A low-cost approach to measure the burden of vaccine preventable diseases in urban areas

Vaccine 28 (2010) 4903–4912

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A low-cost approach to measure the burden of vaccine preventable diseases in urban areas Stephen P. Luby a,∗ , Amal K. Halder a , Samir K. Saha b , Aliya Naheed a , Hossain M.S. Sazzad a , Shamima Akhter a , Emily S. Gurley a , W. Abdullah Brooks a , Shams El-Arifeen a , Nusrat Najnin a , Arifa Nazneen a , Robert F. Breiman a a b

International Centre for Diarrheal Diseases Research, Bangladesh (ICDDR,B), Dhaka, Bangladesh Department of Microbiology, Dhaka Shishu Hospital, Dhaka, Bangladesh

a r t i c l e

i n f o

Article history: Received 19 December 2009 Received in revised form 1 May 2010 Accepted 16 May 2010 Available online 31 May 2010 Keywords: Surveillance Streptococcus pneumoniae Haemophilus influenzae type B Typhoid fever Meningitis Pneumonia

a b s t r a c t We piloted a low-cost approach to measure the disease burden of Streptococcus pneumoniae, Hib and Salmonella Typhi by leveraging the existing infrastructure of high performing microbiology laboratories at two large paediatric hospitals in Dhaka Bangladesh, and assessing the hospital utilization of the catchment population of these hospitals for different syndromes. S. Typhi was the most common bacterium identified in culture and accounted for an estimated 211 hospitalizations per 100,000 children <5 years of age per year. Meningitis due to S. pneumoniae was the most common cause of mortality accounting for 8.0 deaths per 100,000 children <5 years of age per year. This low-cost approach can provide data to support vaccine introduction and the health impact of newly introduced vaccines. Published by Elsevier Ltd.

1. Introduction Illnesses due to Streptococcus pneumoniae (pneumococcus), Haemophilus influenzae type B (Hib) and Salmonella Typhi are important causes of hospitalization and death among children in low income countries. A substantial portion of illness from these and numerous other pathogens are preventable with relatively new vaccines. National health authorities are generally interested in a cost benefit analysis of programs for new vaccine introduction to help them decide how to allocate scarce public funds. Cost benefit analysis, in turn, requires credible measures of the disease burden caused by each of the pathogens [1,2]. Randomized controlled trials of specific vaccines can measure how much disease is prevented with that specific vaccination, but such studies are prohibitively expensive especially if they need to be repeated for each vaccine in each country prior to introduction. The underlying issues that increase costs include that severe outcomes due to any single pathogen are relatively rare, and with the insensitivity of blood culture, it is difficult to ascribe a specific episode of illness to a specific pathogen. For example, the South African pneumococcal vaccine efficacy study required 20,000

∗ Corresponding author. Tel.: +880 2 988 1761. E-mail address: [email protected] (S.P. Luby). 0264-410X/$ – see front matter. Published by Elsevier Ltd. doi:10.1016/j.vaccine.2010.05.040

children per arm, at significant cost, to demonstrate a difference in radiologically confirmed pneumonia [3]. In addition, invasive disease as an outcome requires substantial investment in blood culturing, microbiology, and active surveillance. A second approach to assessing the burden of disease of each of these infections is to conduct active population-based surveillance. In its most rigorous formulation, eligible households are enrolled, regularly visited, and encouraged to seek care at a specific facility, usually free of cost [4–6]. When ill children present to the clinic, trained physicians apply standardized algorithms for the evaluation of each child to determine appropriate diagnostic tests, e.g. chest radiographs or blood cultures. Thus, the number of children who had specific symptoms, met a standard case definition and were diagnostic test positive can be counted. Since these cases arose from a defined population, the number of cases divided by the population and the time under surveillance provides a precise measure of disease incidence. Estimating disease burden requires assessing both disease incidence and severity. In low income settings patients commonly present late in illness with severe disease [7,8]. Active intensive population-based surveillance systematically underestimates disease burden because intensive active surveillance identifies and treats cases early in the course of illness. For diseases that respond well to treatment, early detection and treatment underestimates complications, secondary transmission and case fatality rate.

