Dataset Information

Accounting for Healthcare-Seeking Behaviours and Testing Practices in Real-Time Influenza Forecasts.

ABSTRACT: For diseases such as influenza, where the majority of infected persons experience mild (if any) symptoms, surveillance systems are sensitive to changes in healthcare-seeking and clinical decision-making behaviours. This presents a challenge when trying to interpret surveillance data in near-real-time (e.g., to provide public health decision-support). Australia experienced a particularly large and severe influenza season in 2017, perhaps in part due to: (a) mild cases being more likely to seek healthcare; and (b) clinicians being more likely to collect specimens for reverse transcription polymerase chain reaction (RT-PCR) influenza tests. In this study, we used weekly Flutracking surveillance data to estimate the probability that a person with influenza-like illness (ILI) would seek healthcare and have a specimen collected. We then used this estimated probability to calibrate near-real-time seasonal influenza forecasts at each week of the 2017 season, to see whether predictive skill could be improved. While the number of self-reported influenza tests in the weekly surveys are typically very low, we were able to detect a substantial change in healthcare seeking behaviour and clinician testing behaviour prior to the high epidemic peak. Adjusting for these changes in behaviour in the forecasting framework improved predictive skill. Our analysis demonstrates a unique value of community-level surveillance systems, such as Flutracking, when interpreting traditional surveillance data. These methods are also applicable beyond the Australian context, as similar community-level surveillance systems operate in other countries.

SUBMITTER: Moss R

PROVIDER: S-EPMC6473244 | biostudies-literature | 2019 Jan

REPOSITORIES: biostudies-literature

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Accounting for Healthcare-Seeking Behaviours and Testing Practices in Real-Time Influenza Forecasts.

Moss Robert R Zarebski Alexander E AE Carlson Sandra J SJ McCaw James M JM

Tropical medicine and infectious disease 20190111 1

For diseases such as influenza, where the majority of infected persons experience mild (if any) symptoms, surveillance systems are sensitive to changes in healthcare-seeking and clinical decision-making behaviours. This presents a challenge when trying to interpret surveillance data in near-real-time (e.g., to provide public health decision-support). Australia experienced a particularly large and severe influenza season in 2017, perhaps in part due to: (a) mild cases being more likely to seek he ...[more]

PMID: 30641917

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Project description:Seasonal influenza results in substantial annual morbidity and mortality in the United States and worldwide. Accurate forecasts of key features of influenza epidemics, such as the timing and severity of the peak incidence in a given season, can inform public health response to outbreaks. As part of ongoing efforts to incorporate data and advanced analytical methods into public health decision-making, the United States Centers for Disease Control and Prevention (CDC) has organized seasonal influenza forecasting challenges since the 2013/2014 season. In the 2017/2018 season, 22 teams participated. A subset of four teams created a research consortium called the FluSight Network in early 2017. During the 2017/2018 season they worked together to produce a collaborative multi-model ensemble that combined 21 separate component models into a single model using a machine learning technique called stacking. This approach creates a weighted average of predictive densities where the weight for each component is determined by maximizing overall ensemble accuracy over past seasons. In the 2017/2018 influenza season, one of the largest seasonal outbreaks in the last 15 years, this multi-model ensemble performed better on average than all individual component models and placed second overall in the CDC challenge. It also outperformed the baseline multi-model ensemble created by the CDC that took a simple average of all models submitted to the forecasting challenge. This project shows that collaborative efforts between research teams to develop ensemble forecasting approaches can bring measurable improvements in forecast accuracy and important reductions in the variability of performance from year to year. Efforts such as this, that emphasize real-time testing and evaluation of forecasting models and facilitate the close collaboration between public health officials and modeling researchers, are essential to improving our understanding of how best to use forecasts to improve public health response to seasonal and emerging epidemic threats.

Project description:BackgroundAmong parasitic infections, schistosomiasis ranks second after malaria in terms of worldwide morbidity. Despite efforts to contain transmission, more than 230 million people are infected, of which 85% live in Sub-Saharan Africa. While the epidemiologic characteristics of schistosomiasis have been extensively studied across endemic settings, social factors have been paid less attention. The current study assesses community knowledge of schistosomiasis causes, transmission, signs, symptoms and prevention, as well as healthcare-seeking behaviours in two West African settings, with the aim of strengthening schistosomiasis control interventions.MethodsFrom August 2014 to June 2015, we conducted two cross-sectional surveys in Korhogo, Côte d'Ivoire and Kaédi, Mauritania. We applied a questionnaire to collect quantitative data at the household level in Korhogo (n?=?1456) and Kaédi (n?=?1453). Focus group discussions (Korhogo: n?=?32, Kaédi: n?=?32) and participatory photography (photovoice) (Korhogo: n?=?16, Kaédi: n?=?16) were conducted within the communities to gather qualitative data. In addition, semi-structured interviews were used to discuss with key informants from control programmes, non-governmental organizations and health districts (Korhogo: n?=?8, Kaédi: n?=?7).ResultsThe study demonstrated that schistosomiasis is not well known by the communities; 64.1% claimed to know the causes of the disease, but the reality is different. This knowledge is more from cultural than biomedical source. It was observed that social construction of the disease is different from the biomedical definition. In Korhogo, schistosomiasis was often associated with several other diseases, notably stomach ulcer and gonorrhoea. The populations believe that schistosomiasis is caused by exposure to goat or dog urine in the environment. In Kaédi, schistosomiasis is considered as a disease transmitted by environmenal elements such as sunshine and dirty water. In both settings, the care-seeking pathways were found to be strongly influenced by local customs and self-medication acquired from the informal sector.ConclusionsThis study revealed that knowledge about the aetiology, transmission, symptoms, prevention and treatment of schistosomiasis among the populations in Korhogo and Kaédi is based on their local culture. Deep-rooted habits could therefore pose a significant obstacle to the elimination of schistosomiasis.

