Estimating the influenza disease burden in SARI sentinel hospitals using WHO method

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1 Estimating the influenza disease burden in SARI sentinel hospitals using WHO method Implementation experience in Bolivia, Colombia, Ecuador, and Honduras. Pablo Acosta MD. MPH. Ministry of Public Health Ecuador October 2015

2 Estimating the burden of disease associated with influenza using a Sentinel SARI Surveillance System Pablo Acosta Ministry of Health Public of Ecuador October 2015

and positively impact public health? How to guide decision-making, and investment in health?

3 Why measure disease burden? Health budgets are limited (in all the countries without exception). INVESTMENT IN PUBLIC HEALTH ( ) How can these resources be effectively and efficiently allocated, and positively impact public health? How to guide decision-making, and investment in health? Invest in health to build a safer future World Health Day 7 April 2007 WE MUST MAKE ANOTHER TRANSFER TO THE SAME INVESTMENT FUND

4 Measurements must be made in order to prioritize public health activities

5 Technical basis

6 Sentinel surveillance objectives and use in decision-making 1. Contribute to global knowledge base regarding burden of disease attributable to influenza 2. Allow appropriate allocation of limited health resources among disease-related priorities 3. Establish epidemic thresholds for comparisons of disease severity between years and regions

7 Ways of expressing disease burden Morbidity and mortality associated with a disease (medical burden) Costs associated with disease Direct health care expenses Indirect costs associated with lost productivity due to the disease, disability Premature death (economic burden).

8 Medical burden Estimates morbidity due to respiratory infections associated with influenza (expressed as the incidence rate) Estimates the proportion of deaths attributed to respiratory infections associated with influenza (expressed as the case-fatality rate)

9 Formula used to calculate the incidence of a disease Cumulative incidence or risk Number of new events in a specified period Average number of persons exposed to risk during the same specified period

10 Formula used to calculate a disease s case-fatality rate Case-fatality rate Number of in-hospital deaths from a disease (in a given period) Number of diagnosed and hospitalized cases of that disease (in the same given period)

11 SARI Surveillance How to calculate the cumulative incidence of influenza using Sentinel SARI Surveillance Number of positive SARI cases (influenza or respiratory virus) during the specified period (year) Estimate of Denominator Average number of persons exposed to risk during the specified period (year) Population that would seek care at that hospital if they got ill Reference population or Catchment population

12 Defining the denominator: catchment population This involves determining the catchment population for the sentinel hospital, i.e. those who would go there if they got sick. There are 3 scenarios: 1. Data on the catchment population are available, because this is the only hospital. 2. Data on the catchment population are not available but can be estimated (where the catchment area can be defined). 3. Data on the catchment population would be so difficult to obtain that it would not be worth the time or effort, as other sentinel sites could be chosen.

13 Defining the catchment population Step 1- Get map of area (district, city, etc.) showing the location of the sentinel hospital

14 Defining the catchment population Step 2- Spot-map each patient based on the address given in the notification card. 16/10/ Title of the Presentation

15 Defining the catchment population Step 3-Identify the area where the majority of SARI cases at the sentinel hospital come from/reside ( 80%)(green line). This is the catchment area of the sentinel site.

16 Defining the catchment population Catchment area of sentinel site (boundary determined by the smallest administrative unit for which population data are available). 16

If there are several hospitals within the catchment area of the sentinel hospital, then

17 Defining the catchment population Step 4- Identify the other hospitals (red marks) in this catchment area (green line), where SARI cases would most likely seek medical care. If there are several hospitals within the catchment area of the sentinel hospital, then it is necessary to determine what portion (%) of SARI cases seek care at the sentinel hospital.

