A1 Need to involve traditional Indian medical systems - AYUSH, for Tuberculosis control in India
Rakesh Roshan Bhardwaj1, Ravinder Kumar2, Rajesh Sood1, Beena Thomas3, Omesh K Bharti1, Rajesh Guleri1, Baria1, Sunder Sharma4
1Heath and Family Welfare Department, Shimla, Himachal Pradesh, India; 2WHO Consultant National Tuberculosis Elimination Program (NTEP), Shimla, Himachal Pradesh, India; 3National Institute of Research in Tuberculosis (NIRT), Chennai, Tamil Nadu, India; 4Directorate of Ayurveda, Shimla, Himachal Pradesh, India
Correspondence: Rakesh Roshan Bhardwaj (rakesh9342@gmail.com)
A2 Transmission of Human Immunodeficiency Virus among long-distance truckers in Purba Medinipur district, West Bengal; India
Dr. Dilip Kumar Biswas1, Dr. Rama Bhunia2
1Dy Chief Medical Officer of Health-II, Purba Medinipur district, West Bengal, India; 2District Maternal & Child Health Officer, Howrah district, West Bengal, India
Correspondence: Dr. Dilip Kumar Biswas (dilipbiswas29@gmail.com)
Age group
|
Tested for HIV
N=4650
|
Reactive
|
Positivity Rate/ 1000
|
< 17 Years | 6 | 0 | 0.0 |
18 - 30 Years | 2520 | 11 | 4.4 |
31 - 45 Years | 1742 | 14 | 8.0 |
46 - 60 Years | 362 | 1 | 2.8 |
> 60 Years | 20 | 0 | 0.0 |
Overall | 4650 | 26 | 5.6 |
Sexually transmitted
Infections (STI)
|
Number with STI N = 21065
|
%
| |
Urethral Discharge (UD) | 442 | 2.1 | |
UD (Non-herpetic) | 128 | 0.6 | |
UD (Herpetic) | 21 | 0.1 | |
Total | 591 | 2.8 |
A3 Malaria outbreak investigation in a tribal area of Pratapgarh district, Rajasthan, India, 2016
Prasoon Sheoran1, Chandrakant SMoghe1, ThekkevilayilG Thomas1, Chandrashekhar S Aggarwal1, Sachin Sharma2, Samir VSodha3
1National Centre for Disease Control, New Delhi, India; 2Medical and Health Department, Pratapgarh, Rajasthan, India; 3U.S. Centers for Disease Control and Prevention, New Delhi, India
Correspondence: Prasoon Sheoran (prasoonsheoran12@gmail.com)
A4 An outbreak investigation of acute diarrheal disease attributed to eating ice cream, in three villages of Kamareddy district, Telangana, India, 2019
Seema Tabassum, Manikandanesan Sakthivel, Sailaja Bitrugunta
India Epidemic Intelligence Service Program, ICMR National Institute of Epidemiology, Chennai, India
Correspondence: Seema Tabassum (samarahmustafa786@gmail.com)
A5 Evaluation of National Leprosy Eradication Programme Surveillance System, Pandariya Block, Kawardha district, Chhattisgarh, April — June 2019
Mohamed Azarudeen1, Sarwat Naqvi2, Amol Patil3, Tanzin Dikid1, Kanica Kaushal3 Rupali Roy4, Deepika Karotia4, SK Jain1, Sujeet Singh1, Anil Kumar4
1National Centre for Disease Control, Delhi, India; 2National Leprosy Eradication Programme, Chhattisgarh & Maharashtra, India; 3South Asia Field Epidemiology and Technology Network (SAFETYNET); 4Central Leprosy Division, Ministry of Health and Family Welfare, Government of India
Correspondence: Mohamed Azarudeen (drazareis@gmail.com)
Attribute | Indicators | Results (%) | Evaluation |
---|---|---|---|
Simplicity (April-June 2019)* | Awareness of key informants on case definition for leprosy case suspect | 81% (21/26) | Good |
Proportion of key informants who found it easy to fill patient form | 69% (18/26) Correct reporting | ||
Flexibility (April 2018-March 2019) | Change in reporting format during the reference period | New variable (patient from other state) added in reporting format in April 2018 | Flexible |
Change in the reporting frequency and mechanism (paper/electronic) | No change (block and district level) | ||
Data quality - (Completeness)(April 2019-June 2019) | Proportion of completed MPR | None (0/12) | Poor |
Acceptability (April-June 2019) | Proportion of health facilities reporting through routine surveillance (paper based) | 100% (12/12) | Good |
Predictive value positive (April-June 2019) | Proportion of cases confirmed by MO’s or assistant MO’s in district and block | District 12.5% (40/320) Block 5.2% (8/154) | Poor |
Representativeness (April-June 2019) | Proportion of private facilities reporting in NLEP | None | Poor |
Timeliness (April-June 2019) | Proportion of reports received on time at block, district and state | 100% | Good |
Stability (April -June 2019) | Proportion of NLEP DEO (data entry operator) trained | None Non-Medical Assistant (NMA) compiles the collected data at block | Not Stable |
Usefulness (April 2016-March 2019) | Surveillance system helping in doing contact tracing | Routine surveillance and leprosy case detection campaign helped in detecting cases among contacts | Good |
A6 Descriptive epidemiology of acute encephalopathy syndrome outbreak in Muzaffarpur district, Bihar, India from May–July 2019
Vaisakh T P1, Rajeev Kumar1, Abhishek Mishra1, Binoy S Babu1, Purvi Patel1, Tanzin Dikid1, Ramesh Chandra1, Rajesh Yadav2, Mohan Papanna2, Anoop Velayudhan2, Saurabh Goel1, Suhas Dhandore1, Ajit Shewale1, Manickam Ponnaiah3, Manoj Murhekar3, Ravindra Prasad4, SK Jain1, Sujeet Singh1
1National Centre for Disease Control, New Delhi, Delhi, India; 2U.S. Centers for Disease Control and Prevention, New Delhi, Delhi, India; 3ICMR-National Institute of Epidemiology, Chennai, Tamil Nadu, India; 4Community Medicine Department, Shri Krishna Medical College Hospital, Muzaffarpur, Bihar, India
Correspondence: Vaisakh T P (vaisakhtp@gmail.com)
A7 Investigation of an acute diarrhoeal disease outbreak from contaminated drinking water supply, Village A, Patna, Bihar, India July–August 2019
Ujjawal P Sinha, Kevisetuo A Dzeyie, Lucky Sangal, Pankaj Bhatnagar
National Public Health Surveillance Project, World Health Organization Country Office, New Delhi, India
Correspondence: Ujjawal P Sinha (drujjawal@gmail.com)
Exposures | Cases n (%), N = 81 | Controls n (%) N= 81 | OR (95% CI) |
---|---|---|---|
No handwashing with soap after defecation | 1 (98) | 0 (100) | 3.08 (0.3–30.2) |
Being female | 55 (68) | 31 (38) | 3.41 (1.8–6.5) |
Drinking water source | |||
Government-supplied tap water | 73 (90) | 29 (36) | 16.36 (6.9–38.7) |
Borewell water | 3 (4) | 24 (30) | 0.91 (0.03–0.3) |
Hand pumped water | 5 (6) | 28 (34) | 0.12 (0.05–0.3) |
Treated water before drinking | 30 (37) | 60 (74) | 0.21 (0.1–0.4) |
Ingested non-homemade food in previous 3 days before illness | 9 (11) | 27 (33) | 0.25 (0.1–0.6) |
Drank water from alternate supply | 8 (10) | 30 (37) | 0.19 (0.1–0.4) |
A8 Effect of community-led weekly pulse cleaning of water collections in controlling dengue outbreak at Alipurduar, West Bengal, India, 2019
Puran Kumar Sharma, Subarna Goswami, Kousik Choudhury, Ananta Maji, Golam Mortuja
Office of the CMOH, Alipurduar, Department of Health & Family Welfare, Govt of West Bengal
Correspondence: Puran Kumar Sharma (puran.sharma611@gmail.com)
A9 Descriptive epidemiology of recent three Influenza AH1N1 outbreaks in Puducherry district, India, 2019
Lakshmanasamy Ravivarman, Prabhdeep Kaur
ICMR-National Institute of Epidemiology, Chennai, India
Correspondence: Lakshmanasamy Ravivarman (drravivarman@gmail.com)
Age Group (In years) | 2018 | 2017 | 2015 | |||
---|---|---|---|---|---|---|
Attack rate (per 100,000) | Case fatality ratio (%) | Attack rate (per 100,000) | Case fatality ratio (%) | Attack rate (per 100,000) | Case fatality ratio (%) | |
0-5 | 46.7 | 3.5 | 19.8 | 0 | 14.1 | 10 |
5-15 | 15 | - | 8.5 | 0 | 3.27 | 0 |
15-25 | 21.4 | - | 10.7 | 0 | 3.15 | 0 |
25-35 | 31.6 | - | 14.7 | 7.7 | 6.76 | 8.3 |
35-50 | 26.3 | 3.9 | 15.9 | 8.8 | 4.22 | 11.1 |
50-60 | 49.5 | 7.7 | 27.1 | 17.4 | 10.6 | 11.1 |
>60 | 53.6 | 6.5 | 25.2 | 0 | 8.75 | 0 |
A10 Medically certified causes of death and risk factors in mortality, Puducherry district, India, 2016-19
Lakshmanasamy Ravivarman, Prabhdeep Kaur
ICMR-National Institute of Epidemiology, Chennai, India
Correspondence: Lakshmanasamy Ravivarman (drravivarman@gmail.com)
A11 Malaria elimination in high transmission Hard-to-reach areas in the state of Odisha India
Madan Mohan Pradhan1,2, Praveen Kishore Sahu3, Manoranjan Ranjit4, Ambarish Datta5, Sanghamitra Pati4
1District Vector Borne Disease Control Programme, Boudh, Odisha, India; 2Ex. State Programme Officer, NVBDCP, Odisha, India; 3Molecular and Immunology Lab, Ispat General Hospital, Rourkela, Odisha, India; 4ICMR-Regional Medical Research Center, Bhubaneswar, Odisha, India; 5Public Health Foundation of India, Bhubaneswar, Odisha, India
Correspondence: Madan Mohan Pradhan (drmmpradhan@gmail.com)
A12 Multi-specialty outpatient clinics (Polyclinics) for urban poor at Urban Primary Health Centers - Chennai, Tamil Nadu, India,2019
Suganya Barani, Parasuraman Ganeshkumar, Tarun Bhatnagar
ICMR-National Institute of Epidemiology, Chennai, India
Correspondence: Suganya Barani (suganya.desmart@gmail.com)
A13 An Epidemiological Profile of Injury Patients Admitted in a Tertiary Care Hospital, New Delhi, India, 2015
Naveen K. Rastogi1, Kapil Goel2, Tanu Jain3, Samir V. Sodha4, Chandra S. Aggarwal1, Srinivas Venkatesh1
1National Centre for Disease Control, Ministry of Health and Family Welfare, New Delhi, India; 2Postgraduate Institute of Medical Education and Research, Chandigarh, India; 3Ministry of Health and Family Welfare, New Delhi, India; 4U.S. Centers for Disease Control and Prevention, New Delhi, India.