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In a minority of urban settings, nearly all patients use a single or few providers, and so a surveillance based on the disease experience of these few providers can be projected to the whole community [9]. This is a useful efficient system when it is available, but most high disease burden, low income settings have numerous providers. For example in the Asian sub-continent, home of more than 1.5 billion people, over 70% of outpatient visits occur in a diverse patchwork of private sector providers [10–12]. In India, 62% of hospital treatment in urban areas is delivered by a vibrant private sector that provides multiple choices for patient care [13]. We piloted a low-cost, alternative approach, community adjusted hospital-based surveillance, to measure the disease burden of S. pneumoniae, Hib and S. Typhi in a large urban community within Dhaka, Bangladesh. At the time of the study pneumococcal conjugate vaccine was not available within the country and conjugated Hib vaccine and typhoid vaccine was only available to a small minority of children whose high income families purchased it on the private market. We leveraged the existing infrastructure of high performing microbiology laboratories at two large paediatric hospitals in Dhaka Bangladesh. We assessed the catchment population of these hospitals, the syndromic disease rates in these communities, the proportion who sought care at the two study hospitals, and combined the findings with hospital admission and microbiology results to estimate the population-based incidence and severity of illness from these pathogens. 2. Methods

trypticase soy broth supplemented with 0.025% SPS and 1% isovitalex were inoculated with 2–3 ml of blood, incubated at 37 ◦ C and sub-cultured on days 1, 3 and 5. Isolates were identified by standard procedures [14]. All cerebrospinal fluid sent for any diagnostic work was routinely cultured in the Dhaka Shishu microbiology laboratory. 2.2. Hospital catchment survey 2.2.1. Catchment neighbourhoods To identify a neighbourhood whose residents regularly used Dhaka Shishu and Shishu Sasthya Foundation Hospital Study for hospitalization for serious illnesses, study workers listed the 20 patients under age 5 years most recently admitted with a diagnosis of pneumonia, meningitis, enteric fever or sepsis to the study hospitals. Study workers used a random number table to select a name from the list. If the patient’s home address was located within 60 min travel time by hired private transport during normal traffic to that hospital, field researchers travelled to the patient’s household. If the patient’s home address was more than 60 min travel time from the hospital, then another name was randomly selected from the list. Separate lists were maintained for Dhaka Shishu and Shishu Sasthya Foundation Hospital. The number of patients selected from each hospital was proportional to the number of patients admitted with a diagnosis of pneumonia, meningitis, enteric fever or sepsis who lived within 60 min travel time to the respective hospital. Between May and August 2006 study workers identified 70 patient households for community starting points.

2.1. Hospitalized patients

2.1.3. Data collection For each enrolled patient, the project physician collected basic clinical information and results of available laboratory investigations, including blood and cerebrospinal fluid cultures and chest radiographs if obtained. The project physician followed the patients and updated their clinical courses and outcomes during the hospitalization.

2.2.2. Study subjects The location of the hospitalized child’s home was used to identify catchment neighbourhoods, but the hospitalized child was not enrolled in the community-based study. To identify communitybased study subjects the field team noted the patient’s home as a starting point, identified the closest house, and the next closest house to this house, until five houses were skipped. The field workers evaluated if the sixth closest house was eligible, that is if a child under the age of 5 years was living in the house or if a child born alive, and at age less than 5 years had died within the previous 12 months, and, if so, sought consent for enrolment in the study. After collecting data, field workers approached the next closest household without backtracking, again evaluated if the household was eligible, and if so, sought consent to enrol the household. Field workers repeated this process until they identified 100 eligible households linked to each starting point. Between May and August 2006, study workers identified 70 patient households for community starting points and sought 100 households from each of these starting points. Because of concerns that households unavailable for interview might have different health experience and health seeking behaviour, there was an aggressive effort to enrol all eligible households. Field workers visited neighbourhoods repeatedly including during the evening and on weekends and made appointments, if necessary, to return at a time that was convenient to study subjects to maximize participation. Between May and August 2007, study workers returned to the same 70 neighbourhood starting points, and re-enrolled households to collect updated information. When households from the 2006 survey had moved or their youngest child had become >5 years of age study workers enrolled either the current household occupants if they met the eligibility criteria or the nearest eligible household.

2.1.4. Laboratory methods This study did not provide additional funding or reagents for diagnostic testing. Conventional blood culture bottles containing

2.2.3. Data collection Trained field workers administered a questionnaire that included basic household information including household com-

2.1.1. Study hospitals Dhaka Shishu Hospital is a government aided autonomous hospital, in Dhaka, the largest city in Bangladesh. It is the largest paediatric hospital in the country with 533 beds, 254 (48%) of which are reserved for patients who are unable to afford care. The hospital provides both primary paediatric care to the local community and tertiary care to children referred from throughout the country. The Department of Microbiology at Dhaka Shishu Hospital is a modern, well equipped laboratory that maintains the facility for inoculation of blood and CSF bacterial cultures 24 h per day. Shishu Sasthya Foundation Hospital is a community paediatric hospital located 5 km from Dhaka Shishu Hospital. It has 100 beds, 23 of which are reserved for non-paying patients. The microbiology laboratory of Dhaka Shishu Hospital performs all of the microbiological testing for specimens submitted to Shishu Sasthya Foundation Hospital. 2.1.2. Study subjects From April 2006 through March 2007, project physicians identified all children <5 years of age admitted as inpatients to Dhaka Shishu or Shishu Sasthya Hospital with a diagnosis of meningitis, sepsis, presumed typhoid fever, or pneumonia. Each project physician approached these children’s parents and requested informed consent for participation in the study.

