907 RDTs photographed before August, of which 1336 (4.2%) were missing one or more follow-up images. RDTs initially interpreted as negative were more likely to miss follow-up images (748, 5.7%) than those initially interpreted as positive (581, 3.1%). The proportion of RDTs missing follow-up images was higher in Nigeria (638, 16.2%) compared to Uganda (534, 3.2%) and Benin (164, 1.5%). However, the presence of faint lines was not associated with missing follow-up images (with faint line: 131, 4.2%; without faint line: 1205, 4.2%).\u003c/p\u003e\n\u003ch3\u003eRDT characteristics\u003c/h3\u003e\n\u003cp\u003eUganda contributed 22,553 (50.6%), Benin 15,334 (33.4%) and Nigeria 6718 (15.1%) RDTs (Table \u003cspan class=\"InternalRef\"\u003e1\u003c/span\u003e). The panel interpreted 25,129 (56.3%) RDTs as positive at the time of administration, with variation across countries: the proportion positive was lower in Nigeria (39.4%) and higher in Uganda (60.4%). The number of positive results at one week was 25,022 (56.1%) and at one month was 24,864 (55.7%). Invalid results were rare with 56 (0.1%) at the time of administration, declining to 8 (0.0%) at both follow-up time points.\u003c/p\u003e\n\u003cdiv class=\"gridtable\"\u003e\u0026nbsp;\u003ctable id=\"Tab1\" border=\"1\"\u003e\n \u003ccaption language=\"En\"\u003e\n \u003cdiv class=\"CaptionNumber\"\u003eTable 1\u003c/div\u003e\n \u003cdiv class=\"CaptionContent\"\u003e\n \u003cp\u003eCharacteristics of rapid diagnostic tests stored and followed up to one month\u003c/p\u003e\n \u003c/div\u003e\n \u003c/caption\u003e\n \u003ccolgroup cols=\"5\"\u003e\u003c/colgroup\u003e\n \u003cthead\u003e\n \u003ctr\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eCharacteristic\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eTotal\u003c/p\u003e\n \u003cp\u003eN\u0026thinsp;=\u0026thinsp;44,605\u003c/p\u003e\n \u003cp\u003en (%)\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eBenin\u003c/p\u003e\n \u003cp\u003eN\u0026thinsp;=\u0026thinsp;15,334\u003c/p\u003e\n \u003cp\u003en (%)\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eNigeria\u003c/p\u003e\n \u003cp\u003eN\u0026thinsp;=\u0026thinsp;6718\u003c/p\u003e\n \u003cp\u003en (%)\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eUganda\u003c/p\u003e\n \u003cp\u003eN\u0026thinsp;=\u0026thinsp;22,553\u003c/p\u003e\n \u003cp\u003en (%)\u003c/p\u003e\n \u003c/th\u003e\n \u003c/tr\u003e\n \u003c/thead\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eInitial result\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003ePositive\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e25,129 (56.3)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e8868 (57.8)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e2645 (39.4)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e13,616 (60.4)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eNegative\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e19,420 (43.5)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e6461 (42.1)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e4070 (60.6)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e8889 (39.4)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eInvalid\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e56 (0.1)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e5 (0.0)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e3 (0.0)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e48 (0.2)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eOne-week result\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003ePositive\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e25,022 (56.1)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e8691 (56.7)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e2968 (44.2)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e13,363 (59.3)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eNegative\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e19,575 (43.9)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e6640 (43.3)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e3750 (55.8)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e9185 (40.7)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eInvalid\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e8 (0.0)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e3 (0.0)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0 (0.0)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e5 (0.0)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eOne-month result\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003ePositive\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e24,864 (55.7)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e8697 (56.7)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e2855 (42.5)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e13,312 (59.0)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eNegative\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e19,733 (44.2)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e6633 (43.3)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e3863 (57.5)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e9237 (41.0)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eInvalid\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e8 (0.0)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e4 (0.0)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0 (0.0)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e4 (0.0)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eRDT product\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eAdvDx Malaria Pf\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e6711 (15.0)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0 (0.0)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e6704 (99.8)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e7 (0.0)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eBioline Malaria Pf\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e27,846 (62.4)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e15,127 (98.7)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e14 (0.2)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e12,705 (56.3)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eBioline Malaria Pf (HRP2/pLDH)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e201 (0.5)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0 (0.0)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0 (0.0)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e201 (0.9)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eFirst Response Malaria Pf\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1395 (3.1)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0 (0.0)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0 (0.0)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1395 (6.2)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eFirst Response Malaria Pf Ag (pLDH/HRP2)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1951 (4.4)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0 (0.0)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0 (0.0)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1951 (8.7)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eParaHIT