Antibiotics Use and Its Knowledge in the Community: A Mobile Phone Survey during the COVID-19 Pandemic in Bangladesh
Abstract
:1. Introduction
2. Methods
2.1. Study Design and Study Population
2.2. Data Collection
2.3. Data Collection Tool
2.4. Computer-Assisted Telephone Interviewing (CATI) System
2.5. Sample Size Calculation
2.6. Data Analysis
3. Results
4. Discussion
Author Contributions
Funding
Institutional Review Board Statement
Informed Consent Statement
Data Availability Statement
Acknowledgments
Conflicts of Interest
References
- Vallin, M.; Polyzoi, M.; Marrone, G.; Klintz, S.R.; Wisell, K.T.; Lundborg, C.S. Knowledge and Attitudes towards Antibiotic Use and Resistance—A Latent Class Analysis of a Swedish Population-Based Sample. PLoS ONE 2016, 11, e0152160. [Google Scholar] [CrossRef] [Green Version]
- Davies, J.; Davies, D. Origins and Evolution of Antibiotic Resistance. Microbiol. Mol. Biol. Rev. 2010, 74, 417–433. [Google Scholar] [CrossRef] [Green Version]
- O’Neill, J. Tackling Drug-Resistant Infections Globally: Final Report and Recommendations; Wellcome Trust: London, UK; Her Majesty’s Government: London, UK, 2016. [Google Scholar]
- World Health Organization. Global Action Plan on Antimicrobial Resistance. 2015; World Health Organization: Geneva, Switzerland, 2019. [Google Scholar]
- Lu, H.; Stratton, C.W.; Tang, Y.-W. Outbreak of pneumonia of unknown etiology in Wuhan, China: The mystery and the miracle. J. Med. Virol. 2020, 92, 401–402. [Google Scholar] [CrossRef] [Green Version]
- Huang, C.; Wang, Y.; Li, X.; Ren, L.; Zhao, J.; Hu, Y.; Zhang, L.; Fan, G.; Xu, J.; Gu, X.; et al. Clinical features of patients infected with 2019 novel coronavirus in Wuhan, China. Lancet 2020, 395, 497–506. [Google Scholar] [CrossRef] [Green Version]
- Xu, Z.; Shi, L.; Wang, Y.; Zhang, J.; Huang, L.; Zhang, C.; Liu, S.; Zhao, P.; Liu, H.; Zhu, L.; et al. Pathological findings of COVID-19 associated with acute respiratory distress syndrome. Lancet Respir. Med. 2020, 8, 420–422. [Google Scholar] [CrossRef]
- WHO. WHO Timeline-COVID-19. 2020. Available online: https://www.who.int/news-room/detail/08-04-2020-who-timeline---covid-19 (accessed on 29 July 2021).
- Beović, B.; Doušak, M.; Ferreira-Coimbra, J.; Nadrah, K.; Rubulotta, F.; Belliato, M.; Berger-Estilita, J.; Ayoade, F.; Rello, J.; Erdem, H. Antibiotic use in patients with COVID-19: A ‘snapshot’ Infectious Diseases International Research Initiative (ID-IRI) survey. J. Antimicrob. Chemother. 2020, 75, 3386–3390. [Google Scholar] [CrossRef] [PubMed]
- Verroken, A.; Scohy, A.; Gerard, L.; Wittebole, X.; Collienne, C.; Laterre, P.-F. Co-infections in COVID-19 critically ill and antibiotic management: A prospective cohort analysis. Crit. Care 2020, 24, 1–3. [Google Scholar] [CrossRef] [PubMed]
- Nestler, M.J.; Godbout, E.; Lee, K.; Kim, J.; Noda, A.J.; Taylor, P.; Pryor, R.; Markley, J.D.; Doll, M.; Bearman, G.; et al. Impact of COVID-19 on pneumonia-focused antibiotic use at an academic medical center. Infect. Control. Hosp. Epidemiol. 2020, 42, 1–3. [Google Scholar] [CrossRef]
- Clancy, C.J.; Nguyen, M.H. Coronavirus Disease 2019, Superinfections, and Antimicrobial Development: What Can We Expect? Clin. Infect. Dis. 2020, 71, 2736–2743. [Google Scholar] [CrossRef]
- Huttner, B.; Catho, G.; Pano-Pardo, J.; Pulcini, C.; Schouten, J. COVID-19: Don’t neglect antimicrobial stewardship principles! Clin. Microbiol. Infect. 2020, 26, 808–810. [Google Scholar] [CrossRef]
- Gagliotti, C.; Buttazzi, R.; Ricchizzi, E.; Di Mario, S.; Tedeschi, S.; Moro, M.L. Community use of antibiotics during the COVID-19 lockdown. Infect. Dis. 2021, 53, 142–144. [Google Scholar] [CrossRef]
- Butler, C.C.; Dorward, J.; Yu, L.-M.; Gbinigie, O.; Hayward, G.; Saville, B.R.; Van Hecke, O.; Berry, N.; Detry, M.; Saunders, C.; et al. Azithromycin for community treatment of suspected COVID-19 in people at increased risk of an adverse clinical course in the UK (PRINCIPLE): A randomised, controlled, open-label, adaptive platform trial. Lancet 2021, 397, 1063–1074. [Google Scholar] [CrossRef]