Correspondence: Kapil Goel (drkapil123@gmail.com)
Characteristic | n | % |
---|---|---|
All injuries(N=4028)
| ||
Male | 3085 | 77 |
Urban | 3630 | 90 |
Median age in years (range) | 28* | 0–100** |
Death | 15 | <1 |
Unintentional | 3645 | 90 |
Mechanism of injury | ||
Fall | 1607 | 40 |
Road traffic | 1582 | 39 |
Assault | 276 | 7 |
Stab or cut | 178 | 4 |
Burn | 175 | 4 |
Others | 210 | 5 |
Body part injured (n=3260) | ||
Head | 419 | 13 |
Extremities | 2366 | 73 |
Others | 475 | 15 |
Road traffic injuries (n=1582)
| ||
Male | 1346 | 85 |
Median age in years (range) | 25* | 0–100** |
Road use | ||
Two-wheel vehicle | 1043 | 66 |
Pedestrian | 263 | 17 |
Heavy vehicle | 172 | 11 |
Four-wheel vehicle | 53 | 3 |
No helmet, two-wheel vehicle | 60 | 6 |
No seat belt, four-wheel vehicle | 16 | 30 |
No seat belt, heavy vehicle | 145 | 84 |
Did not receive first aid at scene | 1019 | 64 |
Transported to health facility by private vehicle | 591 | 37 |
Falls (N=1607)
| ||
Male | 1102 | 69 |
Urban | 1461 | 91 |
Median age in years (range) | 25* | 0-100** |
Death | 1 | <1 |
Unintentional | 1531 | 95 |
Place of falls | ||
Home | 1085 | 68 |
Road | 253 | 16 |
Workplace | 89 | 6 |
School or educational institute | 69 | 4 |
Others | 111 | 6 |
Assault (N=276)
| ||
Male | 220 | 80 |
Urban | 253 | 92 |
Median age in years (range) | 30* | 1–82** |
Place of assault | ||
Home | 107 | 39 |
Road | 111 | 40 |
Workplace | 31 | 11 |
School or educational institute | 2 | 1 |
Sport or athletic area | 10 | 4 |
Others | 15 | 5 |
A14 An outbreak of malaria among the migrant construction workers traveled to a malaria-endemic area, Vanghur Village, Vellore District, Tamil Nadu, India, 2017
Polani Rubeshkumar1,2, Manickam Ponnaiah1, KST Suresh2, Kolandaswamy Karumanagounder2
1ICMR-National Institute of Epidemiology, Chennai, Tamil Nadu, India; 2Directorate of Public Health & Preventive Medicine, Tamil Nadu, India.
Correspondence: Polani Rubeshkumar (rubesh.pc@gmail.com)
Characteristics | Population at risk | n (Attack rate per 1000) | ||
---|---|---|---|---|
Probable cases | Confirmed cases | |||
Age (years) | <5 | 214 | 8 (37) | 0 (0) |
5-14 | 451 | 56 (124) | 0 (0) | |
15-24 | 497 | 24 (48) | 1 (2) | |
25-39 | 705 | 110 (156) | 4 (6) | |
40-59 | 619 | 75 (121) | 2 (3) | |
≥60 | 289 | 38 (131) | 0 (0) | |
Sex | Male | 1390 | 175 (126) | 7 (5) |
Female | 1384 | 136 (98) | 0 (0) | |
Total | 2774 | 311 (112) | 7 (3) |
A15 Influenza A (H1N1) outbreak in Irular colony, New Gummidipoondi village, Tiruvallur health unit district, Tamil Nadu, India, 2017
Saraswathi VS1, Parasuraman Ganeshkumar2, Tarun Bhatnagar2, Manoj Murhekar2, Manickam Ponnaiah2, Prabakaran J1
1Department of Public Health and Preventive Medicine, Govt. of Tamil Nadu; 2ICMR - National Institute of Epidemiology, Chennai
Correspondence: Parasuraman Ganeshkumar (ganeshkumardr@gmail.com)
Factors | Cases (N=35) | Controls (N=21) | Odds ratio | 95% CI |
---|---|---|---|---|
Contact with fever cases | 35 | 16 | 10.94 | 1.2 to 101.4 |
Travel history | 4 | 1 | 2.5 | 0.26 to 23.9 |
Tamiflu tablet issued | 35 | 21 | Undefined | 0 to 32.5 |
A16 Contact tracing during Nipah outbreak, Ernakulam District, Kerala, India 2019
Hari Sankar1, Sugunan AP2, Tarun Bhatnagar2, Ganesh Kumar2, Manoj Murhekar2, Saritha Ragini Lohithakshan3, Sreedevi3
1ICMR National Institute of Epidemiology, Chennai, India; 2Indian Council of Medical Research – National Institute of Epidemiology, Chennai, India; 3Kerala Health Services, Thiruvananthapuram, Kerala, India
Correspondence: Hari Sankar (shankarhar@gmail.com)
A17 An outbreak investigation of Dengue and Chikungunya in Thunikinoothala Thanda, Nalgonda District, Telangana, March 2019
Seema Tabassum1, Sushma Choudhary2, Sailaja Bitrugunta1, Manikandanesan Sakthivel1
1ICMR - National Institute of Epidemiology, Chennai, India; 2South Asia Field Epidemiology and Technology Network, India
Correspondence: Seema Tabassum (samarahmustafa786@gmail.com)
A18 Universal Health Coverage Information Technology (UHC-IT) Platform, an approach to have a population as the denominator to establish a digital cohort of the State, Tamil Nadu, 2019
Viduthalai virumbi Balagurusamy, Beela Rajesh, Kolandaswamy Karumanagounder, Darez Ahamed, Senthil Raj K
Department of Health and Family Welfare, Government of Tamil Nadu, India
Correspondence: Viduthalai virumbi Balagurusamy (v.virumbi@gov.in)
A19 Barriers towards timely reporting of and response to outbreaks in West Bengal, India 2015-16
Biswajit Dey1, Tarun Bhatnagar2, Manickam Ponnaiah2, Sharmistha Mitra1 Prabhdeep Kaur2
1Department of Health & Family Welfare, Government of West Bengal, India; 2ICMR-National Institute of Epidemiology, Chennai
Correspondence: Biswajit Dey (deybisu@gmail.com)
A20 Establishing Media Surveillance to monitor post-flood Public Health Response in Kerala, India, August- September 2018
Rontgen Saigal1, Parasuraman Ganeshkumar2, Lakshmi GG3
1FETP NCD Fellowship scholar, ICMR-National Institute of Epidemiology, Chennai; 2Scientist D, ICMR National Institute of Epidemiology, Chennai, India; 3Medical Officer, Kerala Health Services, Thiruvananthapuram, Kerala, India
Correspondence: Parasuraman Ganeshkumar (ganeshkumardr@gmail.com)
A21 Eliciting drug abuse status in the fast-urbanizing Solan district of Himachal Pradesh, India, 2019
Ajay Kumar Singh1, Kushel Verma2, Vaishali Sharma3
1District Programme Officer, Department of Health and Family Welfare, Office of Chief Medical Officer, district Solan, Himachal Pradesh, India; 2Psychiatrist, Department of Health and Family Welfare, Office of Medical Superintendent, District Hospital Solan, Himachal Pradesh, India; 3Psychologist, Department of Health and Family Welfare, Office of Chief Medical Officer, district Solan, Himachal Pradesh, India
Correspondence: Ajay Kumar Singh (ajay7777singh@yahoo.com)
A22 Evaluation of the Leprosy Surveillance System in Raipur, Chhattisgarh, 2018
Manish Gawande1, Amol Patil 2, Pankaj Bhatnagar1
1National Public Health Surveillance Project, World Health Organization, New Delhi, India; 2South Asia Field Epidemiology and Technology Network, New Delhi, India
Correspondence: Manish Gawande (manish.gawande@gmail.com)
Year | Number cases with grade-II deformity | Proportion of cases with grade-II deformity |
---|---|---|
2013-14 | 85 | 10 |
2014-15 | 85 | 10 |
2015-16 | 137 | 13 |
2016-17 | 88 | 9.1 |
2017-18 | 79 | 9.6 |
A23 Dengue outbreak investigation in East Delhi, India, 2015
Somashekar Dundaiah, Venkatesh Bhadravathi Govindappa
Public Health Department, East Delhi Municipal Corporation, Delhi, India
Correspondence: Somashekar Dundaiah (asksoma@gmail.com)
Exposures | Case N=107 | Control N=214 | Odds Ratio OR (95% C.I) |
---|---|---|---|
Use of temephos granules in desert coolers | 71 (32%) | 149 (68%) | 0.3 (0.1-1.1) |
Wearing full sleeves clothes and pants | 77 (72%) | 167 (79%) | 0.6 (0.4-1.1) |
Using mosquito net | 14 (13%) | 35 (17%) | 0.7 (0.3-1.4) |
Window screened with mesh | 46 (44%) | 102 (48%) | 0.8 (0.5-1.3) |
Use of desert cooler | 77 (72%) | 153 (71%) | 0.9 (0.5-1.6) |
Overcrowding at home | 62 (58%) | 83 (40%) | 2.0 (1.2-3.2) |
A24 Measles outbreak in a marginalized population of Jogapatti block, West Champaran district, Bihar, India, February 2019
Vishesh Kumar1, Sanjay Kumar Singh1, Ankur Nair1, Nihar Ranjan Ray1, Sushma Choudhary2, Pauline Harvey1,3
1National Public Health Surveillance Project, World Health Organization Country Office, Delhi, India; 2South Asia Field Epidemiology and Technology Network, Delhi, India; 3Global Immunization Division, U.S. Centers for Disease Control and Prevention, Atlanta, Georgia, USA