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Bengali script are translated variably into the Roman alphabet for English spelling. Thus, matching individuals solely by names misses many appropriate matches. We developed a series of matching criteria that combined information on time of hospitalization, child sex, child’s name, mother’s name, father’s name, and date of birth to identify children who were represented in both the hospital and the community database (Table 3). The most restrictive matching criteria (A) minimized the risk of falsely attributing a match (i.e. maximized specificity). The least restrictive matching criteria (D) minimized the risk of missing a true match (i.e. maximized sensitivity).

position, demographics, and physical assets. For each child under the age of 5 years, field workers asked whether in the preceding 7 days he or she had any episodes of repeated coughing, difficulty breathing, nasal congestion or runny nose, or fever; whether in the preceding 2 months the child had an illness with cough, difficulty breathing or fever that prompted them to visit a health care provider; and, whether in the preceding 12 months the child had been hospitalized for an illness with cough, difficulty, breathing or fever. Information was collected on each of the reported episodes of illness including where the child was taken for health care. When the interviews identified a child death in the preceding year, the field worker made an appointment for an interviewer to return to the home and administer a verbal autopsy questionnaire. We have reported a more complete description of the verbal autopsy methods and results [15]. Briefly, for the verbal autopsy, the trained interviewer administered a structured questionnaire that collected detailed information on reported symptoms, observed signs, comments from any health care providers, and any written health care records. The verbal autopsy questionnaire was modified from one that has been used as part of demographic surveillance in other sites [16]. Interviewers used a separate verbal autopsy questionnaire for deaths among children ≤28 days of age that focused on the causes of neonatal deaths [16].

2.2.5. Data analysis An episode of illness reported in the community survey that resulted in hospitalization in which the child’s caregiver reported cough or difficulty breathing as the primary symptom or fever as the primary symptom with presence of either cough or difficulty breathing was classified as a hospitalization for respiratory illness. An episode of illness that resulted in hospitalization in which the child’s caregiver reported fever or decreased level of consciousness as the primary symptom, and both fever and decreased consciousness were reported, and the episode did not meet the respiratory illness case definition, was classified as a hospitalization for suspected meningo-encephalitis illness. An episode of illness that resulted in hospitalization in which the child’s caregiver reported fever as the primary symptom of illness and the episode did not meet either the respiratory or meningo-encephalitis case definition was categorized as suspect sepsis illness. We used suspect sepsis illness to understand the hospital use patterns of patients with typhoid fever. To estimate the person time at risk for hospitalization, we counted one year of person time for each child >12 and ≤60 months of age, because we asked about hospitalizations within the preceding year. For children <12 months of age, we counted their age as the person time at risk. To estimate the person time at risk for visiting a health care practitioner, we counted 2 months of person time

2.2.4. Data management Questionnaires were reviewed by supervisors. Field workers returned to households to collect missing information. Supervisors revisited 5% of households to double check information collected by field workers. Information from written questionnaires was entered into an electronic database. To link data from the community with the data from the hospital, we were interested in identifying children who participated in both components of the study. In Bangladesh people commonly have three or more different names that are used in different circumstances. Bengali names typically have a family chosen spelling in the Bengali language, but the sounds and 52 characters of written Table 1 Hospital catchment household characteristics. Characteristic

2006 (N = 6971)

2007 (N = 6971)

Dhaka Shishu Hospital (N = 5763)

Shishu Sasthya Foundation (N = 8179)

Total (N = 13942)

Household size (mean) Median duration at current residence (months) Father of the youngest child (%) Able to read newspaper Finished secondary school Mother of the youngest child (%) Able to read newspaper Finished secondary school Occupation of father of the youngest child (%) Salaried employee Employed on daily wages Medium/large shopkeeper Small shopkeeper Unemployed Other Median reported monthly income range (2007 US$)a Household construction Cement floor (%) Cement roof (%) Median number of rooms Water supply inside house (%) Natural gas for cooking fuel (%) Toilet inside house (%) Household assets (%) Television Mobile phone

5.0 18

5.0 24

5.1 24

4.9 18

5.0 20

79 46

80 49

79 48

80 47

79 47

72 34

74 36

74 36

72 34

73 35

46 21 18 8 2 5 71–143

48 19 19 7 2 5 71–143

45 19 20 9 2 5 71–143

48 20 17 7 2 5 71–143

47 20 19 8 2 5 71–143

93 41 1 41 90 44

94 45 1 44 91 45

96 44 1 45 90 47

92 42 1 41 91 42

94 43 1 43 90 44

74 58

76 71

77 66

73 63

75 65

a Respondents were asked to classify their monthly income in local currency within ranges that converts to the following ranges in 2007 US$: <29, 29–43, 43–57, 57–71, 71–143, >143.