Pf\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e322 (0.7)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1 (0.0)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0 (0.0)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e321 (1.4)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eStandard Q Pf\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e626 (1.4)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e206 (1.3)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0 (0.0)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e420 (1.9)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eOther\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e322 (0.7)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1 (0.0)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0 (0.0)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e321 (1.4)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eFaint line on initial result\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eYes\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e4246 (9.5)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e651 (4.2)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e644 (9.6)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e2951 (13.1)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eNo\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e40,359 (90.5)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e14,683 (95.8)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e6074 (90.4)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e19,602 (86.9)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eBlood obscuring results window on initial result\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eYes\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e6 (0.0)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0 (0.0)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0 (0.0)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e6 (0.0)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eNo\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e44,599 (100)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e15,334 (100)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e6718 (100)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e22,547 (100)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n \u003ctfoot\u003e\n \u003ctr\u003e\n \u003ctd colspan=\"5\"\u003eHRP2: histidine-rich protein 2; Pf: \u003cem\u003ePlasmodium falciparum;\u003c/em\u003e pLDH: \u003cem\u003ePlasmodium\u003c/em\u003e lactate dehydrogenase; RDT: rapid diagnostic test\u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tfoot\u003e\n \u003c/table\u003e\n\u003c/div\u003e\n\u003cp\u003eBioline Malaria Pf (Abbott, IL USA) was the most commonly used RDT product (27,846, 62.4%), followed by AdvDx Malaria Pf (Advy Chemical, Mumbai, India; 6711, 15.0%) (Table\u0026nbsp;\u003cspan class=\"InternalRef\"\u003e1\u003c/span\u003e). Benin and Nigeria used Bioline Malaria Pf and AdvDx Malaria Pf RDTs, respectively, almost exclusively whereas the majority of RDTs in Uganda were Bioline Malaria Pf but the country also used a number of other RDT products. There were 322 (0.7%) RDT products that were not recognized by the panel.\u003c/p\u003e\n\u003cp\u003eFaint lines were observed on 4246 (9.5%) RDTs at their initial administration (Table\u0026nbsp;\u003cspan class=\"InternalRef\"\u003e1\u003c/span\u003e). The proportion varied substantially by country, with Benin reporting a much lower proportion (4.2%) and Uganda a much higher proportion (13.1%) of RDTs with faint lines. The occurrence of faint lines was also associated with the RDT product, and was more frequent among the two products with two test lines (28.9\u0026ndash;29.9%) than among those with a single test line (7.5\u0026ndash;12.3%). Only 6 RDTs were flagged as having blood obscuring the results window.\u003c/p\u003e\n\u003cdiv id=\"Sec8\" class=\"Section2\"\u003e\n \u003ch2\u003eStability of results\u003c/h2\u003e\n \u003cp\u003eOver the one-month evaluation period, 42,408 (95.1%) RDTs retained their original result. This included 23,901 (95.1%) of 25,129 initially positive RDTs and 18,499 (95.3%) of 19,420 initially negative RDTs. Among the 56 RDTs originally classified as invalid, 8 (14.3%) remained invalid. After one month of storage, the probability that a cassette with a positive result was originally positive (PPV) was 96.1% (95% confidence interval [CI] 95.9, 96.4) while the probability that a cassette showing a negative result was initially negative (NPV) was 93.7% (95% CI 93.4, 94.1). The equivalent probability for an invalid test result was 100% (95% CI 67.5, 100).\u003c/p\u003e\n \u003cp\u003eThe proportions of RDTs that converted from positive to negative (4.9%) and negative to positive (4.7%) over one month were comparable (Table \u003cspan class=\"InternalRef\"\u003e2\u003c/span\u003e). No results classified initially as positive or negative changed to invalid, but 75% of the invalid tests converted to positive and 10.7% converted to negative over one month.\u003c/p\u003e\n \u003cdiv class=\"gridtable\"\u003e\u0026nbsp;\u003ctable id=\"Tab2\" border=\"1\"\u003e\n \u003ccaption language=\"En\"\u003e\n \u003cdiv class=\"CaptionNumber\"\u003eTable 2\u003c/div\u003e\n \u003cdiv class=\"CaptionContent\"\u003e\n \u003cp\u003eCharacteristics of rapid diagnostic tests and changes in results one week and one month after the initial interpretation (N\u0026thinsp;=\u0026thinsp;44,605)\u003c/p\u003e\n \u003c/div\u003e\n \u003c/caption\u003e\n \u003ccolgroup cols=\"13\"\u003e\u003c/colgroup\u003e\n \u003cthead\u003e\n \u003ctr\u003e\n \u003cth align=\"left\" rowspan=\"2\"\u003e\u0026nbsp;\u003c/th\u003e\n \u003cth align=\"left\" colspan=\"5\"\u003e\n \u003cp\u003eOriginal to one month\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\" colspan=\"4\"\u003e\n \u003cp\u003eOriginal to one week\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\" colspan=\"3\"\u003e\n \u003cp\u003eOne week to one month\u003c/p\u003e\n \u003c/th\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eNo change in result\u003c/p\u003e\n \u003cp\u003eN (%)\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003ePositive to negative\u003c/p\u003e\n \u003cp\u003en (%)\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eNegative to positive\u003c/p\u003e\n \u003cp\u003en (%)\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eInvalid to positive\u003c/p\u003e\n \u003cp\u003en (%)\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eInvalid to negative\u003c/p\u003e\n \u003cp\u003en (%)\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003ePositive to negative\u003c/p\u003e\n \u003cp\u003en (%)\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eNegative to positive\u003c/p\u003e\n \u003cp\u003en (%)\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eInvalid to positive\u003c/p\u003e\n \u003cp\u003en (%)\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eInvalid to negative\u003c/p\u003e\n \u003cp\u003en (%)\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003ePositive to negative\u003c/p\u003e\n \u003cp\u003en (%)\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eNegative to positive\u003c/p\u003e\n \u003cp\u003en (%)\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eInvalid