Correspondence: Vishesh Kumar (vikumar@who.int)
A25 Evaluation of the Measles Component of the Integrated Disease Surveillance Programme in Mirzapur District, Uttar Pradesh, India, March 2018
Hamid Sayeed1, Pankaj Bhatnagar1, Pauline Harvey1,2
1National Public Health Surveillance Project, World Health Organization, New Delhi, India; 2Global Immunization Division, U.S. Centers for Disease Control and Prevention, Atlanta, GA, USA
Correspondence: Hamid Sayeed (srcallahabad@npsuindia.org)
Attribute | Indicator | N | N | (%) |
---|---|---|---|---|
Simplicity | Aware of S, P, and L report form submission day | 8 | 9 | (89) |
Aware of the officer to whom S, P, and L report forms submitted | 8 | 9 | (89) | |
Reported filling of S, P, and L report forms was easy | 7 | 9 | (78) | |
Flexibility | Submitted S, P, and L report forms via an alternative mode (not paper) | 1 | 9 | (11) |
Data Quality | S report forms with the place, person, and time fields completely | 2 | 16 | (13) |
P report forms with the place, person, and time fields complete | 0 | 8 | (0) | |
L report forms with the place, person, and time fields complete | 0 | 8 | (0) | |
Availability of submitted S, P, and L report forms retained by the district | 28 | 32 | (88) | |
Availability of S, P, and L report forms on IDSP web portal | 32 | 32 | (100) | |
Acceptability | Weekly district surveillance meetings | 4 | 4 | (100) |
Representativeness | Private health facilities submitting P and L report forms | 5 | 67 | (7) |
Timeliness | S report form submitted by Monday | 16 | 16 | (100) |
P report form submitted by Tuesday | 4 | 8 | (50) | |
L report form submitted by Tuesday | 8 | 8 | (100) | |
District report submitted by Wednesday | 4 | 4 | (100) | |
Stability | Availability of printed S, P, and L report forms at the health facility on interview day | 9 | 10 | (90) |
Stock out of S, P, and L report forms at the health facility in the last three months | 4 | 10 | (40) |
A26 Investigation of the measles outbreak, Amoja village, Phulparas block, Madhubani, Bihar, India, October–November 2018
Sambit Pradhan1, Sushma Choudhary2, Pankaj Bhatnagar1
1National Public Health Surveillance Project, World Health Organization, Delhi, India; 2South Asia Field Epidemiology and Technology Network, Delhi, India
Correspondence: Sambit Pradhan (sambitpradhan2500@gmail.com)
Risk factor | Ill | Not ill | RR (95% CI) |
---|---|---|---|
No immunization card | 13 (81%) | 231 (69%) | 1.7 (0.5–6.1) |
No immunization with MCV1, by card | 1 (6%) | 15 (5%) | 2 (0.19–21) |
No immunization with MCV1, by card + recall | 8 (50%) | 48 (14%) | 7.2 (2.8–18.5) |
Female sex | 10 (62%) | 167 (50%) | 1.3 (0.5–3.3) |
Illiterate mother | 10 (62%) | 170 (51%) | 1.1 (0.5–4.2) |
Muslim religion | 10 (62%) | 56 (17%) | 7.2 (2.7–18.9) |
A27 Malaria outbreak investigation in Badaun District, Uttar Pradesh, India, August–October 2018
Rakesh Vishwakarma1, Rajesh Kumar1, P. Kumar2, Kaushal Gupta2, V. K. Sharma2, Anil Sharma2, Asha Ram2, Amol Patil3, Gunjan Kumar1, Madhup Bajpai1, Pankaj Bhatnagar1
1National Public Health Surveillance Project, World Health Organization, New Delhi, India; 2Department of Health and Family Welfare, Government of Uttar Pradesh, Lucknow, India; 3South Asia Field Epidemiology and Technology Network, New Delhi, India
Correspondence: Rakesh Vishwakarma (srcjaipur@npsuindia.org)
A28 Profile of maternal deaths in Virudhunagar district, Tamil Nadu, India 2013-2018
Vijay Krishnamoorthy, Muthusamy Santhosh Kumar
ICMR- National Institute of Epidemiology, Chennai, India
Correspondence: Vijay Krishnamoorthy (calldrvijay@gmail.com)
Causes of maternal death | Cause-specific mortality rate /100000 pregnant women (Observed) | Cause-specific mortality rate/100000 pregnant women (Expected) [2] |
---|---|---|
Sepsis
| 5.7 | 3.8 |
Embolism
| 9.0 | 8.5 |
Haemorrhage
| 9.8 | 21.2 |
Pregnancy-induced hypertension
| 13.0 | 10.3 |
A29 Measles Outbreak Investigation among Children in Hamlet A, Nawada District, Bihar, India, December 2018
Sanjay K. Singh1, Ismeet Kaur1, Kevisetuo A. Dzeyie1, Sushma Choudhary2, Pankaj Bhatnagar1
1National Public Health Surveillance Project, World Health Organization Country Office, New Delhi, India; 2South Asia Field Epidemiology and Technology Network, New Delhi, India
Correspondence: Sanjay K. Singh (srcchamparaneast@npsuindia.org)
Risk Factor | Frequency of exposure among measles cases N=21 | Frequency of exposure among non-cases N=82 | Risk Ratio | 95% Confidence Interval |
---|---|---|---|---|
n (%) | n (%) | |||
House <3 rooms | 20 (95) | 39 (48) | 14.91 | 2.0–106.9 |
Family >6 persons | 16 (76) | 21 (26) | 5.70 | 2.2–14.3 |
Kutcha house* | 18 (86) | 36 (44) | 5.44 | 1.7–17.3 |
No MCV1** | 5 (24) | 4 (5) | 3.26 | 1.5–6.8 |
Illiterate mother | 14 (67) | 36 (44) | 1.43 | 0.6–3.2 |
Female | 14 (67) | 38 (46) | 1.55 | 0.6–3.5 |
A30 Evaluation of Adverse Events Following Immunisation surveillance system, Vidisha, Madhya Pradesh, India, March 2018
Abhishek Jain1, Sushma Choudhary2, Pankaj Bhatnagar1
1National Public Health Surveillance Project, World Health Organization, Delhi, India; 2South Asia Field Epidemiology and Technology Network, Delhi, India
Correspondence: Abhishek Jain (drabhishekjain@yahoo.com)
Attribute and indicator | Numerator | Denominator | Results, n/N (%) |
---|---|---|---|
Simplicity | |||
• Proportion of ANMs aware of AEFI reporting | No. of ANMs aware of case definition and reporting | No. of ANMs Interviewed | 2/8 (25) |
• Proportion of MOs aware of AEFI reporting | No. of MOs aware of case definition and reporting | No. of MOs interviewed | 1/4 (25) |
• MOs able to classify AEFIs | No. of MOs able to classify AEFIs | No. of MOs interviewed | 0/4 (0) |
Flexibility | |||
• Proportion of reports uploaded to SAFE-VAC | No of reports of AEFIs uploaded to SAFE-VAC | No of reports of AEFIs reviewed | 0/15 (0) |
• Proportion of AEFI documents sent to state through email | No. of AEFI reports sent through email | No of reports of AEFIs reviewed | 13/15 (87) |
Acceptability | |||
• Use of standard formats at all levels | Availability of standard forms at RUs | No. of RUs where record reviewed | 3/3 (100) |
• Proportion of AEFIs reported by RUs | No. of cases reported in H002 | Total AEFIs | 2/4 (50) |
• Proportion of reported AEFIs investigated | No. of cases investigated | No. of AEFI cases on line list | 0/2 (0) |
Sensitivity | |||
• Proportion of AEFI reported against expected | No. of AEFIs reported | No. of surviving infants in Vidisha district | 5/45,068 (111/1,000,000) |
No. of AEFIs reported | No. of surviving infants in block Pipalkheda | 3/9,948 (301/1,000,000) | |
No. of AEFIs reported | No. of surviving infants in block Gyaraspur | 1/3,808 (262/1,000,000) | |
Data quality | |||
• Proportion of reported persons with symptoms written in AEFI tracking register | No. of reported persons with symptoms in AEFI tracking register | No. of reported persons written in AEFI tracking register | 232/232 (100) |
• Proportion of AEFIs reported in H002 | No. of AEFIs reported in H002 | Total AEFIs | 2/4 (50) |
• Proportion of reported AEFIs investigated | No. of AEFIs investigated | Total AEFIs in line list | 0/2 (0) |
• Proportion of Investigated AEFIs documented at blocks | No. of investigated AEFIs documented at block | Total AEFIs investigated in block | 0/4 (0) |
Representativeness | |||
• Proportion of blocks reported AEFI cases | No. of blocks reporting AEFIs | No. of blocks | 3/7 (47) |
• Proportion of government RU reporting sending weekly reports | No. of government RUs sending weekly reports | No. of government RUs | 11/11 (100) |