4906

Table 2 Disease episodes among children <5 years of age in communities that surround Dhaka Shishu Hospital and Shishu Sasthya Foundation, Dhaka, Bangladesh. Syndromes

2006 Episodes (among 7990 children)

a

Incidence per 1000 child years

Proportion cared for at study hospitals n (%)

Episodes (among 7899 children)

153 child years 2555 12644 16865 1144 child years 3011 40 1320 4371

na na na

302 1304 2276

454 (13) 12 (26) 124 (8) 590 (12)

2606 40 1417 4063

27 9 11 48

81 (41) 27 (41) 33 (41) 141 (41)

202 74 65 341

2.49 0 2.49 4.98

3 (17) 0 5 (33) 8 (25)

13 1 21 35

Combined Incidence per 1000 child years

Proportion cared for at study hospitals n (%)

Episodes

151 child years 1996 8618 15042 1185 child years 2199 34 1196 3429

na na na

693 3239 4857

406 (16) 5 (12) 182 (13) 593 (15)

6049 86 2927 9062

28 10 9 48

120 (59) 38 (51) 33 (51) 191 (56)

399 140 145 684

1.81 0.14 2.93 4.89

2 (15) 0 1 (5) 3 (9)

31 1 39 71

Incidence per 1000 child years

Proportion cared for at study hospitals n (%)

305 child years 2277 10642 15959 2329 child years 2598 37 1257 3892

860 (14) 17 (20) 306 (10) 1183 (13)

28 10 10 48

201 (50) 65 (46) 66 (46) 332 (49)

2.15 0.07 2.71 4.94

5 (16) 0 6 (15) 11 (15)

na na na

Illness episodes with either cough or difficulty breathing as primary symptom or fever as primary symptom with either cough or difficulty breathing present. Illness episodes with fever or decreased level of consciousness as primary symptom and having both fever and decreased level of consciousness present in that episode, and did not meet the respiratory illness case definition were classified as suspected meningo-encephalitis illness. c Illness episodes that had fever as primary symptom and not meeting either the respiratory or meningo-encephalitis case definition were categorized as suspect sepsis illness. b

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Symptoms in the 7 days before interview Difficulty breathing 391 Repeated coughing 1935 Fever 2581 Health care practitioner visit in the 2 months before interview Respiratory diseasea 3443 46 Meningo-encephalitisb 1510 Sepsisc Total 4999 Hospitalizations in the 12 months before interview Respiratory disease 197 Meningo-encephalitis 66 Sepsis 80 Total 343 Deaths in the 12 months before interview Respiratory disease 18 Meningo-encephalitis 0 Sepsis 18 Total 36

2007

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for each child >2 months of age, because we asked about health care practitioner visits within the preceding 2 months. For children <2 months of age, we counted their age as the person time at risk. We corrected estimates of person time for the proportion of children who met the case definition who were seen in the catchment hospital and for the proportion of households that migrated out (Appendix A). We calculated the incidence of syndrome specific hospitalization by summing the number of reported hospitalizations that met the syndrome definitions and dividing by the person time of children under age 5 (Appendix A). We annualized the incidence of visits to health care practitioners reported in the last 2 months by multiplying by 6 and of symptoms reported in the last 7 days by multiplying by 52. Two trained physicians reviewed the verbal autopsy forms and independently assigned the cause of death using the tenth revision of the international classification of disease [17]. If there was a difference in the cause of death assigned by the two physicians, they discussed the case and together made a final judgment. We estimated the community incidence of disease specific mortality by multiplying the hospital-based incidence by the observed case fatality rate for the syndrome and adjusting this for the proportion of children in the community who died with this syndrome that had been seen in a study hospital (Appendix A). Because only a single child death was classified by verbal autopsy as meningoencephalitis, we averaged the proportion of child deaths from pneumonia, meningitis and sepsis identified in the community that had occurred at the study hospital to estimate the proportion of child deaths from meningitis that occurred at the study hospital. 2.2.6. Ethics Adult respondents in participating households and caregivers of hospitalized children provided informed consent. The study protocol was reviewed and approved by the Ethics Review Committee of the International Centre for Diarrhoeal Disease Research, Bangladesh. 3. Results Among 7000 candidate households identified, the field team completed interviews on 6971 (99.6%) in 2006. When the 6971 residences were revisited in 2007 only 3455 (50%) were occupied by the same people who were present when the team visited a year earlier and still had a child under 5 years old in the household or a child who had died in the last year. The field team enrolled 3521 replacement households in 2007. Combining the 2 years, 8179 household