to negative\u003c/p\u003e\n \u003cp\u003en (%)\u003c/p\u003e\n \u003c/th\u003e\n \u003c/tr\u003e\n \u003c/thead\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eOverall\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e42,408 (95.1)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e1228 (4.9)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e921 (4.7)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e42 (75.0)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e6 (10.7)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e1165 (4.6)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e1016 (5.2)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e42 (75.0)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e7 (12.5)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e766 (3.1)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e609 (3.1)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e1 (12.5)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eCountry\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eBenin\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e14,923 (97.3)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e291 (3.3)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e119 (1.8)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e1 (20.0)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0 (0.0)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e294 (3.3)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e115 (1.8)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e2 (40.0)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0 (0.0)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e108 (1.2)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e115 (1.7)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0 (0.0)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eNigeria\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e6189 (92.1)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e159 (6.0)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e367 (9.0)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e2 (66.7)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e1 (33.3)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e134 (5.1)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e455 (11.2)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e2 (66.7)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e1 (33.3)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e289 (9.7)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e176 (4.7)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0 (0.0)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eUganda\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e21,296 (94.4)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e778 (5.7)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e435 (4.9)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e39 (81.2)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e5 (10.4)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e737 (5.4)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e446 (5.0)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e38 (79.2)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e6 (12.5)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e369 (2.8)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e318 (3.5)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e1 (20.0)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eRDT product\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0 (0.0)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0 (0.0)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eAdvDx Malaria Pf\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e6183 (92.1)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e158 (6.0)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e367 (9.0)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e2 (66.7)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e1 (33.3)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e134 (5.1)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e455 (11.2)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e2 (66.7)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e1 (33.3)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e288 (9.7)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e176 (4.7)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0 (0.0)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eBioline Malaria Pf\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e26,732 (96.0)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e872 (5.4)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e240 (2.1)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e2 (25.0)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0 (0.0)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e863 (5.3)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e231 (2.0)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e3 (37.5)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0 (0.0)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e283 (1.8)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e283 (2.3)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0 (0.0)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eBioline Malaria Pf (HRP2/pLDH)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e187 (93.0)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e14 (9.9)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0 (0.0)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0 (0.0)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0 (0.0)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e17 (12.0)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e2 (3.4)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0 (0.0)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0 (0.0)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e6 (4.7)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e7 (9.5)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0 (0.0)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eFirst Response Malaria Pf\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e1328 (95.2)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e10 (0.9)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e49 (15.6)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e7 (77.8)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e1 (11.1)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e7 (0.7)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e51 (16.2)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e7 (77.8)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e1 (11.1)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e17 (1.5)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e12 (4.4)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0 (0.0)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eFirst Response Malaria Pf Ag (pLDH/HRP2)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e1756 (90.0)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e21 (1.5)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e139 (25.9)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e31 (88.6)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e4 (11.4)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e21 (1.5)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e139 (25.9)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e30 (85.7)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e5 (14.3)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e42 (2.7)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e43 (10.2)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0 (0.0)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eParaHIT Pf\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e605 (96.6)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e9 (2.9)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e12 (3.8)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0 (0.0)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0 (0.0)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e11 (3.6)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e10 (3.1)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0 (0.0)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0 (0.0)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e6 (2.0)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e10 (3.1)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0 (0.0)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eStandard Q Pf\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e5320 (95.8)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e133 (4.3)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e100 (4.0)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0 (0.0)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0 (0.0)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e105 (3.4)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e116 (4.7)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0 (0.0)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0 (0.0)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e117 (3.8)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e73 (3.0)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0 (0.0)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eOther\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e297 (92.2)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e11 (5.4)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e14 (12.1)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0 (0.0)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0 (0.0)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e7 (3.4)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e12 (10.3)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0 (0.0)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0 (0.0)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e7 (3.3)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e5 (4.5)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e1 (50.0)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eFaint line\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eYes\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e3107 (73.2)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e1120 (26.7)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e15 (40.5)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e4 (57.1)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0 (0.0)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e1057 (25.2)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e15 (40.5)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e4 (57.1)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0 (0.0)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e296 (9.4)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e233 (21.6)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e1 (25.0)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eNo\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e39,301 (97.4)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e108 (0.5)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e906 (4.7)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e38 (77.6)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e6 (12.2)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e108 (0.5)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e1001 (5.2)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e38 (77.6)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e7 (14.3)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e470 (2.2)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e376 (2.0)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0 (0.0)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n \u003ctfoot\u003e\n \u003ctr\u003e\n \u003ctd colspan=\"13\"\u003eHRP2: histidine-rich protein 2; Pf: \u003cem\u003ePlasmodium falciparum;\u003c/em\u003e pLDH: \u003cem\u003ePlasmodium\u003c/em\u003e lactate dehydrogenase; RDT: rapid diagnostic test\u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tfoot\u003e\n \u003c/table\u003e\n \u003c/div\u003e\n \u003cp\u003eMost changes in RDT results occurred within the first week (Table \u003cspan class=\"InternalRef\"\u003e2\u003c/span\u003e and Fig. \u003cspan class=\"InternalRef\"\u003e1\u003c/span\u003e). Of the 25,129 RDTs initially interpreted as positive, 1165 (4.6%) converted to negative over the first week whereas 1016 (5.2%) of the 19,420 originally negative RDTs converted to positive (Table \u003cspan class=\"InternalRef\"\u003e2\u003c/span\u003e). The conversion of invalids to positive (42, 75.0%) or negative (7, 12.5%) occurred almost entirely over the first week.\u003c/p\u003e\n \u003cp\u003eThe proportion of RDTs that retained their original result varied among the RDT products in the study (Fig. \u003cspan class=\"InternalRef\"\u003e2\u003c/span\u003e). ParaHIT Pf (Arkray Healthcare Prvt Ltd, Mumbai, India) demonstrated the most stability over one month with 96.6% of tests retaining their original result (Table \u003cspan class=\"InternalRef\"\u003e2\u003c/span\u003e). First Response Malaria Pf Ag (pLDH/HRP2) (Premier Medical Corporation Ltd, Gujarat, India) had the greatest proportion of tests change sign with only 90.0% retaining their original result. The Bioline Malaria Pf (HRP2/pLDH) test exhibited a higher rate of positive to negative changes (9.9%) over one month compared to the other RDTs (Table \u003cspan class=\"InternalRef\"\u003e2\u003c/span\u003e). In contrast, First Response Malaria Pf (0.9%) and First Response Malaria Pf Ag (pLDH/HRP2) (1.5%) tests demonstrated lower rates of conversion from positive to negative over one month. The proportion of negative RDTs converting to positive was much higher among First Response Malaria Pf Ag (pLDH/HRP2) (25.9%), First Response Malaria Pf (15.6%) and AdvDx Malaria Pf (9.0%) than other RDTs.\u003c/p\u003e\n \u003cp\u003eFaint lines were associated with large proportions of RDTs changing from positive to negative (26.8%) and negative to positive (48.1%) and only 73.2% of RDTs noted to have faint lines retained their result over one month (Table \u003cspan class=\"InternalRef\"\u003e2\u003c/span\u003e). Although the proportion changing from positive to negative and vice versa was much higher within one week than between one week and one month, there remained a much higher rate of change between one week and one month for the RDTs where faint lines were noted compared to those without faint lines.