Emily Dantzer

Background: In the Greater Mekong Subregion, militaries constitute a critical but often underserved malaria transmission reservoir, given their high mobility, deployment to endemic areas, and frequent exposure to vectors. In Thailand, the Royal Thai Army (RTA) is a key risk population, yet their malaria practices, perceptions, and the scale and scope of coordination with the Ministry of Public Health (MoPH) are not well understood. A joint military-civilian research team conducted a rapid formative assessment in Sisaket Province, a persistent transmission hotspot, to characterize the unique RTA risk profile and identify opportunities to strengthen RTA-MoPH coordination on malaria elimination efforts. Methods: Using a mixed-methods design, the research team conducted a five-year (2016–2020) retrospective analysis of Sisaket’s malaria case and program response data, alongside 16 focus group discussions (FGDs) and 17 key informant interviews (KIIs) with RTA and MoPH respondents across all military ranks and health system levels in the province’s three highest-burden districts (Kantharalak, Khun Han, Phu Sing). Qualitative data were collected between December 2021-January 2022 and thematically analyzed using an inductive content analysis approach. Quantitative data were descriptively analyzed using statistical methods to characterize the RTA malaria risk profile and identify and compare recent trends in program response between military and non-military populations in Sisaket Province. Results: 432 military malaria cases were reported in Sisaket between 2016 and 2020, accounting for 18% of the province’s total 2425 cases (with the proportion ranging from 14 to 30% annually). All military malaria cases were male, with a median age of 28 years. 96% of military cases were diagnosed and treated at MoPH facilities, with 41% of cases classified as indigenous and 40% as imported from abroad. Qualitative data were collected from a total of 116 respondents (72 RTA, 44 MoPH) through 16 FGDs and 17 KIIs. Malaria prevention and treatment practices and perceptions among soldiers were largely consistent across the three study districts. RTA-MoPH coordination occurred at all levels through both formal and informal channels and was reported to be improving and expanding by some respondents. Though several areas could benefit from increased collaboration: patient follow-up, border control efforts, vector control, and further capacitation of military medics. The RTA and MoPH both expressed strong interest in strengthening military-civilian coordination; though to better enable this, several identified challenges may need to be addressed, including restricted MoPH access to military sites, lack of continuity in relationships due to frequent RTA rotations, communication constraints, and broader differences in military versus civilian operating procedures. Conclusion: Strengthening RTA-MoPH coordination towards successfully interrupting malaria transmission in Thailand may require formalizing and standardizing some joint operating procedures, increasing communication and military-civilian touchpoints, and further capacitating the RTA to carry out malaria prevention, diagnosis and treatment, and patient follow-up activities. For Thailand to achieve its goal of nationwide malaria elimination, it is crucial that the RTA as a high-risk population be fully engaged in the country’s malaria control and response efforts.

Lao People’s Democratic Republic (Lao PDR) has made significant progress in reducing malaria in recent years. In the Greater Mekong Subregion, forest-going is often a risk factor contributing to continuing malaria transmission. This study assessed forest-going and other potential risk factors for malaria cases in Champasak Province, Lao PDR. Routine passive surveillance data from August 2017 to December 2018 were extracted from health facilities in three districts for a case-control study; at the time of presentation, all fever cases were asked to report any recent forest travel. Multivariable logistic regression was used to assess the relationship between forest-going and malaria infection while controlling for other covariates. Of 2933 fever cases with data available on forest-sleeping and malaria diagnosis from 25 health facilities, 244 (8%) tested positive (cases), and 2689 (92%) tested negative (controls). Compared with spending 0–2 nights in the forest, spending 3–7 nights in the forest was associated with 9.7 times the odds of having a malaria infection (95% CI: 4.67–20.31, p < 0.001) when adjusting for gender, occupation, and season. Forest-going, especially longer trips, is associated with increased risk for confirmed symptomatic malaria in southern Lao PDR, and appropriate and targeted intervention efforts are needed to protect this high-risk population.

Verbal autopsy (VA) is recognized as the only feasible alternative to comprehensive medical certification of deaths in settings with no or unreliable vital registration systems. However, a barrier to its use by national registration systems has been the amount of time and cost needed for data collection. Therefore, a short VA instrument (VAI) is needed. In this paper we describe a shortened version of the VAI developed for the Population Health Metrics Research Consortium (PHMRC) Gold Standard Verbal Autopsy Validation Study using a systematic approach. We used data from the PHMRC validation study. Using the Tariff 2.0 method, we first established a rank order of individual questions in the PHMRC VAI according to their importance in predicting causes of death. Second, we reduced the size of the instrument by dropping questions in reverse order of their importance. We assessed the predictive performance of the instrument as questions were removed at the individual level by calculating chance-corrected concordance and at the population level with cause-specific mortality fraction (CSMF) accuracy. Finally, the optimum size of the shortened instrument was determined using a first derivative analysis of the decline in performance as the size of the VA instrument decreased for adults, children, and neonates. The full PHMRC VAI had 183, 127, and 149 questions for adult, child, and neonatal deaths, respectively. The shortened instrument developed had 109, 69, and 67 questions, respectively, representing a decrease in the total number of questions of 40-55 %. The shortened instrument, with text, showed non-significant declines in CSMF accuracy from the full instrument with text of 0.4 %, 0.0 %, and 0.6 % for the adult, child, and neonatal modules, respectively. We developed a shortened VAI using a systematic approach, and assessed its performance when administered using hand-held electronic tablets and analyzed using Tariff 2.0. The length of a VA questionnaire was shortened by almost 50 % without a significant drop in performance. The shortened VAI developed reduces the burden of time and resources required for data collection and analysis of cause of death data in civil registration systems.