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visits (59%) were in neighbourhoods identified through children discharged from Shishu Sasthya Foundation Hospital. Families had lived at their current residence for a median of 2 years. Approximately one half of fathers and one third of mothers had completed secondary school (Table 1). Although over 90% had a cement floor and two thirds owned a mobile phone, their median reported income was less than 1 US$ per household member per day. Household characteristics were similar in 2006 and 2007 and similar in neighbourhoods identified from children discharged from Dhaka Shishu Hospital and Shishu Sasthya Foundation (Table 1). Respondents reported 684 hospitalizations among children <5 years of age living in study households in the year preceding the survey for respiratory disease, suspect meningo-encephalitis or suspect sepsis (Table 2). The majority of hospitalizations (58%) were for respiratory disease. The syndrome specific hospitalization incidence rates were similar in 2006 and 2007, though a higher proportion reported hospitalization at the study hospitals in 2007 (56%) compared to 2006 (41%) (Table 2). The incidence of health care visits for the study syndromes (3892 per 1000 child years) was 81 times higher than the incidence of hospitalization for the study syndromes (48 per 1000 child years), which was 10 times higher than the incidence of death for the study syndromes (4.9 per 1000 child years) (Table 2). Most of the children who died (63%) never visited a hospital and only 15% were seen at one of the study hospitals during their fatal illness. We compared the retrospective reports of hospitalizations during the prior year in the 2007 community cross sectional survey to the prospective study-hospital-based surveillance. Since the 2007 community-based cross sectional survey was conducted from April through July 2007 and collected information on hospitalizations during the preceding 12 months, data on recall of hospitalization were available for all children included in the 2007 communitybased cross sectional study for hospitalizations that occurred from August 2006 through March 2007. Among 1231 children identified in the prospective hospital-based surveillance who were under the age of 5 years, hospitalized with a final diagnosis of pneumonia, meningitis or typhoid fever from August 2006 through March 2007 and lived within 60 min travel time to the hospital caregivers 1217 (99%) consented to enrolment (Table 3). The number of children who were co-identified in both the retrospective community cross sectional study and the prospective hospital surveillance varied depending on the criteria used to match children, though were quite similar for matching criteria B or C. A higher proportion of children with a discharge diagnosis of meningitis were matched with children in the cross sectional study compared to children with discharge diagnoses of pneumonia or typhoid fever (Table 3).

Table 3 Hospitalizations for syndromes of interest among patients who live within 60 min travel time to Dhaka Shishu or Shishu Sasthya Hospital, August 2006 through March 2007. Final diagnosisa

No.

Number (%) participated in cross sectional survey Matching criteria

Pneumonia Meningitis Typhoid fever Total a

729 121 367 1217

A

B

C

D

• Time of hospitalization within 2 months • Child sex • All 3 names matches ◦ Child’s name ◦ Mother’s name ◦ Father’s name • Date of birth exactly matches

• Time of hospitalization within 2 months • Child sex • At least 1 name matches ◦ Child’s name ◦ Mother’s name ◦ Father’s name • Date of birth within 2 months

• Time of hospitalization within 2 months • Child sex • At least 2 name matches ◦ Child’s name ◦ Mother’s name ◦ Father’s name

• Time of hospitalization within 2 months • Child sex • At least 1 name matches ◦ Child’s name ◦ Mother’s name ◦ Father’s name

7 (1.0) 4 (3.3) 6 (1.6) 17 (1.4)

19 (2.6) 6 (5.0) 11 (3.0) 36 (3.0)

21 (2.9) 6 (5.0) 11 (3.0) 38 (3.1)

39 (5.3) 7 (5.8) 20 (5.4) 63 (5.2)

Final diagnosis assigned by the treating physician.

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1 (0.09) 0 81 (13.6) 81 (4.51) 0 0 0 0 1 (0.09) 0 81 (13.6) 81 (4.51) Final diagnosis assigned by the treating physician. The total number of patients is less than the sum of the individual diagnoses, because 17 patients had multiple diagnoses. a

b

0 6 (3.73) 0 6 (0.33) 1060 161 597 1797b Pneumonia Meningitis Typhoid fever Total

228 (21) 146 (91) 253 (42) 617 (34)

197 (19) 71 (44) 250 (42) 515 (29)

48 (4.5) 138 (86) 9 (1.5) 185 (10)

0 7 (4.35) 0 7 (0.39)

1 (0.09) 10 (6.21) 0 10 (0.56)

1 (0.09) 14 (8.7) 0 14 (0.78)

0 2 (1.24) 0 2 (0.11)

0 7 (4.35) 0 7 (0.39)

CSF culture Blood culture CSF culture Blood culture

CSF culture

Either from blood culture or CSF

Blood culture

Either from blood culture or CSF

n (%) with S. Typhi n (%) with Hib n (%) with S. pneumoniae n (%) with CSF culture n (%) with blood culture n (%) with any culture No. of patients Final diagnosisa

Table 4 Culture results and hospital mortality among patients under 5 years of age who live within 60 min travel time to Dhaka Shishu or Shishu Sasthya Hospital, April 2006 through March 2007.