\u003c/p\u003e\n \u003cp\u003eWe compared the proportion of initially negative RDTs that converted to positive in the first week by the minimum time recommended by the manufacturer for reading the result (Fig. \u003cspan class=\"InternalRef\"\u003e3\u003c/span\u003e). Three RDTs were recommended to be read between 15\u0026ndash;30 minutes, another three between 20\u0026ndash;30 minutes, and one test between 25\u0026ndash;30 minutes. There was a trend toward higher proportions of tests converting to positive with longer minimum wait times, although the ParaHIT Pf test was a notable outlier with a long minimum wait time and small proportion of change.\u003c/p\u003e\n\u003c/div\u003e\n\u003ch3\u003eStability of HRP2 and pLDH lines on two-line tests\u003c/h3\u003e\n\u003cp\u003eOn the Bioline Pf (HRP2/pLDH) test, the T1 line detected \u003cem\u003eP. falciparum\u003c/em\u003e-specific pLDH, whereas on the First Response Pf Ag (pLDH/HRP2) test, the T1 line detected pan-pLDH, which is indicative of \u003cem\u003eP. falciparum\u003c/em\u003e, \u003cem\u003eP. vivax\u003c/em\u003e, \u003cem\u003eP. ovale\u003c/em\u003e, or \u003cem\u003eP. malariae\u003c/em\u003e. For both products, changes in the T lines were more frequent within the first week than between the one-week and one-month time points (Fig. \u003cspan class=\"InternalRef\"\u003e4\u003c/span\u003e). Notably, the T0 (HRP2) line on the Bioline Pf test was more likely to change from present to absent during the first week, whereas the same line on the First Response test more often changed from absent to present (Table \u003cspan class=\"InternalRef\"\u003e3\u003c/span\u003e). This pattern was also observed for the T1 (pLDH) lines on both tests.\u003c/p\u003e\n\u003cdiv class=\"gridtable\"\u003e\u0026nbsp;\u003ctable id=\"Tab3\" border=\"1\"\u003e\n \u003ccaption language=\"En\"\u003e\n \u003cdiv class=\"CaptionNumber\"\u003eTable 3\u003c/div\u003e\n \u003cdiv class=\"CaptionContent\"\u003e\n \u003cp\u003eChange in presence of test lines over one week by rapid diagnostic test product and antigen, 2023 (N\u0026thinsp;=\u0026thinsp;2143)\u003c/p\u003e\n \u003c/div\u003e\n \u003c/caption\u003e\n \u003ccolgroup cols=\"4\"\u003e\u003c/colgroup\u003e\n \u003cthead\u003e\n \u003ctr\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eCharacteristic\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eNo change in line presence\u003c/p\u003e\n \u003cp\u003en (%)\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003ePresent to absent\u003c/p\u003e\n \u003cp\u003en (%)\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eAbsent to present\u003c/p\u003e\n \u003cp\u003en (%)\u003c/p\u003e\n \u003c/th\u003e\n \u003c/tr\u003e\n \u003c/thead\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eBioline Malaria Pf (HRP2/pLDH) (n\u0026thinsp;=\u0026thinsp;201)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eT0 (HRP2)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e182 (90.5)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e17 (12.0)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e2 (3.4)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eT1 (Pf pLDH)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e182 (90.5)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e19 (22.4)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0 (0)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eFirst Response Malaria Pf Ag (pLDH/HRP2) (n\u0026thinsp;=\u0026thinsp;1942)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eT0 (HRP2)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e1778 (91.6)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e19 (1.5)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e143 (26.4)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eT1 (pan-pLDH)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e1732 (89.2)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e26 (3.9)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e184 (14.4)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n \u003ctfoot\u003e\n \u003ctr\u003e\n \u003ctd colspan=\"4\"\u003eHRP2: histidine-rich protein 2; Pf: \u003cem\u003ePlasmodium falciparum;\u003c/em\u003e pLDH: \u003cem\u003ePlasmodium\u003c/em\u003e lactate dehydrogenase\u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tfoot\u003e\n \u003c/table\u003e\n\u003c/div\u003e"},{"header":"DISCUSSION","content":"\u003cp\u003eWe found that more than 95% of stored used RDTs remained unchanged over a one-month period, with most changes occurring within the first week after test administration. Both the frequency and direction of changes in results were associated with the RDT product-type. Among products with a single test line, the AdvDx Malaria Pf demonstrated the highest overall rate of change and ParaHIT Pf showed the most stability. Both of the products with two test lines (Bioline Malaria Pf [HRP2/pLDH] and First Response Malaria Pf Ag [pLDH/HRP2]) also showed higher rates of change. AdvDx Malaria Pf and both Bioline RDT products showed higher rates of change from positive to negative compared to other products. Products showing higher rates of change from negative to positive included both First Response RDT products and AdvDx Malaria Pf.\u003c/p\u003e \u003cp\u003eThe observed changes from negative to positive may be linked to early reading of results as there was an association between the minimum recommended read time of the RDT product type (per manufacturer instructions) and the likelihood of a change from negative to positive. It is likely that many of these RDTs were interpreted and photographed before the minimum development period had been reached. The mechanisms underlying the transition from positive to negative are less clear, but may relate to characteristics of the test membrane or the monoclonal antibodies used to detect parasite antigens.\u003c/p\u003e \u003cp\u003eAmong RDT products with two test lines, there was no consistent association between result stability and the specific antigen target (HRP2 vs pLDH); rather, differences were more apparent at the product level. Both RDTs with two test lines observed in this study were associated with a higher proportion of tests flagged as having faint lines. Given that pLDH is generally less sensitive than HRP2 in infections where parasites express the HRP2 antigen, tests including pLDH lines may be more prone to faint bands [\u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e14\u003c/span\u003e]. Faint lines were associated with substantial changes in both directions, which may reflect an inherent difficulty in visualizing faint lines in photographs. Several factors may contribute to this discrepancy, including the visual acuity of the individual reading the test and lighting conditions, as photographs taken in low-light settings may benefit from camera flash, enhancing line visibility. However, the most critical factor appears to be the degree of attention devoted to interpretation: faint lines are more likely to be overlooked during a cursory glance but are often detected when the reader takes the time to carefully examine the result.