• Proportion of private. RUs sending weekly reports | No. of private RUs sending weekly reports | No. of private RUs | 3/3 (100) |
Timeliness | |||
• Proportion of reports (H002, CRF, PCIF, FCIF) sent to appropriate level within expected timeline | No. of H002 sent | No. of AEFIs with H002 to be filled | 616/728 (85) |
No. of CRF sent | No. of AEFIs with CRF to be filled | 5/5 (100) | |
No. of PCIF sent | No. of AEFIs with PCIF to be filled | 5/5 (100) | |
No. of FCIF sent | No. of AEFIs with FCIF to be filled | 3/5 (60) | |
Stability | |||
• Proportion of weekly reports physically present at district | No. of H002 available at district | No. of AEFIs with H002 to be filled | 67/728 (9) |
• Proportion of blocks having AEFI tracking register, | No of blocks having AEFI tracking register | No. of blocks | 2/2 (100) |
• H002 booklet and CRF | No of blocks having H002 booklet and blank CRF | No. of blocks | 1/1 (100) |
• Availability of PCIF and FCIF at district | No of districts having blank PCIF and FCIF available | No. of districts | 1/1 (100) |
A31 Assessment of the Measles Surveillance System in East Champaran District, Bihar State, India, April 2017–March 2018
Md. Subhan Ali1, Rajesh Yadav2, Kevisetuo A. Dzeyie1, Shanmukhappa3, Pankaj Bhatnagar1, Pauline Harvey1,4
1National Public Health Surveillance Project, World Health Organization, New Delhi, India; 2Division of Global Health Protection, U.S. Centers for Disease Control and Prevention, New Delhi, India; 3Department of Microbiology, Mandya Institute of Medical Sciences, Mandya, Karnataka, India; 4Global Immunization Division, U.S. Centers for Disease Control and Prevention, Atlanta, Georgia, USA
Correspondence: Md. Subhan Ali (smomotihari@npsuindia.org)
Attribute | Indicator | n | N | % |
---|---|---|---|---|
Simplicity | Aware of case definition | 16 | 16 | 100 |
Aware of age groups in definition | 10 | 16 | 63 | |
Aware of WHO VPD-H002 and how to report | 16 | 16 | 100 | |
Acceptability | Weekly WHO VPD-H002 forms submitted by block primary health centers to district | 1456 | 1456 | 100 |
District surveillance meetings | 51 | 52 | 98 | |
Outbreak reports available at district | 0 | 12 | 0 | |
Data Quality | WHO VPD-H002 forms with complete information | 1398 | 1456 | 96 |
Timeliness | WHO VPD-H002 form received on time by district | 1456 | 1456 | 100 |
Representativeness | Private health facilities reporting | 8 | 217 | 4 |
Measles outbreaks reported by private health facilities | 8 | 12 | 67 |
A32 Measles outbreak investigation in Thiruvananthapuram city, Kerala, India, 2019
Kolar Neelakanta Arun Kumar1, Shobha Malini2, Anoop Velayudhan3, Rajesh Yadav3, Kevisetuo Anthony Dzeyie1, Pankaj Bhatnagar1, Pauline Harvey1,4
1National Public Health Surveillance Project, World Health Organization Country Office, New Delhi, India; 2Department of Community Medicine, S.L.N. Medical College & Hospital, Koraput, Odisha, India; 3Division of Global Health Protection, Center for Global Health, U.S. Centers for Disease Control and Prevention, New Delhi, India; 4Global Immunization Division, Center for Global Health, U.S. Centers for Disease Control and Prevention, Atlanta, Georgia, USA
Correspondence: Kolar Neelakanta Arun Kumar (srcchennai@npsuindia.org)
A33 Investigation of the Rubella outbreak, Manawar urban, Dhar district, Madhya Pradesh, India, 2018–2019
Bhavani Gunta1, Ismeet Kaur1, Sushma Choudhary2, Kevisetuo A Dzeyie1, Pankaj Bhatnagar1
1National Public Health Surveillance Project, World Health Organization Country Office, New Delhi, India; 2South Asia Field Epidemiology and Technology Network, New Delhi, India
Correspondence: Bhavani Gunta (smovishakapatnam@npsuindia.org)
A34 An outbreak investigation of Kyasanur Forest disease in villages of Shimoga district, Karnataka, India, 2018–2019
Asish K. Satapathy1, Puttaraju A.K. Jetty1, D.M. Satishchandra1, Rajesh Yadav2, Mohan Papanna2, Pankaj Bhatnagar1, Harvey Pauline1,3
1National Public Health Surveillance Project, World Health Organization Country Office, New Delhi, India; 2Division of Global Health Protection, Center for Global Health, U.S. Centers for Disease Control and Prevention, New Delhi, India; 3Global Immunization Division, Center for Global Health, U.S. Centers for Disease Control and Prevention, Atlanta, Georgia, USA
Correspondence: Asish K. Satapathy (satapathya@who.int)
Exposure | Number (%) | Odds Ratio (95% CI) | |
---|---|---|---|
Case-patients n=50 | Controls n=100 | ||
Location of house
| |||
<50 metre from forest | 45 (90) | 66 (66) | 4.6 (1.7–12.7) |
<50 metre from agricultural fields | 41 (82) | 54 (54) | 4.0 (1.7–8.8) |
Working in areca nut plantation | 43 (86) | 75 (75) | 2.0 (0.8–5.1) |
Ticks
| |||
Tick seen in household/surrounding | 44 (88) | 74 (74) | 2.5 (0.9–6.7) |
History of tick bite | 27 (27) | 23 (23) | 4.0 (1.9–8.1) |
Presence of rodents in house | 32 (64) | 35 (35) | 3.0 (1.6–6.7) |
Dead monkey(s) reported near house | 43 (86) | 46 (46) | 7.2 (2.9–17.6) |
Distance from dead monkey(s) to house | |||
<50 m | 28 (56) | 29 (29) | 5.9 (2.6–13.9) |
50–100 m | 12 (24) | 9 (9) | 8.2 (2.8–24.6) |
>100 m | 10 (6) | 62 (7) | (Reference) |
No exposure to dead monkey | 18 (36) | 62 (62) | (Reference) |
Any exposure to dead monkey
| 32 (64) | 38 (38) | 2.9 (1.4–5.9) |
Went near death site* | 32 (100) | 0 (0) | 1287.0 (77.4–1384.6) |
Cattle or dog(s) went to death site | 16 (50) | 2 (5) | 18.0 (3.6–87.7) |
Participated in monkey cremation* | 6 (19) | 0 (0) | 10.1 (1.2–86.9) |
Touched dead monkey* | 5 (16) | 0 (0) | 8.3 (0.9–73.3) |
No PPE used | 1 (3) | 1 (1) | 1.9 (0.1–19.8) |
A35 Influenza A (H1N1)pdm09 outbreak in Lucknow District, Uttar Pradesh, India, January–May 2019
Madhup Bajpai1, Rakesh Vishwakarma1, Sushma Choudhary2, Pauline Harvey1,3
1National Polio Surveillance Project, World Health Organization, New Delhi, India; 2South Asia Field Epidemiology and Technology Network, New Delhi, India; 3Global Immunization Division, U.S. Centers for Disease Control and Prevention, Atlanta, GA, USA
Correspondence: Madhup Bajpai (bajpaim@who.int)
A36 Assessment of the Measles-Rubella Surveillance System in Hyderabad, India, 2018
Puttaraju Agrahara Kumbijetty1, Sushma Choudhary2, Manjunatha S Nagaraja3, Pankaj Bhatnagar1
1National Public Health Surveillance Programme, World Health Organization Country Office, New Delhi, India; 2South Asia Field Epidemiology and Technology Network, New Delhi, India; 3Professor, Department of Community Medicine,, Mysore Medical College and Research Institute, Mysore, Karnataka, India
Correspondence: Puttaraju Agrahara Kumbijetty (srchyderabad@npsuindia.org)
Attribute | Indicators | n/N | % |
---|---|---|---|
Simplicity | Awareness about the definition of suspected Measles-Rubella case | 12/12 | 100 |
Ease of case reporting, case investigation form filling, and data transfer to a higher level | 12/15 | 80 | |
Laboratory confirmation requirements and process | 12/12 | 100 | |
Flexibility | Understanding and implementation of case-based surveillance | 17/19 | 89 |
New reporting and laboratory system | 17/19 | 89 | |
Change in the threshold for flagging outbreaks | 12/13 | 92 | |
Data Quality | Completeness of case investigation forms | 19/25 | 76 |
Completeness of weekly reports | 27/34 | 79 | |
Variables missing from weekly reports | 565/ 1768 | 32 | |
Acceptability | Number of reporting units submitting a weekly report | 33/34 | 97 |
Unreported cases detected in active case search | - | ||
Sensitivity | Compared ‘non-measles non-rubella discard rate’ against expected – 2 per 100,000 population per year | For week 24, 2018 = 0.8 | |