Either from blood culture or CSF

33 (3.1) 14 (8.7) 1 (0.2) 45 (2.5)

n (%) deaths

4908

Throughout the entire year of hospital surveillance, a minority of hospitalized patients with a discharge diagnosis of pneumonia or typhoid fever had a blood culture processed (Table 4). No child diagnosed with pneumonia had a positive blood culture for either S. pneumoniae or Hib. The mortality rate of hospitalized children with meningitis (8.7%) was higher than for pneumonia (3.1%). The CSF cultures of three of the children with meningitis who died grew S. pneumoniae. Only one patient of 597 with a final diagnosis of typhoid fever died. This child’s blood culture showed no growth, though one child with a clinical diagnosis of pneumonia who died had a blood culture that grew S. Typhi. Table 5 summarizes the modelled estimates of disease burden in the hospitals’ catchment area based on matching criteria B. Although pneumonia was a more common cause of hospitalization than either typhoid fever or meningitis, we were unable to estimate pathogen specific incidence of pneumonia, because there were no positive blood cultures from pneumonia patients. The estimated population-based incidence of hospitalization for culture confirmed enteric fever (211 per 100,000 child years) was an order of magnitude higher than for either pneumococcal or Hib meningitis. Pneumococcal meningitis was responsible for 8.0 deaths and Hib meningitis for 4.0 deaths per 100,000 child years. The model estimates are directly dependent on the parameters estimated from the study (Table 6). Changes in any one of these parameters by 25% changes the estimated incidence figures of hospitalizations and deaths by 20–33% (Table 6). Changing the criteria for what is considered a match between the hospital records and the community-based data had a somewhat larger impact on the estimates.

4. Discussion This study combined clinical and microbiological data collected for routine care at two large paediatric hospitals in Dhaka with an assessment of health care utilization in the catchment area of these two hospitals to estimate population-based incidence of selected vaccine preventable diseases. S. Typhi was the most common bacteria identified in culture; invasive typhoid fever accounted for an estimated 211 hospitalizations per 100,000 children <5 years of age. Meningitis due to S. pneumoniae was the most common cause of mortality among specific diseases evaluated, accounting for 8.0 deaths per 100,000 children <5 years of age per year. There are several advantages of this approach for measuring disease burden. First, it measures the disease as actually experienced in the community. In active community-based surveillance for bacterial disease, field workers commonly visit the target population regularly. They seek to identify people with early symptoms of disease. Since patients are treated early, the normal course of the disease in the broader population is not realized, and so mild disease is over-represented and complications from delays in treatment are under-represented. Also, because of costs, catchment areas for active, household-centred, population-based surveillance tend to be orders of magnitude smaller than hospital-based surveillance; this further exacerbates the under ascertainment of severe disease, since the crude numbers for severe outcomes are markedly lower, making the incidence estimates for severe disease much less precise. For example, active population-based surveillance in a population of 5000 children living in the Kamalapur community in Dhaka identified a high incidence of blood culture confirmed typhoid fever, 3900 episodes per 100,000 children <5 years of age, but no episodes of hospitalization [18]. Similarly, active surveillance in Kamalapur identified a much higher incidence of blood culture confirmed pneumococcal disease (447 episodes per 100,000 child years), but no episodes of meningitis or death from

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Table 5 Modelled incidence of study diseases, Dhaka Bangladesh. Pneumonia

Meningitis

Pneumococcal Person years under observation in the community study Proportion of hospitalized children in the study hospitals with the study diagnosis at discharge who were identified in the community study Mean proportion of the year the community population continued to live in their identified residence Projected hospital catchment population of children <5 years for the study diagnosis Number of children hospitalized for study diagnosis in study hospitals during 12 months of observations Proportion of hospitalized children with study diagnosis at study hospitals who died before discharge Proportion of hospitalized children with study diagnosis at study hospitals from whom culture was obtained Among cultures from children living within 60 min of the study hospitals the proportion that grew the study organism Proportion of child deaths from study syndromes in the community that occurred at the study hospitals Incidence of hospitalization at study hospitals for culture confirmed study organisms per 100,000 children <5 years Incidence of in hospital mortality from study diagnosis per 100,000 children <5 years Incidence of total mortality from study diagnosis per 100,000 children <5 years

HIB

7889

Pneumococcal

7889

Enteric Fever HIB

7889

Salmonella Typhi

7889

7899

0.0261

0.0261

0.0496

0.0496

0.0300

0.7478

0.7478

0.7478

0.7478

0.7478

226354

226354

118973

118973

197078

1060

1060

161

161

597

0.0311

0.0311

0.0870

0.0870

0.0017

0.1858

0.1858

0.9068

0.9068

0.4288

0.0000

0.0000

0.0959

0.0479

0.3164

0.1613

0.1613

0.1549

0.1549

0.1690

0

0

0.00

0.00

1.24

0.62

0.37

0.0

0.0

8.0

4.0

2.2

pneumococcal disease [6]. Pneumococcal bacteremia can resolve spontaneously without treatment and without sequelae [19,20]. Thus, the larger population assessed in the community adjusted hospital-based surveillance permitted an evaluation of less common hospitalization and mortality outcomes. It is these more