\u003c/p\u003e \u003cp\u003eRDTs are primarily designed to provide point-of-care parasitological confirmation of malaria and guide treatment decisions. Their widespread scale-up over the past decade has additionally strengthened malaria surveillance by expanding coverage of laboratory testing and standardizing diagnostic confirmation. Increasingly, RDTs are also used in research, including for parasite DNA extraction and genomic analysis [\u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e15\u003c/span\u003e, \u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e16\u003c/span\u003e]. However, to our knowledge, this is the first analysis of RDT result stability to assess their potential as a source record for cross-verifying data in health facility registers, laboratory registers, and national health information systems. National malaria programs have already begun to use stored RDTs to cross-check health facility records to rationalize use of antimalarial medicines and strengthen data quality [\u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e, \u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e17\u003c/span\u003e]. This study demonstrates that there is a 96.1% probability that a cassette indicating a positive result after being stored for one month was initially positive and a 93.7% probability that a cassette indicating negative at one month was initially negative, providing reasonable confidence that RDT cassettes can be used as a reference standard to compare with health facility records of RDT results. The findings from this study therefore generally support the use of malaria RDTs stored for up to one month under typical ambient conditions in health facilities for data validation purposes, in line with the approach implemented in Benin. However, the specific RDT product used will affect the validity of this approach and countries are recommended to review these results and take the RDT product into account when deciding whether to validate health facility data with stored RDT cassettes. Results from this study can also be used to determine the sample size that would be needed to evaluate other RDT products not observed in this study. Finally, further research assessing the stability of RDT results beyond one month (such as over a three-month period), could help determine the feasibility of longer storage durations, potentially providing a more practical and cost-effective frequency for routine data verification.\u003c/p\u003e \u003cp\u003eA key strength of this study was the large number of RDTs observed and the inclusion of multiple RDT products across three countries. The use of barcodes to track individual RDTs enabled systematic follow-up and image capture over a one-month period. However, a notable limitation was the absence of follow-up images for over 9,000 RDTs. While many of these were likely missing due to the time constraints of the study, there was evidence that RDTs initially interpreted as negative were more likely to be missing follow-up images. This may reflect a tendency among health facility staff to prioritize the retention of positive tests, although we do not believe this would systematically bias the assessment of result stability over time as the rate of change was similar. Additionally, faint test lines continue to pose a challenge for visual interpretation of RDTs. It is likely that a proportion of RDTs with faint lines were misclassified, which could have affected the results for certain RDT products, particularly those with pLDH lines.\u003c/p\u003e"},{"header":"CONCLUSIONS","content":"\u003cp\u003eThis study demonstrated that more than 95% of stored RDT cassettes retained their original result. Close to half of the changes may be due to reading RDTs before their period of development has concluded. Health officials should emphasize the need to follow manufacturer\u0026rsquo;s guidelines and ensure that tests are only interpreted as negative after the minimum development time has elapsed. Despite this finding, RDT cassettes stored for one month provide reliable information on the original RDT result and could be used as part of a strategy to evaluate surveillance data quality and the rational use of antimalarials in health facilities in Africa.\u003c/p\u003e"},{"header":"ABBREVIATIONS","content":"\u003ctable border=\"0\" cellspacing=\"0\" cellpadding=\"0\" width=\"636\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003eC\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eControl line\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003eCI\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eConfidence interval\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003eHRP2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eHistidine-rich protein 2\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003eLTFU\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eLost to follow-up\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003ePf\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u003cem\u003ePlasmodium falciparum\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003epLDH\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003ePlasmodium lactate dehydrogenase\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003ePMI\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003ePresident’s Malaria Initiative\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003eRDT\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eRapid diagnostic test\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003eT\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eTest line\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003eWHO\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eWorld Health Organization\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n\u003c/table\u003e"},{"header":"Declarations","content":"\u003cp\u003e\u003cstrong\u003eACKNOWLEDGEMENTS\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eMany staff members of the organizations implementing MaCRA made important contributions: Manfred Accrombessi, Hospice Avanon, Corneille Hueha (CREC, Benin); Hilary Okagbue, Evelyn Orya, Shiva Gab-Deedam, Olufisayo Bademosi, (Sydani Group, Nigeria); Anne Katahoire, Jane Frances Namuganga, Jenipher Musoke (CHDC, Uganda). We are grateful to the national malaria programs in each country for their support. We received excellent research support from Saadjo Sow (PMI Insights). Megan Littrell, Kim Vu and Taj Munson provided direction and administrative support to the PMI Insights project. Sasha Frade, Sam Smedinghoff, and the Audere development team (Audere, Seattle, WA USA) supported customisation of the HealthPulse application and provided the data sets used in this evaluation. We gratefully acknowledge the contributions of Audere\u0026rsquo;s artificial intelligence data creation and labeling teams at the Centre for HIV-AIDS Prevention Studies (South Africa) and Indivillage (India). We are grateful to Stephanie Zobrist (PATH) for comments on a draft of this paper.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eFUNDING\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThis evaluation was co-funded by PMI Insights and the Bill \u0026amp; Melinda Gates Foundation (INV-043942). PMI Insights was the global operational research and program evaluation project of the U.S. President\u0026rsquo;s Malaria Initiative (PMI). Funding for this evaluation is made possible by the generous support of the American people through the United States Agency for International Development (USAID) through cooperative agreement No. 7200AA20CA00031. The contents are the responsibility of the authors and do not necessarily reflect the views of USAID or the United States Government.