18 Step 5. Gather information on HOSPITAL DISCHARGES (ICD 10 J09-J22) of all hospitals in the catchment area to determine the portion of SARI cases that go to the sentinel hospital. Nombre Número total de casos de neumonía en los 12 meses anteriores del área de Proporción de casos de neumonía (que accede al hospital Centinela para el del Pneumonia Neumonias captación año del area catchment de cobertura area tratamiento desde el área Portion Proporción de captación) de of Neumonias Pneumonia (a / b). año 2012 Consider: centro 0-<6 6 m -< 1 15-<50 50-<65 0-<6 6 m -< 1 15-<50 50-<65 de salud 1-<2 años 2-<5 años 5-<15 años 65 años 1-<2 años 2-<5 años 5-<15 años 65 años meses año años años meses año años años HOSPITAL <1 <1 year año a 4 years años a 14 years años a 64 years años >65 >65 years años o <1 <1 year año a years 4 años a 14 years años a 64 years >65 years años >65 años MThe F period M F Mof Fsurvey M F of M information F M F M F on M Discharges F M F M is F of M a Fyear M F M F Total M F Total M F Total M F Total M F Total t M F Total M F M F Total M M F F M Total F M M F F Total M F Total Sitio H. Sentinel CentinelaHosp ,29 0,29 0,29 0,28 0,27 0,27 0,55 0,50 0,53 0,67 0,55 0,61 0,78 0,77 0,77 Centinela / hospital All the hospital discharges whose residence is within the area of influence will admit. (a) H. Hosp. N1 N ,23 0,26 0,24 0,32 0,32 0,32 0,05 0,00 0,03 0,00 0,00 0,00 0,00 0,00 0,00 Otros hospitales H. Hosp N2 N2 67 Fill 46 matrix with 49 hospital discharges ,44 0,41 0,42 0,30 0,38 0,34 0,25 0,21 0,24 0,33 0,43 0,38 0,22 0,23 0,23 H. Hosp. N3 N ,04 0,04 0,04 0,10 0,03 0,07 0,15 0,29 0,21 0,00 0,02 0,01 0,00 0,00 0,00 Total Total (b) Defining the catchment population

Defining the catchment population Step 6.

years years years the area of reference.

19 Defining the catchment population Step 6. Calculating the catchment population. Request the total population of the unit Under or administrative 5-14 units Districts from 1 year 1-4 years years years the area of reference. Disaggregated by age and sex. Sources: Territorial planning units (Ministries of Health) Municipality censes Electoral councils Add 65 years or over

20 Defining the catchment population Step 6. Calculating the catchment population. Multiply the official population number by the proportion of the sentinel hospital to obtain the catchment population per group. Estimating Estimación the denominator Denominadores <1 <1 year año a years 4 años a 14 years años a 64 years años >65 years años M F Total M F Total M F Total M F Total M F Total Población Official Oficial Population Proporciones Estimated portions Estimadas 0,29 0,29 0,29 0,28 0,27 0,27 0,55 0,50 0,53 0,67 0,55 0,61 0,78 0,77 0,77 Estimated Denominator Denominador Estimado

21 Influenza cumulative incidence rates: Bolivia, Colombia, Ecuador, and Honduras

22 Rate per 100,000 inhabitants Rate per 100,000 inhabitants Influenza Cumulative Incidence Rate Influenza incidence rate in La Paz - Bolivia Influenza incidence rate. Hospital Carlos Andrade Martín. Quito Ecuador

23 Rate per 100,000 inhabitants Rate per 100,000 inhabitants Influenza Cumulative Incidence Rate Influenza incidence rate, Hospital Los Angeles- Pasto- Colombia Influenza incidence rate. Social Security Hospital. San Pedro Sula- Honduras

24 Rate per 100,000 inhabitants Rate per 100,000 inhabitants Cumulative Incidence Rates of Other Respiratory Viruses RSV incidence rate. Hospital Carlos Andrade Martín. Quito - Ecuador Influenza incidence rate. Hospital Carlos Andrade Martín. Quito - Ecuador /10/2015

25 Rate per 100,000 inhabitants Rate per 100,000 inhabitants Cumulative Incidence Rates of Other Respiratory Viruses Parainfluenza incidence rate. Hospital Carlos Andrade Martín. Quito - Ecuador Adenovirus incidence rate. Hospital Carlos Andrade Martín. Quito - Ecuador /10/2015

26 Case-fatality Rates Numerator: Number of deaths with positive laboratory diagnosis of influenza Total number of SARI cases with positive laboratory diagnosis of influenza

27 Methodological Limitations Global Medical Burden (clinical presentations) This method estimates a proportion of the global medical burden; only that with clinical manifestations of SARI or ILI. Bacterial Pneumonia Primary viral pneumonia not considered Cases with SARI symptoms The burden of other events is not measured, such as unrecognized influenza-complicating underlying medical conditions that could be responsible for a proportion of the medical burden. Cases in which influenza is not recognized as a cause: e.g. asthmatic attacks Secondary bacterial pneumonia occurring after a period of improvement in the primary viral disease.