28

14

211

serious outcomes that are of particular interest to policy makers when prioritizing resources for various interventions. This approach also provides insight on incidence in a dynamic urban population. Urban populations are important, because the majority of the world’s population lives in urban areas, and these urban

Table 6 Sensitivity analysis of parameters of study model. Effect on measured incidence if parameter increased by 25% of its measured value Proportion of hospitalized children in the study hospitals with the study diagnosis at discharge who were identified in the community study Proportion of hospitalized children with the study syndrome from the community study, who were hospitalized at one of the study hospitals Among cultures from children living within 60 min of the study hospitals the proportion that grew the study organism Average change with alternative matching criteria in linking the hospital and the community cross sectional data

Effect on measured incidence if parameter decreased by 25% of its measured value

+25%

−25%

−20%

+33%

+25%

−25%

More restrictive Criteria A

Less restrictive Criteria D

−54%

+73%

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areas continue to grow [21]. Moreover, transmission patterns may be different for pathogens in the more highly crowded urban areas compared to rural communities. The frequent in and out migration of urban residents and the plethora of health care practitioners that offer services to these communities, can make it more difficult to track the health experience of large urban populations. A major advantage of this approach is substantially lower costs than active, community-based surveillance. Active communitybased surveillance requires repeated household visits, often weekly, for each enrolled household, substantial investment in patient evaluation, specimen collection and diagnostics tests. For this community adjusted hospital-based surveillance we implemented two cross sectional studies, but only the second study was used to generate the adjusted estimates of disease incidence. Thus, combining systematic collection of hospitalization and microbiological data with a single appropriately designed community-based cross sectional study, would provide a 1-year estimate of disease incidence. We did not systematically collect costs associated with implementation of such a system, but within this research setting the costs were approximately 25% of the costs for our intensive population-based surveillance. The hospital costs were offset by investments by PneumoADIP to improve sample collection and blood and CSF culture at these two hospitals. However, there are commonly centres of microbiological excellence in large urban communities in low income countries. The relatively lower cost means that this approach can be applied in multiple settings where there is too little funding for more intense measures. Moreover, the approach can provide data on multiple pathogens. The primary disadvantage of this approach is a disadvantage shared by all modelling efforts. It depends heavily upon a number of assumptions of uncertain accuracy. The most troubling assumption is that children who were admitted to the surveillance hospitals were representative of other children with similar illnesses in their neighbourhoods. The cost of medical care and hospitalization represents major barriers for populations whose median monthly income is $71–143. The nationally representative Bangladesh Demographic and Health survey notes that children under the age of 5 years with diarrhoea or respiratory illness who live in the richest 20% of households are three times more likely to be seen by a qualified practitioner compared to children from the poorest 20% of households [22]. While the rich are more likely to be hospitalized, and so be identified in the surveillance system, the poor are more likely to report child mortality [22]. Thus, the subset of the population that were hospitalized at the two surveillance hospitals, and the subset that are cultured likely have a different disease experience than those who are not cultured and those who sought care elsewhere. Because these biases will consistently provide more data on children from wealthier households, the estimates of inpatient mortality should be considered minimum estimates. Indeed, the 15% of patients who left the hospital against medical advice, and for whom we have no outcome information, would be expected to have a higher mortality than the children who remained hospitalized. Moreover, of the 71 children identified in the cross sectional study who died of one of the study syndromes only 12 (17%) were seen at one of the study hospitals (Table 2). Another important potential bias in the estimates of mortality is that we applied the observed meningitis mortality among all hospitalized children to the meningitis due to Hib and S. pneumoniae. As bacterial causes of meningitis have higher rates of mortality than viral infections [23], this assumption likely underestimates mortality. Other potential errors in these estimates result from uncertainties in the proportion of hospitalized patients who were also seen in the cross sectional surveys. We conducted the summary analysis based on a set of matching criteria that required time of reported