Reliable data on the distribution of causes of death (COD) in a population are fundamental to good public health practice. In the absence of comprehensive medical certification of deaths, the only feasible way to collect essential mortality data is verbal autopsy (VA). The Tariff Method was developed by the Population Health Metrics Research Consortium (PHMRC) to ascertain COD from VA information. Given its potential for improving information about COD, there is interest in refining the method. We describe the further development of the Tariff Method. This study uses data from the PHMRC and the National Health and Medical Research Council (NHMRC) of Australia studies. Gold standard clinical diagnostic criteria for hospital deaths were specified for a target cause list. VAs were collected from families using the PHMRC verbal autopsy instrument including health care experience (HCE). The original Tariff Method (Tariff 1.0) was trained using the validated PHMRC database for which VAs had been collected for deaths with hospital records fulfilling the gold standard criteria (validated VAs). In this study, the performance of Tariff 1.0 was tested using VAs from household surveys (community VAs) collected for the PHMRC and NHMRC studies. We then corrected the model to account for the previous observed biases of the model, and Tariff 2.0 was developed. The performance of Tariff 2.0 was measured at individual and population levels using the validated PHMRC database. For median chance-corrected concordance (CCC) and mean cause-specific mortality fraction (CSMF) accuracy, and for each of three modules with and without HCE, Tariff 2.0 performs significantly better than the Tariff 1.0, especially in children and neonates. Improvement in CSMF accuracy with HCE was 2.5 %, 7.4 %, and 14.9 % for adults, children, and neonates, respectively, and for median CCC with HCE it was 6.0 %, 13.5 %, and 21.2 %, respectively. Similar levels of improvement are seen in analyses without HCE. Tariff 2.0 addresses the main shortcomings of the application of the Tariff Method to analyze data from VAs in community settings. It provides an estimation of COD from VAs with better performance at the individual and population level than the previous version of this method, and it is publicly available for use.

Monitoring progress with disease and injury reduction in many populations will require widespread use of verbal autopsy (VA). Multiple methods have been developed for assigning cause of death from a VA but their application is restricted by uncertainty about their reliability. We investigated the validity of five automated VA methods for assigning cause of death: InterVA-4, Random Forest (RF), Simplified Symptom Pattern (SSP), Tariff method (Tariff), and King-Lu (KL), in addition to physician review of VA forms (PCVA), based on 12,535 cases from diverse populations for which the true cause of death had been reliably established. For adults, children, neonates and stillbirths, performance was assessed separately for individuals using sensitivity, specificity, Kappa, and chance-corrected concordance (CCC) and for populations using cause specific mortality fraction (CSMF) accuracy, with and without additional diagnostic information from prior contact with health services. A total of 500 train-test splits were used to ensure that results are robust to variation in the underlying cause of death distribution. Three automated diagnostic methods, Tariff, SSP, and RF, but not InterVA-4, performed better than physician review in all age groups, study sites, and for the majority of causes of death studied. For adults, CSMF accuracy ranged from 0.764 to 0.770, compared with 0.680 for PCVA and 0.625 for InterVA; CCC varied from 49.2% to 54.1%, compared with 42.2% for PCVA, and 23.8% for InterVA. For children, CSMF accuracy was 0.783 for Tariff, 0.678 for PCVA, and 0.520 for InterVA; CCC was 52.5% for Tariff, 44.5% for PCVA, and 30.3% for InterVA. For neonates, CSMF accuracy was 0.817 for Tariff, 0.719 for PCVA, and 0.629 for InterVA; CCC varied from 47.3% to 50.3% for the three automated methods, 29.3% for PCVA, and 19.4% for InterVA. The method with the highest sensitivity for a specific cause varied by cause. Physician review of verbal autopsy questionnaires is less accurate than automated methods in determining both individual and population causes of death. Overall, Tariff performs as well or better than other methods and should be widely applied in routine mortality surveillance systems with poor cause of death certification practices.

Verbal autopsy methods are critically important for evaluating the leading causes of death in populations without adequate vital registration systems. With a myriad of analytical and data collection approaches, it is essential to create a high quality validation dataset from different populations to evaluate comparative method performance and make recommendations for future verbal autopsy implementation. This study was undertaken to compile a set of strictly defined gold standard deaths for which verbal autopsies were collected to validate the accuracy of different methods of verbal autopsy cause of death assignment. Data collection was implemented in six sites in four countries: Andhra Pradesh, India; Bohol, Philippines; Dar es Salaam, Tanzania; Mexico City, Mexico; Pemba Island, Tanzania; and Uttar Pradesh, India. The Population Health Metrics Research Consortium (PHMRC) developed stringent diagnostic criteria including laboratory, pathology, and medical imaging findings to identify gold standard deaths in health facilities as well as an enhanced verbal autopsy instrument based on World Health Organization (WHO) standards. A cause list was constructed based on the WHO Global Burden of Disease estimates of the leading causes of death, potential to identify unique signs and symptoms, and the likely existence of sufficient medical technology to ascertain gold standard cases. Blinded verbal autopsies were collected on all gold standard deaths. Over 12,000 verbal autopsies on deaths with gold standard diagnoses were collected (7,836 adults, 2,075 children, 1,629 neonates, and 1,002 stillbirths). Difficulties in finding sufficient cases to meet gold standard criteria as well as problems with misclassification for certain causes meant that the target list of causes for analysis was reduced to 34 for adults, 21 for children, and 10 for neonates, excluding stillbirths. To ensure strict independence for the validation of methods and assessment of comparative performance, 500 test-train datasets were created from the universe of cases, covering a range of cause-specific compositions. This unique, robust validation dataset will allow scholars to evaluate the performance of different verbal autopsy analytic methods as well as instrument design. This dataset can be used to inform the implementation of verbal autopsies to more reliably ascertain cause of death in national health information systems.