Timeliness | Timely submission of weekly report | 1343/1768 | 76 |
Representativeness | Geographic distribution of reporting sites | 15/15 | 100 |
Private facilities | 108/141 | 76 | |
Stability | All health facilities had identified human resources for measles-rubella surveillance. | ||
Line lists of all reported cases and weekly reports available at assessed sites. | |||
No system breakdowns were identified, such as out of stock of reporting forms and specimen collection kits. |
A37 Assessment of Kala-azar Surveillance System in Khagaria District, Bihar, August 2018
Ashish Nawal Tigga1, Amol Patil2, Mohammad Ahmad1, Pankaj Bhatnagar1
1National Public Health Surveillance Project, World Health Organization, New Delhi, India; 2South Asia Field Epidemiology and Technology Network, New Delhi, India
Correspondence: Ashish Nawal Tigga (srckhagaria@npsuindia.org)
Attribute | Indicator | n/N | % |
---|---|---|---|
Simplicity | MOs aware of case definition | 4/4 | 100 |
KTSs aware of case definition | 2/2 | 100 | |
PPs aware of case definition | 4/8 | 50 | |
ASHAs aware of case definition | 0/11 | 0 | |
ANMs aware of case definition | 0/4 | 0 | |
Flexibility | ASHA using more than one method for reporting | More than one method, such as short message service (SMS), call or personal accompaniment, used | |
Change of diagnostic test | Changed to rapid detection of anti-recombinant 39-amino acid repeat antigen (rK-39) from rK-16 | ||
Additional fields in line-list (2017) | ▪ Co-infection with HIV/TB ▪ Place of treatment ▪ Unique identification number ▪ Referral details – ASHA/ Doctor/other primary health centers | ||
Modifications in line-list (2017) | ▪ Outcome at 1, 6, and 12 months ▪ The extra field ‘lost’ added to the outcome | ||
Acceptability | MO Training | 2017 – national-level training | |
KTS Training | 2018 – state-level training | ||
District Task Force Meeting frequency | 2018 – 1/7 | 14 | |
District Partners’ Meeting Frequency | 2018 – 1/7 | 14 | |
Block Task Force Meeting frequency | 2018 – 12/16 | 75 | |
ANM Meeting discussion on kala-azar | 2018 – 1/26 | 3.8 | |
ASHA Meeting discussion on kala-azar | 2018 – 1/27 | 3.7 | |
Data Quality | Proportion of blank fields in line-list | 431/1390 | 31 |
Completeness of Monthly district report | 2017 – 3/3 | 100 | |
Timeliness | Timeliness of district reports | 2015 – 2/3 | 66 |
2016 – 2/3 | 66 | ||
2017 – 2/3 | 66 | ||
Timeliness of block reports | 2015 – 14/24 | 58 | |
2016 – 19/24 | 79 | ||
2017 – 19/24 | 79 | ||
Stability | District Vector Borne Disease Control Officer posting vs sanctioned post | 1/1 | 100 |
District Vector Borne Disease Consultant posted vs sanctioned post | 1/1 | 100 | |
Posted vs sanctioned posts - KTS | 6/6 | 100 |
A38 Assessment of the Integrated Disease Surveillance Programme Surveillance System for Measles, Ghaziabad District, Uttar Pradesh, 2018
Rajesh Kumar1, Sushma Choudhary2, Madhup Bajpai1, Pankaj Bhatnagar1
1National Public Health Surveillance Project, World Health Organization, Delhi, India; 2South Asia Field Epidemiology and Technology Network, Delhi, India
Correspondence: Rajesh Kumar (srcghaziabad@npsuindia.org)
Attribute | Indicator | n/N | % |
---|---|---|---|
Simplicity
| Staff aware of case definition | 7/7 | 100 |
Staff found ease of reporting | 7/7 | 100 | |
Staff found ease of compilation of information | 5/5 | 100 | |
Ease of data analysis | 1/1 | 100 | |
Flexibility
| Proportion of reporting units sending weekly report by email | 22/86 | 26 |
Acceptability
| Staff willing to report in formats | 4/5 | 80 |
P and L forms submitted against expected by reporting units | 29/32 | 91 | |
S forms submitted against expected Feedback provided by the district surveillance unit to the reporting units | 0/16 0/8 | 0 0 | |
Data Quality
| Completeness of P form | 24/24 | 100 |
Completeness of L form | 24/24 | 100 | |
Timeliness
| Timeliness of report from sub-centre | 0/16 | 0 |
Timeliness of report from government reporting units | 15/16 | 93 | |
Timeliness of report from private facilities | 3/4 | 75 | |
Timeliness of report by district | 8/8 | 100 | |
Stability
| Vacant positions | 0 | 0 |
Availability of computer and internet connection for reporting | Yes | ||
S forms | Not available | ||
Representativeness
| Proportion of government facilities included in reporting network against expected | 62/68 | 91 |
Proportion of private facilities in reporting number against expected | 22/314 | 7 | |
Proportion of registered private laboratories included in reporting network | 22/228 | 9 |
A39 Analysis of measles outbreaks in Aligarh District, Uttar Pradesh, India, 2014–2018
Vikas Kumar Gupta1, Pankaj B Shah2, Rajesh Yadav3, Pankaj Bhatnagar1, Pauline Harvey1,4
1National Public Health Surveillance Project, World Health Organization Country Office, New Delhi, India; 2Shri Ramchandra Medical College, Porur, Chennai, Tamil Nadu, India; 3Division of Global Health Protection, U.S. Centers for Disease Control and Prevention, New Delhi, India; 4Global Immunization Division, U.S. Centers for Disease Control and Prevention, Atlanta, GA, USA
Correspondence: Vikas Kumar Gupta (srcaligarh@npsuindia.org)
Outbreaks | 2014 | 2015 | 2016 | 2017 | 2018 | Total |
---|---|---|---|---|---|---|
n (%) | n (%) | |||||
Identified | 13 | 16 | 2 | 19 | 12 | 62 |
Investigated with serological testing | 9 (69) | 7 (44) | 2 (100) | 17 (89) | 10 (83) | 45 (73) |
Confirmed by serologic testing | 9 (69) | 7 (44) | 0 (—) | 16 (84) | 4 (33) | 36 (58) |
Cases, median (range) | 14 (8–45) | 35 (23–73) | 0 (—) | 17 (9–31) | 15 (6–22) | 17 (6–73) |
Days (range)
| ||||||
Duration, median | 29 (9–91) | 60 (28–93) | 60 (29–91) | 42 (9–84) | 34 (8–71) | 42 (8–93) |
Median lag from rash onset in first case to notification | 23 (1–90) | 46 (18–82) | 53 (23–84) | 53 (0–91) | 22 (0–74) | 23 (0–90) |
Median lag from notification to investigation | 13 (3–28) | 14 (10–22) | 8 (7–10) | 8 (3–21) | 12 (3–26) | 13 (3–28) |
Confirmed cases
|
n (%)
|
n (%)
| ||||
Number | 158 | 295 | — | 279 | 155 | 887 |
Age (yrs), median (range) | 4 (0–35) | 3 (0–25) | — | 5 (0–30) | 4 (0–13) | 4 (0–35) |
<1 year | 27 (17) | 30 (10) | — | 31 (11) | 15 (10) | 103 (12) |
1–4 years | 76 (48) | 142 (48) | — | 137 (49) | 52 (34) | 407 (46) |
5–9 years | 40 (25) | 107 (36) | — | 92 (33) | 77 (50) | 316 (36) |
10-14 years | 12 (8) | 12 (4) | — | 16 (6) | 11 (7) | 51 (6) |
≥15 years | 3 (2) | 4 (1) | — | 3 (1) | 0 (—) | 10 (1) |
Sex, male | 70 (44) | 142 (48) | — | 158 (57) | 87 (56) | 457 (52) |
Religious minority | 79 (50) | 166 (56) | — | 116 (42) | 21 (14) | 382 (43) |
Vaccination status | ||||||
MCV 1 | 26 (16) | 21 (7) | — | 49 (18) | 14 (9) | 110 (12) |
MCV 2 | 11 (7) | 5 (2) | — | 44 (16) | 20 (13) | 80 (9) |
Unvaccinated | 103 (65) | 246 (83) | — | 146 (52) | 96 (62) | 591 (67) |
Unknown | 18 (11) | 23 (8) | — | 40 (14) | 25 (16) | 106 (12) |
A40 Unaware hypertension among persons with hypertension in the adult population of Kangra District, Himachal Pradesh, 2019
Gurmeet Katoch1, Parasuraman Ganeshkumar2, Prabhdeep Kaur2 Rajesh Guleri1 Boopathi Kanguswamy2
1Directorate of Health Services, Himachal Pradesh, India; 2ICMR-National Institute of Epidemiology, Chennai, Tamil Nadu, India
Correspondence: Parasuraman Ganeshkumar (ganeshkumardr@gmail.com)
Factors | n (%) | |
---|---|---|
Age group | 18-45 years | 23 (31.9) |
46-69 years | 38 (52.8) | |
70 years & above | 11 (15.3) | |
Gender | Male | 25 (34.7) |
Female | 47 (65.3) | |
Occupation | Retired/Unemployed | 10 (13.9) |
Homemaker | 46 (63.9) | |
Skilled/Unskilled | 16 (22.2) | |
Education | No formal schooling | 17 (23.6) |
Primary school | 9 (12.5) | |
