Reporting Delays and the Incidence of AIDS

Increasing situational awareness through nowcasting of the reproduction number

Background

The time-varying reproduction number R is a critical variable for situational awareness during infectious disease outbreaks; however, delays between infection and reporting of cases hinder its accurate estimation in real-time. A number of nowcasting methods, leveraging available information on data consolidation delays, have been proposed to mitigate this problem.

Methods

In this work, we retrospectively validate the use of a nowcasting algorithm during 18 months of the COVID-19 pandemic in Italy by quantitatively assessing its performance against standard methods for the estimation of R.

Results

Nowcasting significantly reduced the median lag in the estimation of R from 13 to 8 days, while concurrently enhancing accuracy. Furthermore, it allowed the detection of periods of epidemic growth with a lead of between 6 and 23 days.

Conclusions

Nowcasting augments epidemic awareness, empowering better informed public health responses.

Timely epidemic monitoring in the presence of reporting delays: anticipating the COVID-19 surge in New York City, September 2020

Background

During a fast-moving epidemic, timely monitoring of case counts and other key indicators of disease spread is critical to an effective public policy response.

Methods

We describe a nonparametric statistical method, originally applied to the reporting of AIDS cases in the 1980s, to estimate the distribution of reporting delays of confirmed COVID-19 cases in New York City during the late summer and early fall of 2020.

Results

During August 15–September 26, the estimated mean delay in reporting was 3.3 days, with 87% of cases reported by 5 days from diagnosis. Relying upon the estimated reporting-delay distribution, we projected COVID-19 incidence during the most recent 3 weeks as if each case had instead been reported on the same day that the underlying diagnostic test had been performed. Applying our delay-corrected estimates to case counts reported as of September 26, we projected a surge in new diagnoses that had already occurred but had yet to be reported. Our projections were consistent with counts of confirmed cases subsequently reported by November 7.

Conclusion

The projected estimate of recently diagnosed cases could have had an impact on timely policy decisions to tighten social distancing measures. While the recent advent of widespread rapid antigen testing has changed the diagnostic testing landscape considerably, delays in public reporting of SARS-CoV-2 case counts remain an important barrier to effective public health policy.

Supplementary Information

The online version contains supplementary material available at 10.1186/s12889-022-13286-7.

A Note on Observation Processes in Epidemic Models

Many disease models focus on characterizing the underlying transmission mechanism but make simple, possibly naive assumptions about how infections are reported. In this note, we use a simple deterministic Susceptible-Infected-Removed (SIR) model to compare two common assumptions about disease incidence reports: Individuals can report their infection as soon as they become infected or as soon as they recover. We show that incorrect assumptions about the underlying observation processes can bias estimates of the basic reproduction number and lead to overly narrow confidence intervals.

Multivariate hierarchical frameworks for modeling delayed reporting in count data

In many fields and applications, count data can be subject to delayed reporting. This is where the total count, such as the number of disease cases contracted in a given week, may not be immediately available, instead arriving in parts over time. For short‐term decision making, the statistical challenge lies in predicting the total count based on any observed partial counts, along with a robust quantification of uncertainty. We discuss previous approaches to modeling delayed reporting and present a multivariate hierarchical framework where the count generating process and delay mechanism are modeled simultaneously in a flexible way. This framework can also be easily adapted to allow for the presence of underreporting in the final observed count. To illustrate our approach and to compare it with existing frameworks, we present a case study of reported dengue fever cases in Rio de Janeiro. Based on both within‐sample and out‐of‐sample posterior predictive model checking and arguments of interpretability, adaptability, and computational efficiency, we discuss the relative merits of different approaches.

Potential adjustment methodology for missing data and reporting delay in the HIV Surveillance System, European Union/European Economic Area, 2015

Accurate case-based surveillance data remain the key data source for estimating HIV burden and monitoring prevention efforts in Europe. We carried out a literature review and exploratory analysis of surveillance data regarding two crucial issues affecting European surveillance for HIV: missing data and reporting delay. Initial screening showed substantial variability of these data issues, both in time and across countries. In terms of missing data, the CD4+ cell count is the most problematic variable because of the high proportion of missing values. In 20 of 31 countries of the European Union/European Economic Area (EU/EEA), CD4+ counts are systematically missing for all or some years. One of the key challenges related to reporting delays is that countries undertake specific one-off actions in effort to capture previously unreported cases, and that these cases are subsequently reported with excessive delays. Slightly different underlying assumptions and effectively different models may be required for individual countries to adjust for missing data and reporting delays. However, using a similar methodology is recommended to foster harmonisation and to improve the accuracy and usability of HIV surveillance data at national and EU/EEA levels.