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAUTHOR INFORMATION\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAuthors and Affiliations\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003e\u003cem\u003eCentre de Recherche Entomologique de Cotonou, Cotonou, Benin\u003c/em\u003e\u003c/p\u003e\n\u003cp\u003eIdelphonse Ahogni\u003c/p\u003e\n\u003cp\u003eCorine Ngufor\u003c/p\u003e\n\u003cp\u003e\u003cem\u003ePMI Insights Project/PATH, Geneva, Switzerland\u003c/em\u003e\u003c/p\u003e\n\u003cp\u003eKim A. Lindblade\u003c/p\u003e\n\u003cp\u003e\u003cem\u003eSydani Group, Abuja, Nigeria\u003c/em\u003e\u003c/p\u003e\n\u003cp\u003eEse Akpiroroh\u003c/p\u003e\n\u003cp\u003eSunday Atobatele\u003c/p\u003e\n\u003cp\u003e\u003cem\u003eChild Health and Development Centre, Makerere University, Kampala, Uganda\u003c/em\u003e\u003c/p\u003e\n\u003cp\u003eArthur Mpimbaza\u003c/p\u003e\n\u003cp\u003eNelson Ssewante\u003c/p\u003e\n\u003cp\u003e\u003cem\u003eAudere, Seattle, WA USA\u003c/em\u003e\u003c/p\u003e\n\u003cp\u003eShawna Cooper\u003c/p\u003e\n\u003cp\u003e\u003cem\u003eU.S. President\u0026rsquo;s Malaria Initiative, United States Agency for International Development, Washington, DC USA\u003c/em\u003e\u003c/p\u003e\n\u003cp\u003eKevin Griffith\u003c/p\u003e\n\u003cp\u003eMichael Humes\u003c/p\u003e\n\u003cp\u003e\u003cem\u003eNational Malaria Control Division, Ministry of Health, Kampala, Uganda\u003c/em\u003e\u003c/p\u003e\n\u003cp\u003eBosco Agaba\u003c/p\u003e\n\u003cp\u003e\u003cem\u003eProgramme National de Lutte contre le Paludisme, Cotonou, Benin\u003c/em\u003e\u003c/p\u003e\n\u003cp\u003eAugustin Kpemasse\u003c/p\u003e\n\u003cp\u003e\u003cem\u003eNational Malaria Elimination Programme, Abuja, Nigeria\u003c/em\u003e\u003c/p\u003e\n\u003cp\u003eOnyebuchi Okoro\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eContributions\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003e\u003cem\u003eKL, MH\u0026nbsp;\u003c/em\u003eand\u003cem\u003e\u0026nbsp;KG conceived and designed the evaluation with contributions from CN, AM and SA. CN, \u0026nbsp;SA, AM, IA, NS, EA, AK, OO and BA oversaw data collection activities. SC oversaw the development of the HealthPulse application used in the study. CN and KL drafted the manuscript. KL analyzed the data. All authors critically reviewed the manuscript. All authors read and approved the final manuscript.\u003c/em\u003e\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eEthical approval and consent to participate\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eEthical approval was obtained from: the Comit\u0026eacute; National d\u0026rsquo;Ethique pour la Recherche en Sant\u0026eacute; (Benin); the National Health Research Ethics Committee (Nigeria); the Oyo State Health Research Ethics Committee (Nigeria); the Sokoto State Health Research Ethics Committee (Nigeria); the Uganda National Council on Science and Technology (Uganda); the Vector Control Division Research and Ethics Committee (Uganda); and the WGC IRB in the USA. All participants provided written, informed consent to participate.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eConsent for publication\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eNot applicable.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAvailability of data and materials\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe datasets used and/or analyzed during the current evaluation can be provided by the corresponding author on reasonable request.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eCompeting interests\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe authors declare that they have no competing interests.\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\n \u003cli\u003eFountain A, Ye Y, Roca-Feltrer A, Rowe AK, Camara A, Fofana A, et al. Surveillance as a Core Intervention to Strengthen Malaria Control Programs in Moderate to High Transmission Settings. Am J Trop Med Hyg. 2023;108:8\u0026ndash;13.\u003c/li\u003e\n \u003cli\u003eKavanaugh MJ, Azzam SE, Rockabrand DM. Malaria Rapid Diagnostic Tests: Literary Review and Recommendation for a Quality Assurance, Quality Control Algorithm. Diagnostics. 2021;11:768.\u003c/li\u003e\n \u003cli\u003eUNITAID. Malaria diagnostics market and technology landscape [Internet]. Geneva, Switzerland: World Health Organization; 2022. Available from: https://unitaid.org/uploads/Malaria-Diagnostics-Market-and-Technology-Landscape.pdf\u003c/li\u003e\n \u003cli\u003eGillet P, Mukadi P, Vernelen K, Van Esbroeck M, Muyembe J-J, Bruggeman C, et al. External quality assessment on the use of malaria rapid diagnostic tests in a non-endemic setting. Malar J. 2010;9:359.\u003c/li\u003e\n \u003cli\u003eGatton ML, Rees-Channer RR, Glenn J, Barnwell JW, Cheng Q, Chiodini PL, et al. Pan-Plasmodium band sensitivity for Plasmodium falciparum detection in combination malaria rapid diagnostic tests and implications for clinical management. Malar J. 2015;14:115.\u003c/li\u003e\n \u003cli\u003eWHO. World Malaria Report 2024. Geneva, Switzerland: World Health Organization; 2024.\u003c/li\u003e\n \u003cli\u003eDavlantes E, Camara A, Guilavogui T, Fofana, Balde. Quality of Malaria Case Management and Reporting at Public Health Facilities in Six Health Districts in Guinea, 2018. Am J Trop Med Hyg. 2019;101:148\u0026ndash;56.\u003c/li\u003e\n \u003cli\u003eZurovac D, Githinji S, Memusi D, Kigen S, Machini B. Major Improvements in the Quality of Malaria Case-Management under the \u0026ldquo;Test and Treat\u0026rdquo; Policy in Kenya. PLoS One. 2014;9.\u003c/li\u003e\n \u003cli\u003ePlucinski, Ferreira. Evaluating malaria case management at public health facilities in two provinces in Angola. Malar J. 2017;16.\u003c/li\u003e\n \u003cli\u003eLindblade, KA, Mpimbaza, A, Ngufor, C, Yavo, W, Atobatele, S, Akpiroroh, E, et al. Assessing the accuracy of the recording and reporting of malaria rapid diagnostic test results in four African countries: Methods and key results. under review.\u003c/li\u003e\n \u003cli\u003eNgufor, C, Ahogni, I. Documentation of the monthly validation of malaria rapid diagnostic test (RDTs) results in Benin. Cotonou, Benin; 2025 Apr.\u003c/li\u003e\n \u003cli\u003eWHO. WHO prequalification of in vitro diagnostics programme: AdvDx Malaria Pf Rapid Malaria Ag Detection Test [Internet]. Geneva, Switzerland: WHO; 2019. Available from: https://extranet.who.int/prequal/sites/default/files/whopr_files/PQDx_0345-101-00_AdvDxMalariaDetectionTest_v2.pdf\u003c/li\u003e\n \u003cli\u003eDorai-Raj, S. Package \u0026ldquo;binom\u0026rdquo; [Internet]. 2022. Available from: https://cran.r-project.org/web/packages/binom/binom.pdf\u003c/li\u003e\n \u003cli\u003eMouatcho JC, Goldring JPD. Malaria rapid diagnostic tests: challenges and prospects. J Med Microbiol. 2013;62:1491\u0026ndash;505.\u003c/li\u003e\n \u003cli\u003eVeron V, Carme B. RECOVERY AND USE OF PLASMODIUM DNA FROM MALARIA RAPID DIAGNOSTIC TESTS. Am J Trop Med Hyg. 2006;74:941\u0026ndash;3.