28 Frequent problems on implementing the method Has the most appropriate hospital been selected? Incomplete information Under-reporting: Not all patients attending the unit are entered into the surveillance system Laboratory samples are not obtained from all SARI cases Inadequate sampling procedures (taking, transportation and storage of samples) Diagnostic algorithm (IIF first then PCR-RT) Sentinel unit collects information with population representativeness General hospitals that collect information on a specific population: children and the elderly only. Social security hospitals that collect information on the member population. Availability of hospital discharge and local census population data

29 Suggestions for estimating medical burden in SARI surveillance 1- Validate methodology used to estimate medical burden Three possible scenarios: Implementation is technically feasible: Mature SARI surveillance system with sentinel units and good performance indicators. Implementation is technically feasible in line with surveillance system procedures: Improvement Plan. Implementation is not technically feasible: Surveillance system is currently being implemented Obtaining hospital discharge data is challenging

30 Suggestions for estimating medical burden in SARI surveillance (2) Which criteria to validate? 1. Availability of data: 3 to 5 years to make inferences 1 complete year (beginning) 2. Under-reporting: Selection biases: Surveillance continues throughout the year or only during the seasonal period. Every SARI case is admitted, or sampling is conducted due to lack of resources. Case definition not used, clinical criteria preferred. The patient is very ill, or does not consent to specimen collection. Information biases: Poor quality data in notification card (missing data on variables such as sex, age, address, among others). Issues with diagnosis (type of algorithm, sample taking, transport, or storage).

31 Suggestions for estimating medical burden in SARI surveillance (3) Which criteria to validate? 3. Data representativeness Demographic and socio-economic characteristics Sex and age Ethnic group Region (e.g. Coast, Sierra, Amazon region) 4. Availability of hospital discharge and local census population data 5. Selection of the sentinel unit or units Surveillance system performance indicators General Hospital or a Specialized Hospital?

32 1. Availability of data How to validate these criteria? Essential data Number of cases hospitalized in the sentinel hospital Total number of new SARI cases admitted to the sentinel site Total number of SARI cases with clinical samples for virological diagnosis Number of new influenza-positive SARI cases Desirable data Mid-year population of sentinel site catchment area Number of deaths due to SARI in the sentinel hospital (influenza +/ - ) Number of deaths among influenza-positive SARI cases in the sentinel hospital Number of samples sent to the laboratory for influenza confirmation Number of SARI cases in pregnant women Number of influenza (+) SARI cases in pregnant women Number of deaths in influenza (+) SARI cases in pregnant women Number of SARI cases, influenza (+) SARI cases and influenza (+) SARI fatalities with chronic medical conditions: COPD, asthma, diabetes, chronic heart disease, chronic hepatopathy, chronic nephropathy, immunodeficiency (including HIV). Crude birth rate

33 How to validate these criteria? 2. Under-reporting, biases, and representativeness Is surveillance conducted throughout the entire year? No Yes Location Tropical and subtropical region Temperate region Is the seasonality of influenza known? No Yes Are the cases representative of the catchment population? Has laboratory confirmation for influenza been conducted in at least a proportion of the cases? Yes No Yes No Interpret results with caution! Not all types of data can be used to quantitatively estimate the influenza disease burden in a community. Some of the data can only be used to make some qualitative inferences regarding the trends of influenza transmission in the country. Conduct surveillance for one full calendar year Exclude cases where the date of sampling is not within the year under analysis Exclude cases that live outside the catchment area of the sentinel hospital Initiate diagnosis in at least 10% of cases Evaluate the possibility of bias

34 How to validate these criteria? 3. Evaluating biases: Case selection and diagnostic tests SARI cases Review sampling fraction Review sampling method used Review diagnostic test used 100% sampling - High Low If sampling is not randomized If sampling is randomized If PCR, viral culture or IIF is used If rapid test is used Low potential for bias High potential for bias Low potential for bias High potential for bias

35 How to validate these criteria? 4. Availability of hospital discharge and local census population data: 1. Is there an official system for the systematic collection of HOSPITAL DISCHARGE data, for public and private units, in the country? 2. If this system is not available, it would be possible to organize systematic DISCHARGE data collection with sites close to the sentinel unit. Essential variables: ICD-10 diagnosis Patient s age Patient s sex Patient s address

36 How to validate these criteria? 5. Selecting the sentinel unit or units. Annex 7. Surveillance system performance indicators