Data Mining in HIV-AIDS Surveillance System : Application to Portuguese Data

The Human Immunodeficiency Virus (HIV) is an infectious agent that attacks the immune system cells. Without a strong immune system, the body becomes very susceptible to serious life threatening opportunistic diseases. In spite of the great progresses on medication and prevention over the last years, HIV infection continues to be a major global public health issue, having claimed more than 36 million lives over the last 35 years since the recognition of the disease. Monitoring, through registries, of HIV-AIDS cases is vital to assess general health care needs and to support long-term health-policy control planning. Surveillance systems are therefore established in almost all developed countries. Typically, this is a complex system depending on several stakeholders, such as health care providers, the general population and laboratories, which challenges an efficient and effective reporting of diagnosed cases. One issue that often arises is the administrative delay in reports of diagnosed cases. This paper aims to identify the main factors influencing reporting delays of HIV-AIDS cases within the portuguese surveillance system. The used methodologies included multilayer artificial neural networks (MLP), naive bayesian classifiers (NB), support vector machines (SVM) and the k-nearest neighbor algorithm (KNN). The highest classification accuracy, precision and recall were obtained for MLP and the results suggested homogeneous administrative and clinical practices within the reporting process. Guidelines for reductions of the delays should therefore be developed nationwise and transversally to all stakeholders.

Adjusting for reporting delay in cancer incidence when combining different sets of cancer registries

Cancer registries collect cancer incidence data that can be used to calculate incidence rates in a population and track changes over time. For incidence rates to be accurate, it is critical that diagnosed cases be reported in a timely manner. Registries typically allow a fixed amount of time (e.g. two years) for diagnosed cases to be reported before releasing the initial case counts for a particular diagnosis year. Inevitably, however, additional cases are reported after the initial counts are released; these extra cases are included in subsequent releases that become more complete over time, while incidence rates based on earlier releases will underestimate the true rates. Statistical methods have been developed to estimate the distribution of reporting delay (the amount of time until a diagnosed case is reported) and to correct incidence rates for underestimation due to reporting delay. Since the observed reporting delays must be less than the length of time the registry has been collecting data, most methods estimate a truncated delay distribution. These methods can be applied to a group of registries that began collecting data in the same diagnosis year. In this paper, we extend the methods to two groups of registries that began collecting data in two different diagnosis years (so that the delay distributions are truncated at different times). We apply the proposed method to data from the National Cancer Institute's Surveillance Epidemiology and End Results (SEER) program, a consortium of U.S. cancer registries that includes nine registries with data collection beginning in 1981 and four registries with data collection beginning in 1992. We use the method to obtain delay-adjusted incidence rates for melanoma, liver cancer, and Hodgkin lymphoma.

The estimated magnitude of AIDS in Brazil: a delay correction applied to cases with lost dates

The number of HIV-infected people is an important measure of the magnitude of the AIDS epidemic in Brazil and allows for comparison with epidemic patterns in other countries. This quantity can be estimated from the number of reported AIDS cases, which in turn needs to be corrected for the distribution of reporting delays and under-recording of cases. These distributions are unknown and must also be estimated from the recorded dates, which were missed to the Brazilian National AIDS registry. This paper estimates the number of AIDS cases diagnosed by inputting the lost information based on an estimate of the pattern in registration delay until 1996. We first fitted a non-stationary bivariate Poisson regression model to estimate the pattern in reporting delay. In the subsequent steps these models were applied to input new data, thus replacing the missing information, and to estimate the magnitude of the AIDS epidemic in the country. Model estimates ranged from 36,000 to 50,000 AIDS cases diagnosed in Brazil and still unreported. Therefore, the epidemic was 20 to 30% greater than known from the available information as of February 1999. To be useful to health policy-makers, the surveillance system based on officially reported AIDS cases must be continuously improved.

Estimating HIV incidence using dates of both HIV and AIDS diagnoses

Knowledge of HIV incidence is important to formulate sensible strategies aimed at controlling the HIV/AIDS epidemic. Back-projection is one of the methods for reconstructing the HIV incidence curve from AIDS incidence data. However, because of the low risk of developing AIDS during the first few years after infection, precise estimates of HIV incidence for the recent past are unlikely if we use AIDS incidence data only. As a result there have been recent attempts to use, not only the date of AIDS diagnosis, but also to use the date of their first positive HIV test. The objective of this paper is to incorporate into back-projection the additional information provided by those individuals who have tested HIV positive but have not yet developed AIDS. This adds information on a very large number of other individuals, and provides the hope that the precision of back-projection is improved considerably. The date of a positive HIV test or an AIDS diagnosis of an individual, whichever comes first, is used in a generalized convolution equation for the purpose of back-projection. The method is illustrated by an application to Australian HIV and AIDS data. Study results show that dramatic improvement in precision is gained for estimates of HIV incidence in recent years when both HIV and AIDS diagnosis dates are used on all individuals.

Estimating AIDS incidence and jack-knife variance from a continuous delay distribution and incomplete data

Usual methods for estimating AIDS incidences are based on the inflation of a discrete reporting delay distribution, which often results in very imprecise estimates of the incidence in the most recent past. In this paper, we propose an alternative approach to estimate the AIDS incidence by inflating a continuous reporting delay distribution for each reported case. Covariate effects on reporting delays are evaluated by a proportional hazards model for the reverse time hazard function. A jack-knife variance for the estimated AIDS incidence is given. Study results showed that precision of estimates is improved by using the continuous time model as compared with those estimates given by its discrete counterpart. This feature is useful in assessing current trends in AIDS incidence.