\u003c/li\u003e\n \u003cli\u003eIshengoma DS, Lwitiho S, Madebe RA, Nyagonde N, Persson O, Vestergaard LS, et al. Using rapid diagnostic tests as source of malaria parasite DNA for molecular analyses in the era of declining malaria prevalence. Malar J. 2011;10:6.\u003c/li\u003e\n \u003cli\u003eAyandipo EO, Fagbola M, Gbolade A, Okpokpolom JT, Ojo A, Abikoye O, et al. Decline in malaria test positivity rates following capacity building and archiving of malaria rapid diagnostic test cassettes in Oyo State, Nigeria: a retrospective review of records. Malar J. 2025;24:132.\u003c/li\u003e\n\u003c/ol\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":false,"highlight":"","institution":"","isAcceptedByJournal":true,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"malaria-journal","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"malj","sideBox":"Learn more about [Malaria Journal](http://malariajournal.biomedcentral.com/)","snPcode":"12936","submissionUrl":"https://submission.nature.com/new-submission/12936/3","title":"Malaria Journal","twitterHandle":"@malariajournal","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"em","reportingPortfolio":"BMC/SO AJ","inReviewEnabled":true,"inReviewRevisionsEnabled":true},"keywords":"malaria, rapid diagnostic tests, surveillance, Benin, Nigeria, Uganda, stability ","lastPublishedDoi":"10.21203/rs.3.rs-6760274/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-6760274/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003e\u003cstrong\u003eBackground\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eRapid diagnostic tests (RDTs) have improved malaria case management by enabling point-of-care confirmation of infection, particularly in low-resource settings. In addition to clinical use, RDT results recorded in health facility registers are a critical component of national malaria surveillance systems. Recently, national programs have explored using stored RDT cassettes to validate register data. However, manufacturers caution that results should be read within 15 - 30 minutes, raising concerns about result validity after this period. This study evaluated the stability of RDT results over a one-month period to assess whether stored cassettes can reliably reflect initial test outcomes.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eMethods\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eWe conducted a prospective, observational study in 48 health facilities across Benin, Nigeria, and Uganda from June to September 2023. A digital artificial intelligence (AI)-powered RDT reader (HealthPulse, Audere, Seattle WA USA) was used to photograph RDTs immediately after interpretation by health workers and again at one week and one month. Images were independently interpreted by a trained panel, with results categorized as positive, negative, invalid, or indeterminate. Only RDTs with valid interpretations at all three time points were included in the final analysis. Positive and negative predictive values (PPV and NPV) were calculated to measure the accuracy of results from stored RDTs relative to the initial interpretation.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eResults\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eOut of 54,251 RDTs captured, 45,155 (83.2%) met inclusion criteria. At one month, 95.1% of initially positive RDTs remained positive, and 95.3% of initially negative RDTs remained negative. The PPV of a positive result at one month was 96.3% (95% CI 96.1, 96.5), while the NPV of a negative result was 93.8% (95% CI 93.4, 94.1). Most result changes occurred within the first week. Faint lines were associated with higher rates of change in both directions; 26.8% changing from positive to negative and 48.1% changing from negative to positive. Stability of results also varied across RDT products and specific test lines.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eConclusions\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eStored RDT cassettes maintain high result stability over one month and can serve as a reliable reference to verify health facility records. Result changes were linked to premature interpretation, faint lines or product- or line-specific characteristics. Adherence to manufacturer-recommended read times may reduce the proportion of RDTs that change from negative to positive. These findings support the utility of stored RDTs in improving data quality and rational antimalarial use in malaria-affected settings.\u003c/p\u003e","manuscriptTitle":"Are malaria rapid diagnostic test results stable over time to support verification of surveillance data?","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2025-06-17 14:49:15","doi":"10.21203/rs.3.rs-6760274/v1","editorialEvents":[{"type":"communityComments","content":0},{"type":"decision","content":"Revision requested","date":"2025-08-01T05:32:01+00:00","index":"","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2025-07-25T21:55:47+00:00","index":"hide","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2025-07-24T20:24:47+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"55932173306428118317757807329594397135","date":"2025-07-07T19:06:21+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"140388210349969044940937239329816917813","date":"2025-07-04T10:23:31+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"111654000714135264723138006526587238559","date":"2025-06-16T12:25:06+00:00","index":"hide","fulltext":""},{"type":"reviewersInvited","content":"","date":"2025-06-13T04:54:16+00:00","index":"","fulltext":""},{"type":"editorAssigned","content":"","date":"2025-05-27T15:51:13+00:00","index":"","fulltext":""},{"type":"checksComplete","content":"","date":"2025-05-27T15:48:55+00:00","index":"","fulltext":""},{"type":"submitted","content":"Malaria Journal","date":"2025-05-27T14:03:14+00:00","index":"","fulltext":""}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"malaria-journal","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"malj","sideBox":"Learn more about [Malaria Journal](http://malariajournal.biomedcentral.com/)","snPcode":"12936","submissionUrl":"https://submission.nature.com/new-submission/12936/3","title":"Malaria Journal","twitterHandle":"@malariajournal","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"em","reportingPortfolio":"BMC/SO AJ","inReviewEnabled":true,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"5a2d6eda-d622-48b3-8b3e-a69ec4116274","owner":[],"postedDate":"June 17th, 2025","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"published-in-journal","subjectAreas":[],"tags":[],"updatedAt":"2025-10-27T16:24:41+00:00","versionOfRecord":{"articleIdentity":"rs-6760274","link":"https://doi.org/10.1186/s12936-025-05595-0","journal":{"identity":"malaria-journal","isVorOnly":false,"title":"Malaria Journal"},"publishedOn":"2025-10-22 16:16:13","publishedOnDateReadable":"October 22nd, 2025"},"versionCreatedAt":"2025-06-17 14:49:15","video":"","vorDoi":"10.1186/s12936-025-05595-0","vorDoiUrl":"https://doi.org/10.1186/s12936-025-05595-0","workflowStages":[]},"version":"v1","identity":"rs-6760274","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-6760274","identity":"rs-6760274","version":["v1"]},"buildId":"8U1c8b4HqxoKbykW_rLl7","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}