High school & above | 46 (63.9) |
A41 Data Improvement Plan for Vaccine-Preventable Disease Surveillance, Chhattisgarh, India, 2019
Bala Ganesakumar1, Manishkumar Gawande1, Rahul Mohan Shimpi1, Mohankumar Raju2
1National Public Health Surveillance Project, World Health Organization, Delhi, India; 2ICMR-National Institute of Epidemiology, Chennai, Tamil Nadu, India
Correspondence: Bala Ganesakumar (smotirunelveli@npsuindia.org)
Categories | Indicators | (%) |
---|---|---|
Factors that influence the variations in both data (n=133) | No Verification of both data sets | 92% |
Un availability of Sample kits in PHC | 60% | |
Variations in the number of reporting sites | 63% | |
Attended any Training about VPD surveillance by IDSP in the last year | 21% | |
No Epidemiological (Time Place Person - analysis) training to BMOs | 100% | |
No any Analytical person at the Block level | 100% | |
Don’t receive feedback or analytical report from districts regularly | 92% | |
Factors that could synchronize and improve VPD Surveillance data (n=70) | One nodal person for both WHO and IDSP Surveillance | 100% |
Weekly data synchronization at the district level | 83% | |
Active case search by BMOs in other MOs registers | 94% | |
Mobile app-based data entry | 94% | |
Integrating into one system | 92% |
A42 Community-based intervention for blood pressure monitoring through Anganwadi centers in Viralimalai block, Pudukkottai District, Tamil Nadu, India, 2016-17
Subash Gandhi VC1, Parasuraman Ganeshkumar2, Kamaraj Pattabi2
1Department of Public Health and Preventive Medicine, Govt. of Tamil Nadu; 2ICMR National Institute of Epidemiology, Chennai, India
Correspondence: Parasuraman Ganeshkumar (ganeshkumardr@gmail.com)
After intervention | ||||
---|---|---|---|---|
Complaint | Non-complaint | Total | ||
Before intervention | Complaint | 128 | 4 |
132 (18%)
|
Non-complaint | 607 | 0 |
607
| |
735 (99%)*
|
4
|
739
|
A43 Evaluation of Kala-Azar Surveillance System in Muzaffarpur District, Bihar, India, 2019
Abhishek Mishra1, Amol Patil2, Nupur Roy3, Sushma Choudhary2, Tanzin Dikid1, Sudhir Jain1, Sujeet Singh1
1National Centre for Disease Control, New Delhi, India; 2South Asia Field Epidemiology and Technology Network (SAFETYNET), India; 3National Vector Borne Disease Control Programme, New Delhi, India
Correspondence: Abhishek Mishra (abhishekmishra85@gmail.com)
Attributes | Summary |
---|---|
Simplicity | Surveillance system simple to operate |
Stability | Not stable, especially in block A due to logistics and HR constrains |
Acceptability | Acceptable to government health facility |
Data quality | Average |
Timeliness | Poor for Block A because of lack of priority and resources, like net connectivity |
Representativeness | Representative only for government health facilities |
Flexibility | Flexible |
Predictive Value Positive | Low |
Usefulness | Useful in detecting early cases but scope for improvement |
A44 Description and Evaluation of the recording and reporting component of Programmatic Management of the Drug resistance Tuberculosis (PMDT) in one district, Himachal Pradesh India 2017
Vishal Thakur1, Parasuraman Ganeshkumar2
1Directorate of Health Services, Himachal Pradesh, India; 2ICMR National Institute of Epidemiology, Chennai, Tamil Nadu, India
Correspondence: Parasuraman Ganeshkumar (ganeshkumardr@gmail.com)
A45 Trend of Infant Mortality Rate in Dindigul district, Tamil Nadu, India, 2014-19
Shanmuganathan Shankarprasath1,2, Manickam Ponnaiah1, Polani Rubeshkumar1, Manoj Murhekar1
1ICMR-National Institute of Epidemiology, Chennai, Tamil Nadu, India; 2Directorate of Public Health and Preventive Medicine, Tamil Nadu, India
Correspondence: Shanmuganathan Shankarprasath (shankarprasath87@gmail.com)
A46 Evaluation of a symptom-based surveillance model for Ebola virus disease at Indira Gandhi International Airport, Delhi, India 2014–2015
Chandrakant Moghe1, Prabha Arora1, Meera Dhuria1, Sujeet Kumar Singh2, Sanjay Mattu2, Naresh Jakhar2, Ekta Sahora3, Samir Sodha3, Srinivas Venketesh1
1National Centre for Disease Control, Delhi, India; 2Ministry of Health and Family Welfare, Delhi, India; 3U.S. Centers for Disease Control and Prevention, Delhi, India
Correspondence: Chandrakant Moghe (c_moghe@rediffmail.com)
A47 Malaria outbreak with Plasmodium vivax preponderance in a tea garden despite high usage of LLINs in Kumargram block of Alipurduar district, West Bengal, India, 2019
Puran K Sharma, Subarna Goswami, Kousik Choudhury, Ananta Maji, Golam Mortuja Sk
Office of the Chief Medical Officer of Health, Department of Health & Family Welfare, Government of West Bengal
Correspondence: Puran K Sharma (puran.sharma611@gmail.com)
Characteristics | Population | Malaria cases | ||
---|---|---|---|---|
# | Attack rate per 100 population | |||
Age group | 0 – 4 | 588 | 25 | 4.3 |
5 – 14 | 1440 | 89 | 6.2 | |
15 – 29 | 2430 | 99 | 4.0 | |
30 – 44 | 2070 | 37 | 1.8 | |
45 – 59 | 1440 | 32 | 2.2 | |
60+ | 901 | 19 | 2.1 | |
Gender | Male | 4592 | 166 | 3.6 |
Female | 4410 | 135 | 3 | |
Total | 9002 | 301 | 3.3 |
A48 Capacity building in public health emergency & hospital preparedness in India, 2019 - An experience sharing
Kapil Goel1, Pinnaka Venkata Maha Lakshmi1, Manish Chaturvedi2, Jarnail Singh Thakur1
1Post Graduate Institute of Medical Education & Research (PGIMER), Chandigarh, India; 2National Institute of Health & Family Welfare, New Delhi, India
Correspondence: Kapil Goel (drkapil123@gmail.com)
A49 Assessment of delays in TB diagnosis and treatment – Revised National TB Control Program, India, 2014
Prashant Bhat1,2, Mohan Kumar Raju2, Niraj Kulshrestha3
1Department of Health and Family Welfare, Government of Karnataka, India; 2School of Public Health, ICMR-National Institute of Epidemiology, Chennai, India; 3Ministry of Health, Government of India, New Delhi
Correspondence: Prashant Bhat (bhatp74@gmail.com)
Particulars | Patient sided delay Mean (SD) [95%CI] | Diagnostic Delay Mean (SD) [95%CI] | Treatment delay Mean (SD) [95%CI] | Number of visits before diagnosis Median (Range) |
---|---|---|---|---|
Type of Provider | ||||
Private | 27(52)[23-31] | 24(46)[21-27] | 4(10)[3-4] | 2 (1-6) |
Public | 29(45)[26-33] | 11(30)[8-13] | 5(18)[4-6] | 1 (1-4) |
RNTCP Zone | ||||
North | 21(39)[18-24] | 15(35)[11-18] | 6(14)[5-7] | 2 (1-9) |
South | 20(32)[16-24] | 14(22)[11-16] | 5(4)[4-5] | 3 (1-9) |
West | 24(38)[21-26] | 14(30)[12-16] | 6(16)[4-7] | 2 (1-9) |
East | 31(69)[22-40] | 11(47)[5-18] | 5(8)[4-6] | 2 (1-9) |
A50 Evaluation of Hospital Management System in secondary care hospitals of Thiruvarur district, Tamil Nadu, 2015
Prakash Venkatesan1, Prabhdeep Kaur2
1Directorate of Public Health and Preventive Medicine, Tamil Nadu; 2ICMR- National Institute of Epidemiology, Chennai
Correspondence: Prakash Venkatesan (drprakash85@gmail.com)
Key Indicators | N | n | Proportion (%) |
---|---|---|---|
HMS operationalization
| |||
Doctors using HMS in the district | 61 | 61 | 100 |
HMS counters with functional CPU and Monitor | 166 | 148 | 89 |
HMS counters with functioning printers | 76 | 55 | 78 |
HMS counters with functioning Internet | 166 | 138 | 83 |
HMS counters in the hospitals utilized to capture patient details on the day of the survey | 166 | 107 | 65 |
HMS Counters in the 'OP Department' utilized to capture patient details on the day of the survey | 113 | 82 | 73 |
HMS Counters in the hospital other than 'OP Department' utilized for HMS on the day of the survey | 53 | 25 | 42 |
Outpatients registered in the HMS for 6-month period | 753769 | 634674 | 84 |
Outpatients who received HMS based prescriptions | 753769 | 522918 | 69 |