Estimativas do número de casos de aids no Brasil, corrigidas pelo atraso de notificação

Dois modelos estatísticos são propostos para estimar os casos de aids no Brasil já diagnosticados e ainda não notificados até o primeiro semestre de 1996, para as diversas categorias de exposição. O primeiro considerou a distribuição do atraso de notificação como uma função de sobrevida, com dados censurados à direita, que foi estimada a partir do método de Kaplan-Meyer. O segundo ajusta uma regressão de Poisson a uma tabela de contingência onde cada célula representa o número de casos diagnosticados no semestre e notificados com um determinado atraso. Precedendo o ajuste desta regressão, utilizou-se um modelo aditivo generalizado para identificar uma função que ajuste melhor que um modelo linear as relações funcionais em questão. As estimativas obtidas foram comparadas com as notificações ocorridas entre o segundo semestre de 1996 e o primeiro semestre de 1997. O confronto entre as estimativas fornecidas pela regressão de Poisson e o número de casos oficialmente notificados sugere uma possível mudança no comportamento do padrão de notificação. O número de casos oficialmente notificados encontra-se bem próximo à epidemia real, provavelmente devido à distribuição gratuita de medicamentos. As estimativas do modelo de sobrevida, que não leva em consideração mudanças no nível da epidemia nos diversos semestres de diagnóstico, tendem a uma subestimação das categorias que estão em crescimento.

Changing pattern of delays in reporting AIDS diagnoses in Australia

To accurately monitor and predict the progress of the HIV/AIDS epidemic, it is important to adjust reported AIDS counts for reporting delays. This requires estimation of the reporting delay distribution. This paper aims to use a statistical model to identify the main factors influencing reporting delays in Australia and to adjust reported incidence data for these delays among cases of AIDS diagnosed from 1993 and reported before 30 June 1997. Reporting delays were found to vary significantly across states/territories. The influence of calendar time of diagnosis was also significant, with an overall trend toward longer delays over time. AIDS cases diagnosed in the fourth quarter of a year were reported significantly more quickly than those diagnosed in the first or third quarters. No significant differences were found due to sex, age and HIV exposure category, except people with haemophilia, in whom AIDS cases appeared to be reported more slowly. After adjusting for under-reporting and reporting delay, we found that the AIDS incidence in Australia was declining from about 1000 cases per year in 1994 to about 760 cases per year in 1996.

A direct approach for correcting AIDS incidence: variance formula and comparison with other methods

We present a direct approach for correcting the acquired immunodeficiency syndrome (AIDS) incidence data for reporting delays, based on a non-parametric method for the analysis of right truncated data. We show that the proposed method when applied for grouped data is equivalent to three other published methods. We give a simple formula for the variance of the estimated AIDS incidence. Both estimator and variance are assessed in a simulation study. It is important for the estimation of AIDS incidence in the last quarter of the period under consideration to use month rather than quarter or half-year as the time unit for the analyses. The method is illustrated using data from the United States Centers for Disease Control.

Back-projection of German AIDS data using information on dates of tests

The paper presents an application of back-projection methods to the reported AIDS data which are collected by the AIDS Center in the Federal Office of Health, Berlin. The analysis is based on all data reported up to 31 December 1992. The data are broken down by major risk groups. Correction for reporting delays takes into account temporal changes in their distribution. The paper applies the EMS algorithm for the estimation of HIV incidence. The incubation period is modelled according to a convolution of several exponential distributions which describe time dependent phenomena like change of case definition and therapy effects. For each case it is reported whether an antibody test has been performed before diagnosis, and if yes, at what time this test was performed. The modelling of the incubation distribution takes into account the transition from a state 'not yet tested' into a state 'tested'. Only tested individuals in three pre-AIDS states are eligible for treatment. The model allows us to estimate not only the current total HIV prevalence but also a breakdown into those that are not yet tested, those that are tested but not yet treated and those that are under treatment. The results depend on the assumptions about the effect of treatment and on the degree of smoothing applied in the EMS algorithm.

AIDS: the statistical basis for public health

The backcalculation method has been extensively used in AIDS modelling and forecasting. Knowledge of reported AIDS cases, information on the time between HIV infection and onset of AIDS, and assumptions on the rate at which infections occurs, can be used to reconstruct the past history of the HIV epidemic, as well as to provide short term predictions of AIDS incidence. Uncertainty in the three components of the backcalculation method and the increasingly available information on HIV prevalence must be taken into account in order to provide realistic projections. In this paper we discuss ways of acknowledging uncertainty and suggest a Bayesian formulation of the backcalculation idea as a means of combining into a single model both random and systematic variation as well as prior information.