Project description:OBJECTIVES:We aimed to analyse the healthcare seeking behaviours (HSB) of populations with different migration status and examine the relationship between migration status and HSB. DESIGN:A secondary analysis of cross-sectional data of China Labor-force Dynamics Survey wave 2016, which was conducted by the Sun Yat-sen University every 2 years since 2010. SETTING:29 provinces/cities/autonomous regions in mainland China, excluding Tibet and Hainan province. PARTICIPANTS:3522 respondents in a condition in the previous 2 weeks or during the last 12 months were analysed. MAIN OUTCOME MEASURES:The migration status included internal migrants (IMs), returned population, rural residents and urban residents. The HSB including health services utilisation and choice of health facilities for their health services. Multivariable logistic regressions were used to examine the association between migration status and HSB. RESULTS:2193 and 1898 of 3522 respondents were in need of outpatient services (having a condition in the previous 2 weeks) or inpatient services (being asked to use the inpatient services during the last 12 months). Compared with urban residents, the returned population (OR=0.66, 95% CIs 0.49 to 0.89) and rural residents (OR=0.65, 95% CIs 0.51 to 0.82) were less likely to have an unmet need for outpatient services. The latter tended to choose lower-level hospitals, while the IMs preferred the lowest-level healthcare facilities for health services. After controlling for confounding factors, the results above did not reach any statistical significance. CONCLUSION:Association between migration status and HSB might be through enabling resource and needs. Returned population, IMs and rural residents tended to choose low-level hospitals for their health service needs, but this association was mainly associated with their poorer social and economic resources. Improving their social and economic resources would be helpful for reducing the barriers of HSB.

Project description:BackgroundImplementing effective self-care practices for non-communicable diseases (NCD) prevents complications and morbidity. However, scanty evidence exists among patients in rural sub-Saharan Africa (SSA). We sought to describe and compare existing self-care practices among patients with hypertension (HTN) and diabetes (DM) in rural Uganda.MethodsBetween April and August 2019, we executed a cross-sectional investigation involving 385 adult patients diagnosed with HTN and/or DM. These participants were systematically randomly selected from three outpatient NCD clinics in the Nakaseke district. Data collection was facilitated using a structured survey that inquired about participants' healthcare-seeking patterns, access to self-care services, education on self-care, medication compliance, and overall health-related quality of life. We utilized Chi-square tests and logistic regression analyses to discern disparities in self-care practices, education, and healthcare-seeking actions based on the patient's conditions.ResultsOf the 385 participants, 39.2% had only DM, 36.9% had only HTN, and 23.9% had both conditions (HTN/DM). Participants with DM or both conditions reported more clinic visits in the past year than those with only HTN (P = 0.005). Similarly, most DM-only and HTN/DM participants monitored their weight monthly, unlike those with only HTN (P<0.0001). Participants with DM or HTN/DM were more frequently educated about their health condition(s), dietary habits, and weight management than those with only HTN. Specifically, education about their conditions yielded adjusted odds ratios (aOR) of 5.57 for DM-only and 4.12 for HTN/DM. Similarly, for diet, aORs were 2.77 (DM-only) and 4.21 (HTN/DM), and for weight management, aORs were 3.62 (DM-only) and 4.02 (HTN/DM). Medication adherence was notably higher in DM-only participants (aOR = 2.19). Challenges in self-care were significantly more reported by women (aOR = 2.07) and those above 65 years (aOR = 5.91), regardless of their specific condition(s).ConclusionCompared to rural Ugandans with HTN-only, participants with DM had greater utilization of healthcare services, exposure to self-care education, and adherence to medicine and self-monitoring behaviors. These findings should inform ongoing efforts to improve and integrate NCD service delivery in rural SSA.

Dataset Information

Accounting for Healthcare-Seeking Behaviours and Testing Practices in Real-Time Influenza Forecasts.

Publications

Accounting for Healthcare-Seeking Behaviours and Testing Practices in Real-Time Influenza Forecasts.

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