Outpatients for whom the pharmacists issued drugs through HMS | 753769 | 445851 | 59 |
Outpatients for whom the Lab investigations results were entered in the HMS | 334525 | 314675 | 94 |
HMS maintenance
| |||
Days for which the IT coordinator was available in the field (GH) in past six months | 135 days | 40 days | 29.6% |
Occasions by which a HMS counter in the hospital is ‘down’ due to hardware problem in six month period | 166 | 203 | 1.22 occasion / counter |
‘HMS Downtime’ hours in the hospital due to ‘Primary connectivity failure’ during OP hours (slowness) in six-month period | 9412 hrs | 3128 hrs | 33% |
‘HMS Downtime’ hours in the hospital due to both ‘Primary and Secondary connectivity failure’ during OP hours | 9412 hrs | 744 hrs | 8% |
‘HMS Downtime’ hours in the hospital due to ‘Power failure’ during OP hours | 9412 hrs | 0 | 0 |
Mean days required to rectify a breakdown in a hospital HMS counter due to a hardware problem | 93 occasions | 408 days | 4.4 days |
A51 Epidemiology of Leptospirosis infection in the state of Kerala, India during 2018-19
Ajan Maheswaran Jaya1, Sarita Ragini Lohithakshan1, Meenakshi Vasu1, Parasuraman Ganeshkumar2
1Directorate of Health Services, Kerala; 2ICMR National Institute of Epidemiology, Chennai, India
Correspondence: Parasuraman Ganeshkumar (ganeshkumardr@gmail.com)
A52 Deaths due to noncommunicable diseases among the tribal population in Mokokchung, Nagaland, 2017
Aonungdok Tushi Ao1, Prasanta Kumar Borah2, Nabajit Kumar Das2, Pankaj Uike3, Subongtemjen Sangtemkaba Longchar1, Prabhdeep Kaur3
1Health & Family Welfare Department, Nagaland; 2ICMR-Regional Medical Research Center, Dibrugarh, Assam; 3ICMR-National Institute of Epidemiology, Chennai, Tamil Nadu
Correspondence: Aonungdok Tushi Ao (nungdok@yahoo.com)
A53 Review of hypertension management services in Primary Public Health facilities in Puducherry district, 2019
Lakshmanasamy Ravivarman, Prabhdeep Kaur
ICMR-National Institute of Epidemiology, Chennai, India
Correspondence: Lakshmanasamy Ravivarman (drravivarman@gmail.com)
Logic Model of the Programme | ||
---|---|---|
Framework | Opportunistic Screening | Treatment & Counselling |
Input | Human Resources, Training manual, BP apparatus, weighing scale, stadiometer, protocol for diagnosis | Hypertensive Drugs, Funds, Protocol for treatment, IEC materials |
Process | Training for NCD nurses & medical officers Hypertension screening as per protocol | Prescribing drugs as per protocol, Counselling for long term treatment and life style modification |
Output | Hypertension cases screened Hypertension cases detected | Patients adhering to taking drugs for 1 month, Patients following lifestyle modification measures |
Outcome | Hypertension control among patients screened and treated at the facilities |
A54 Diarrhoeal disease, Water and Sanitation facility in rural Dharmapuri district, Tamil Nadu, India, 2011 – 2014
Nallathambi Yogananth1, Tarun Bhatnagar2
1Directorate of Public Health and Preventive Medicine, Chennai, India; 2ICMR School of Public Health, ICMR-National Institute of Epidemiology, Chennai, India
Correspondence: Nallathambi Yogananth (dryogananth@gmail.com)
A55 Public health budget analysis of India- A measure to attain NCD sustainable developmental goal
Ashish Krishna1, Anupam Khungar Pathni1, Bhawna Sharma1, Parasuraman Ganeshkumar2, Savitha Kasiviswanathan2
1Resolve to Save Lives; 2 ICMR – National Institute of Epidemiology, Chennai, Tamil Nadu, India
Correspondence: Ashish Krishna (akrishna@resolvetosavelives.org)
A56 A critical discourse analysis of outbreak investigation process: A study on outbreak investigation reports of Field Epidemiology Training Programme (FETP) Scholars, ICMR School of Public Health, India, 2019
Nuzrath Jahan1, Harshal Sonekar1, Manickam Ponnaiah1, Mathew George2
1ICMR School of Public Health, ICMR-National Institute of Epidemiology, Chennai, Tamilnadu, India; 2Centre for Public Health, School of Health Systems Studies-Tata Institute of Social sciences, Mumbai, India
Correspondence: Nuzrath Jahan (dr.nuz23@gmail.com)
Elements | Representative texts |
---|---|
Genres | Narrative: Mid-March 2018, we noted reports of blurred vision among school event attendees in a village in South India |
Argumentative: Claim-Contamination of drinking water could be the source of the outbreak. Warrant- Such contaminated source has been implicated to be the source of E. coli in many diarrheal outbreaks in India and elsewhere in the world. Premises- The floods following the cyclone may be a reason for the contamination of drinking water. | |
Scales of discourse | Global: Globally, an estimated 11-20 million people become ill from typhoid and between 128-161,000 people die from it annually. |
National: Typhoid fever is endemic in many parts of India with an estimated incidence of 377 (178-801) per 100,000 person-years | |
Local: In May 2018, the microbiology department of KAP Viswanatham medical college of Trichy, Tamil Nadu detected and reported a cluster of four isolates of S. Typhi resistant to Ceftriaxone. | |
Indirect intertextuality | "It has been estimated in a study that 27,486,636 DAL YS will be lost in the year 2016 in India due to diarrheal diseases." |
Clauses | Elaborative: “...about common exposures to food, water or place (travel history, mass gathering, social activity)”. |
Temporal: "Subsequently, they devised an operational case definition to diagnose cases individually." | |
Additive: “They complained of overcrowding and lack of cleanliness during their stay; they also mentioned the practice of drinking raw water daily." | |
Categorical assertion | “It is projected that by 2050, the number will increase to 10 million deaths per year.” |
Modalised assertions | “Our report may be the first report of Photokeratitis due to the indoor exposure to unshielded mercury vapour and metal halide lights from South India.” |
Passivated sentence | “Overall cleanliness was graded as good when there was no litter.” |
The omission of subjects/actors | “It is of importance to educate food handlers”. “The indoor school event occurred on..” |
Strategic disappearance and appearance of authors | “Firstly, the bias in selection could have led to an underestimation of the attack rate. However, it is less likely that we would have under-estimated the incidence.” |
Nominalisation | “Environmental investigation indicated that the water of the wells used at the ice factory and the hotel were positive for non-faecal contamination”. "This new public health problem has repercussions for the health system." |
Problem-solution structure | “Drinking contaminated water from borewell was associated with the diarrhoeal disease. Repairing the water pipelines led to the control of the outbreak”. |
Contrastive/concessive structure | "We could have had information bias for the ascertainment of exposure as well as the outcome. However, despite such bias, the magnitude of the association was beyond chance.” |
A57 Cholera Outbreak Investigation in Sangli District, Maharashtra, India, 2019
Purvi Patel1, Mohamed Azarudeen1, Sushma Choudhary2, Suneet Kaur1, Milind Pore3, Tanzin Dikid1, Sudhir Kumar Jain1, Sujeet Singh1
1National Centre for Disease Control, Delhi; 2South Asia Field Epidemiology and Technology Network; 3District surveillance unit, Sangli
Correspondence: Purvi Patel (eis6ppatel@gmail.com)
A58 Outdoor Air Pollution and respiratory diseases, Faridabad, Haryana 2018
Manoj Bajaj, Manikandanesan Sakthivel, Tarun Bhatnagar
ICMR-National Institute of Epidemiology, Chennai, Tamil Nadu, India
Correspondence: Tarun Bhatnagar (drtarunb@gmail.com)