hospitalization and child’s date of birth in the cross sectional survey to be within 2 months of the hospital records, and the child’s sex and either the child’s mother’s or the father’s name matched. These criteria seem a reasonable tradeoff between a risk of under matching and overmatching in a setting where Julian dates are often not recalled, and where children and adults commonly use several different names with different spellings. Matching accuracy would be improved by adding specific questions in both the hospital and the cross sectional questionnaires to assist with matching, for example asking the paternal grandfather’s village name. In addition to concerns of bias, there is also sampling uncertainty in the estimates of the measured parameters. This is a particularly high risk for those estimates based on a small number of observations. The community adjusted hospital-based surveillance provides more credible estimates of the burden for meningitis and hospitalization for typhoid fever than for pneumonia. The primary problem is that blood culture diagnosis of pneumonia is substantially less sensitive than culture diagnosis of cerebrospinal fluid for meningitis or blood for enteric fever [3,24,25], a problem compounded in this study because blood cultures were only performed in 19% of patients with a final diagnosis of pneumonia. This approach underestimates the disease burden due to Hib and S. pneumoniae because the majority of this burden is from respiratory disease [26–29]. Systematic use of additional diagnostic tests including latex agglutination antigen detection [30], immunochromatographic testing [31] or PCR evaluation [32] of cerebrospinal fluid would likely identify culture negative cases and provide a larger and more accurate estimate of meningitis burden, but the low sensitivity of blood cultures to confirm bacterial pneumonia, remain a primary limitation in accurately attributing respiratory disease burden to bacterial pathogens, a limitation shared by all surveillance systems and exacerbated by high levels of antibiotic use before presentation to clinical care [8]. Active, population-based surveillance programs are likely to identify a much higher proportion of patients undergoing blood culture who meet surveillance criteria for febrile and/or respiratory illness, because they support all laboratory diagnostic costs and employ physicians to implement specific diagnostic algorithms. Information from these two different surveillance approaches provide complementary information helpful in measuring pathogen specific incidence rates to prioritize vaccine development and implementation, and to evaluate the impact of vaccine introduction. Many more children in the community reported being hospitalized for symptoms of pneumonia and sepsis, than had the confirmed diagnosis of pneumonia, sepsis or typhoid fever according to hospital assessments. This is not surprising because the community-based definitions were designed to be sensitive, not specific. Since the questions were asked within the context of a child who was hospitalized, we believe they represent the health care seeking behaviour of children with a similar severity of disease to those who actually had the syndrome of interest. The regular use of vaccines against Hib, S. pneumoniae, and S. Typhi can prevent premature death and severe morbidity for millions of children globally. Surveillance for these vaccine preventable diseases is especially important because many require substantial national level financial contribution. However, neither national governments nor global vaccine advocates can afford randomized controlled trials or intensive active population-based surveillance in every setting where these diseases threaten public health. The approach we used in Dhaka to characterize the catchment population of two leading facilities, and leverage their diagnostic capacity provides a local estimate of disease burden Although this approach underestimates respiratory disease burden and the burden among impoverished families, such local data can help persuade local influential paediatricians and national vac-

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cine authorities that these infections cause serious illness and death within the country. If the disease incidence trends are followed over time, they can also provide evidence of the reduction in disease following introduction of vaccine and so inform country-specific public health decision making. Acknowledgements This work was funded by the Department of Health and Human Services National Vaccine Program Office (NVPO) through the United States Agency for International Development Global Bureau’s Global Research Activity Cooperative Agreement with Johns Hopkins University Bloomberg School of Public Health, and the Government of Bangladesh through IHP-HNPRP. ICDDR,B acknowledges with gratitude the commitment of NVPO and IHPHNPRP to the Centre’s research efforts. The authors appreciate the suggestions made by Brendan Flannery, Danny Feikin, and John A. Crump to the study protocol, the consistent professional efforts of the field team especially Mamun-Ar-Rashid, Iffat Sharmin and Kazi Faisal Alam, and the careful laboratory work of Rajan Debnath. Appendix A. Formulas for incidence calculations Person time (PT) PT = PTCS ×

SFR SFH

where PTCS is the sum of person time among children <5 from the 2007 community cross sectional survey; SFR the sampling fraction residence – the mean proportion of time the population in the cross sectional survey remained in their household between the 2006 and 2007 survey; and SFH the sampling fraction hospitalized – the proportion of hospitalized children meeting the case definition in the study hospitals who were also identified in the 2007 community cross sectional study. The incidence of hospitalization (IH ) nH IH = PT where IH is the incidence of hospitalizationl; nH the number of reported hospitalizations that met the case definition; and PT the Person time of children under age 5 years in the catchment community. Incidence of hospitalization for culture confirmed study disease (ICD ) ICD = CP /CO /DH where CP is the proportion of hospitalized children with the study disease from the catchment area who were culture positive; CO the proportion of hospitalized children with the study disease from the catchment area who had a specimen obtained and cultured; and DH the proportion of children in the community survey who met this study disease case definition and who were hospitalized in one of the study hospitals. The community incidence of mortality (IM ) IM = IH ×

CFR SFM

where IH is the incidence of hospitalization; CFR the case fatality rate from hospital surveillance; and SFM the sampling fraction for mortality, that is the proportion of children who died in the community with this syndrome who were seen in a study hospital.

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