37 Steps in sentinel surveillance Indicators linked to monitoring and evaluation Indicator Structure or calculation Target Step 1 Identifying hospital patients who meet criteria for SARI case Percentage of weeks with timely reporting of denominators Timely reporting of denominators (Number of epidemiological weeks in which denominators were duly reported/total number of epidemiological weeks covered by the report) x % Percentage of cases hospitalized with SARI that are captured by the surveillance system Under-reporting (SARI cases reported during the period/cases identified during the period through active case searching) x % Median time (days) between date of admission and date of reporting Timely reporting of cases Median time (number of days) between date of admission and date of reporting 1 day Step 2 Form for data collection and data loading Percentage of cases investigated and closed Coverage of case investigation (Total number of SARI cases fully investigated and closed/total number of cases reported and discharged) x 100 * Fully investigated and closed means that tests were conducted for the etiological diagnosis of the case reported and discharged, and that both the clinical and epidemiological data are complete. 90% Step 3 Obtaining respiratory tract specimens and test results Percentage of SARI cases from whom a samples was obtained Coverage of SARI cases from whom a sample was obtained (Number of SARI cases from whom a sample was obtained/number of SARI cases with valid criteria for sampling) x 100 *Samples should be obtained within seven days of onset of symptoms. 90% Percentage of samples of good quality received Quality of samples (Number of samples of good quality received/total number of samples received) x 100 * Of good quality means that the samples were correctly obtained, stored and transported up to arrival at laboratory. 90% Percentage samples of good quality processed Coverage of processing (Number of samples processed/total number of samples received correctly) x % Median time between date of admission and date of obtaining the sample Timely sampling Median time (number of days) between date of admission and date of obtaining sample 2 days Median time between date of obtaining the sample and date of reception of sample Timely reception of samples Median time (number of days) between date of obtaining sample and date of reception of sample *If date of reception is unknown, use date of transport for this indicator. 1 day Median time between date of reception of sample and date of starting processing Timely processing Median time between date of reception of sample and date of processing 3 days Median time between date of reception of the sample and date of issuing results Timely issuing of results Median time between date of reception of sample and date of emitting results 3 days Percentage of SARI cases cared for in an Intensive Care Unit from whom a sample was obtained Coverage of SARI cases cared for in Intensive Care Units from whom a sample was obtained (Number of SARI cases cared for in an Intensive Care Unit from whom a sample was obtained/number of SARI cases cared for in an Intensive Care Unit) x % Percentage of deaths associated with SARI from whom a sample was obtained Coverage of SARI deaths from whom a sample was obtained (Number of deaths due to SARI from whom a sample was obtained/number of deaths due to SARI) x % Step 4 Data analysis and interpretation Number of cases reported per month Number of samples dispatched per month Percentage of samples dispatched that were positive for influenza Step 5 Dissemination of data and results Percentage of weeks in which regional or national level data were sent Timely reporting 80% Aptness of the data presented in the weekly influenza surveillance report Timely reporting < 2 weeks

38 How to validate these criteria? Selecting the sentinel unit or units. Apply the previous validation criteria to the unit. Availability of data Level of under-reporting, biases, and representativeness Access to information on HOSPITAL DISCHARGES of other hospital units in the sentinel hospital catchment area. Prepare an Implementation Plan that includes improvement activities for all the validation criteria

39 What type of hospital to select? General Hospital versus National Referral Hospital General Hospital: May be more representative of the population and Helps define the catchment area. National Referral Hospital: Serves populations from several parts of the country, making it more difficult to establish the catchment area. However, in the SARI databases of the referral hospitals analyzed, 70-80% of the cases captured live near the sentinel unit.

40 The selection will depend on the hospital s performance in surveillance, plus its ability to implement a plan to improve the validation criteria.

41 Why include disease burden estimates in SARI surveillance? It helps improve the main objectives of SARI surveillance : Viral circulation Establishing the seasonality Detecting phenotypic/genotypic changes It is an opportunity to optimize sentinel surveillance data The methodology is relatively simple Facilitates decision-making: Population prioritizations Policy-making Resource-planning

42 Final recommendation for PAHO/WHO and ministries Monitor countries that have decided to incorporate disease burden estimates in their SARI surveillance systems. Has this strengthened the system? Is the information from the disease burden estimates being used? Or does it simply remain as an epidemiological surveillance exercise?

Regional Update Pandemic (H1N1) 2009 (May 24, h GMT; 12 h EST)

Regional Update Pandemic (H1N1) 2009 (May 24, 2010-17 h GMT; 12 h EST) The information contained within this update is obtained from data provided by Ministries of Health of Member States and National