Factors | Total patients | Total OPD | Total IPD | Asthma | Bronchitis | ARI | |
---|---|---|---|---|---|---|---|
CO and NO2 | Correlation Coefficient | .404* | .348✝ | -.078 | .287✝ | .344✝ | .366✝ |
Sig. (1-tailed) | .007 | .019 | .326 | .045 | .022 | .014 | |
N | 36 | 36 | 36 | 36 | 35 | 36 | |
CO, NO2 and PM2.5 | Correlation Coefficient | .323* | .223 | .001 | .199 | .313* | .192 |
Sig. (1-tailed) | .027 | .095 | .497 | .123 | .034 | .131 | |
N | 36 | 36 | 36 | 36 | 35 | 36 |
A59 Quality of delivery services in public sector primary care health facilities in Saidapet Health Unit District, Kanchipuram District, Tamil Nadu, India, 2017-18 - A cross-sectional quality assessment evaluation
Arun Nagamuthu, Kamaraj Pattabi, Prabhdeep Kaur
ICMR-National Institute of Epidemiology, Chennai, Tamil Nadu, India
Correspondence: Arun Nagamuthu (arun23moscow@gmail.com)
Domains | Primary Health Centers (N=20) | Community Health Centers | All (N=26) |
---|---|---|---|
Mean (SD) | Mean (SD) | Mean (SD) | |
Service Provision | 78.0 (6.1) | 78.5 (8.3) | 78.1 (6.5) |
Patient Rights | 83.4 (9.7) | 83.0 (5.7) | 83.2 (8.9) |
Inputs | 79.5 (3.4) | 77.8 (5.1) | 79.1 (3.8) |
Support Services | 84.5 (6.5) | 84.6 (6.7) | 84.5 (6.4) |
Clinical Services | 91.7 (6.3) | 77.8 (6.3) | 88.5 (6.9) |
Infection Control | 75.9 (7.9) | 77.1 (4.9) | 76.1 (7.3) |
Quality Management | 46.8 (20.2) | 37.3 (2.8) | 44.6 (18.1) |
Outcome | 45.4 (10.6) | 44.0 (5.0) | 45.0 (9.6) |
Overall | 79.4 (3.6) | 73.8 (5.0) | 78.1 (4.5) |
A60 Referral mechanism in breast cancer screening program in Tiruchirappalli district, Tamil Nadu, India, 2012-15
Vidhya Viswanathan1, Parasuraman Ganeshkumar2, Jerard Maria Selvam1, TS Selvavinayagam1
1Department of Public Health and Preventive Medicine, Government of Tamil Nadu, Chennai, India; 2Scientist D, ICMR- National Institute of Epidemiology, Chennai, India
Correspondence: Parasuraman Ganeshkumar (ganeshkumardr@gmail.com)
A61 Investigation of an acute gastroenteritis outbreak in a hilly village of Bilaspur district, Himachal Pradesh, May 2019
Harit Puri1, Polani Rubeshkumar, Parasuraman Ganeshkumar2
1Directorate of Health Services, Himachal Pradesh, India; 2ICMR- National Institute of Epidemiology, Chennai India
Correspondence: Parasuraman Ganeshkumar (ganeshkumardr@gmail.com)
Food Items | Those who ate | Those who didn’t eat | RR | 95% CI | ||||
---|---|---|---|---|---|---|---|---|
Cases | Total | Attack rate (%) | Cases | Total | Attack rate (%) | |||
Mitha | 44 | 66 | 67 | 0 | 6 | 0 | - | - |
Kadi | 44 | 67 | 66 | 0 | 5 | 0 | - | - |
Rajma | 40 | 52 | 77 | 4 | 14 | 28 | 2.7 | 1.1-6.2 |
Dal | 34 | 62 | 55 | 8 | 10 | 80 | 0.7 | 0.5-1.0 |
Rice | 44 | 72 | 61 | 0 | 0 | 0 | - | - |
A62 Kyasanur Forest Disease in Wayanad District, Kerala, India, 2019
Noona Marja K.M1, Mohan Kumar R2, Dineesh P3, Dileep4,
1District Surveillance officer, Wayanad, Kerala; 2Consultant epidemiologist ICMR-NIE Chennai; 3MPH Scholar NIE Chennai; 4District epidemiologist, Veterinary, Wayanad, Kerala
Correspondence: Noona Marja K.M (noonu28@yahoo.in)
A63 Dengue epidemiology in Kancheepuram district, Tamil Nadu, India, 2016 to 2018.
Sangeetha Subramaniyan, M SreeKalpana, Mohankumar Raju
National Institute of Epidemiology, Chennai, India
Sangeetha Subramaniyan (sangsubramaniyan@gmail.com)
A64 Family Planning Services of Tamil Nadu – An understanding from National Family Health Survey - 4, 2015-16
Mohankumar Raju, Vidhya Vishwanathan, Savitha AK
Consultant Epidemiologist, National Institute of Epidemiology, Indian council of medical research, Chennai
Correspondence: Mohankumar Raju (rmkhari2000@yahoo.com)
A65 Description and Evaluation of Influenza Like Illness (ILI) Component of Daily Disease Surveillance System in Coimbatore District Tamil Nadu, India 2018-19
Ramanathan Sarangapani1, Jerome Wesley Vivian Thangaraj2
1MPH Scholar 11th Cohort, ICMR-SPH, NIE, Chennai, Tamil Nadu, India; 2Scientist, ICMR-National Institute of Epidemiology, Chennai, Tamil Nadu, India
Correspondence: Ramanathan Sarangapani (Srnathan16@gmail.com)
A66 Causes of non-compliance to the continuum of care among patients screened for hypertension or diabetes, Sundergarh district, Odisha, 2016
Biswa Prakash Dutta1, S P Padhi3, D Bachanni4, Sameer Sodha2, Rajesh Yadav2, S Gupta1, Mohamed Shaukat4, PKB Pattnaik3, C S Aggarwal1, S Venkatesh1
1National Centre for Disease Control, Delhi, India; 2Centers for Disease Control and Prevention, Delhi, India; 3Department of Health & Family Welfare, Government of Odisha, India; 4Department of Health & Family Welfare, Government of India, Delhi, India
Correspondence: Biswa Prakash Dutta (dutta_bp@rediffmail.com)
A67 Descriptive epidemiology of dengue infection, Madurai District, Tamil Nadu, 2013-2017
Yazhini Madurapandian1, Parasuraman Ganeshkumar2, Polani Rubeshkumar2
1Department of Public Health and Preventive Medicine, Govt. of Tamil Nadu; 2ICMR National Institute of Epidemiology, Chennai India
Correspondence: Parasuraman Ganeshkumar (ganeshkumardr@gmail.com)
A68 Evaluation of Swachh Bharat Mission (Urban) in Tirunelveli city, Tamil Nadu, India, 2014-2018
Porchelvan Shanmugiah, Manickam Ponnaiah
ICMR – National Institute of Epidemiology, Chennai, India
Correspondence: Manickam Ponnaiah (manickamp@nieicmr.org.in)
A69 Description and evaluation of High-risk pregnancy detection and management of public facilities in Tiruchirappalli district, Tamil Nadu, India, 2016
Sathishkumar Ramadass1, Parasuraman Ganeshkumar2, Pattabi Kamaraj2
1Directorate of Public Health and Preventive Medicine, Tamil Nadu, India; 2ICMR-National Institute of Epidemiology, Chennai, Tamil Nadu, India
Correspondence: Parasuraman Ganeshkumar (ganeshkumardr@gmail.com)
Level | Output | Indicators | n | N | % |
---|---|---|---|---|---|
PHC | High-risk pregnancy Identified | Anaemia (Moderate) | 1278 | 9627 | 13 |
Pregnancy-induced hypertension (PIH) | 28 | 11023 | 0.3 | ||
Gestational diabetes mellitus (GDM) | 15 | 4580 | 0.3 | ||
Foetal anomalies identified | 3 | 4929 | 0.06 | ||
Treatment started | Iron sucrose injection to anaemic Mothers | 628 | 1278 | 49 | |
Blood transfusion to anaemic mothers | 19 | 628 | 5 | ||
Insulin to GDM Mothers | 0 | 15 | 0 | ||
Labetalol to PIH Mothers | 22 | 28 | 78 | ||
Referred to CEmONC Centres | Anaemic mothers referred | 44 | 1278 | 3 | |
PIH Mothers referred | 12 | 28 | 43 | ||
GDM Mothers referred | 12 | 15 | 80 | ||
HSC | High-risk pregnancy followup | Anaemia (Moderate) | 144 | 791 | 18 |
Pregnancy-induced hypertension (PIH) | 15 | 791 | 2 | ||
Gestational diabetes mellitus (GDM) | 7 | 791 | 0.8 | ||
Treatment follow up | Iron and folic acid tablets to anaemic mothers | 144 | 144 | 100 | |
Referred to CEmONC | Anaemic mothers referred | 21 | 144 | 15 | |
PIH Mothers referred | 12 | 15 | 80 | ||
GDM Mothers referred | 2 | 7 | 29 |
A70 Determinants of Place of childbirth in Chamba District, Himachal Pradesh, India
Jalam Singh Bharadwaj1, Parasuraman Ganeshkumar2, Kamaraj Pattabi2
1Directorate of Health Services, Himachal Pradesh, India; 2ICMR-National Institute of Epidemiology, Chennai, Tamil Nadu, India
Correspondence: Parasuraman Ganeshkumar (ganeshkumardr@gmail.com)
Characteristics | Categories | Unadjusted odds ratio | 95 % CI | Adjusted odds ratio | 95% CI |
---|---|---|---|---|---|
Maternal Occupation | Working | 1.0 (ref) | 1.0 (ref) | ||
Unemployed | 4.7 | 2.1 - 10.1 | 4.5 | 1.9 - 10.4 | |
Parity | Primipara | 1.0 (ref) | 1.0 (ref) | ||
Multipara | 6.3 | 3.8 - 10.4 | 6.3 | 3.7- 10.7 | |
Decision regarding place of delivery | Self | 1.0 (ref) | 1.0 (ref) | ||
Jointly by Family | 1.7 | 1.1- 2.8 | 1.7 | 1.0- 2.9 |