- Chang, C.; Lee, M.; Lee, J.; Lee, N.; Ng, T.; Shafie, A.; Thong, K. Public KAP towards COVID-19 and Antibiotics Resistance: A Malaysian Survey of Knowledge and Awareness. Int. J. Environ. Res. Public Health 2021, 18, 3964. [Google Scholar] [CrossRef]
- Elsayed, A.A.; Darwish, S.F.; Zewail, M.B.; Mohammed, M.; Saeed, H.; Rabea, H. Antibiotic misuse and compliance with infection control measures during COVID-19 pandemic in community pharmacies in Egypt. Int. J. Clin. Pract. 2021, 75, e14081. [Google Scholar] [CrossRef]
- Hicks, J.P.; Latham, S.M.; Huque, R.; Das, M.; Newell, J.; Abdullah, S.M.; Al Azdi, Z.; Jahan, I.; Rassi, C.; Hamade, P.; et al. Antibiotic practices among household members and their domestic animals within rural communities in Cumilla district, Bangladesh: A cross-sectional survey. BMC Public Health 2021, 21, 1–10. [Google Scholar] [CrossRef]
- Das, P.; Martin, D.; Banu, S.; Rahman, M.; Chisti, M.; Friedman, M. Antibiotic use of patients having acute febrile illness prior to their hospital attendance in Bangladesh. Int. J. Infect. Dis. 2020, 101, 93. [Google Scholar] [CrossRef]
- Mah-E-Muneer, S.; Hassan, Z.; Biswas, A.A.J.; Rahman, F.; Akhtar, Z.; Das, P.; Islam, A.; Chowdhury, F. Use of Antimicrobials among Suspected COVID-19 Patients at Selected Hospitals, Bangladesh: Findings from the First Wave of COVID-19 Pandemic. Antibiotics 2021, 10, 738. [Google Scholar] [CrossRef]
- Lucas, P.J.; Uddin, M.R.; Khisa, N.; Akter, S.M.S.; Unicomb, L.; Nahar, P.; Islam, M.A.; Alam Nizame, F.; Rousham, E.K. Pathways to antibiotics in Bangladesh: A qualitative study investigating how and when households access medicine including antibiotics for humans or animals when they are ill. PLoS ONE 2019, 14, e0225270. [Google Scholar] [CrossRef] [Green Version]
- Liu, B.; Brotherton, J.M.; Shellard, D.; Donovan, B.; Saville, M.; Kaldor, J.M. Mobile phones are a viable option for surveying young Australian women: A comparison of two telephone survey methods. BMC Med. Res. Methodol. 2011, 11, 159. [Google Scholar] [CrossRef] [Green Version]
- Iachan, R.; Pierannunzi, C.; Healey, K.; Greenlund, K.J.; Town, M. National weighting of data from the Behavioral Risk Factor Surveillance System (BRFSS). BMC Med. Res. Methodol. 2016, 16, 1–12. [Google Scholar] [CrossRef] [Green Version]
- Brick, J.M.; Edwards, W.S.; Lee, S. Sampling telephone numbers and adults, interview length, and weighting in the California Health Interview Survey cell phone pilot study. Public Opin. Q. 2007, 71, 793–813. [Google Scholar] [CrossRef] [Green Version]
- BTRC. Teledensity & Internet Penetration at the End of May, 2021. Teledensity (Voice + Internet Subscription) in Bangladesh May, 2021. Available online: http://www.btrc.gov.bd/teledensity-internet-penetration (accessed on 4 August 2021).
- BTRC. Mobile Phone Subscribers in Bangladesh May, 2021. Available online: http://www.btrc.gov.bd/content/mobile-phone-subscribers-bangladesh-may-2021 (accessed on 4 August 2021).
- Harris, D.; Grimshaw, J.; Lemon, J.; Russell, I.T.; Taylor, R. The Use of a Computer-assisted Telephone Interview Technique in a General Practice Research Study. Fam. Pract. 1993, 10, 454–458. [Google Scholar] [CrossRef]
- Gibson, D.G.; Pereira, A.; A Farrenkopf, B.; Labrique, A.B.; Pariyo, G.W.; A Hyder, A. Mobile Phone Surveys for Collecting Population-Level Estimates in Low- and Middle-Income Countries: A Literature Review. J. Med. Internet Res. 2017, 19, e139. [Google Scholar] [CrossRef] [Green Version]
- Choi, B.C. Computer assisted telephone interviewing (CATI) for health surveys in public health surveillance: Methodological issues and challenges ahead. Chronic Dis. Can. 2004, 25, 21–27. [Google Scholar] [PubMed]
- Hassan, Z.; Monjur, M.R.; Biswas, A.A.J.; Chowdhury, F.; Kafi, M.A.H.; Braithwaite, J.; Jaffe, A.; Homaira, N. Antibiotic use for acute respiratory infections among under-5 children in Bangladesh: A population-based survey. BMJ Glob. Health 2021, 6, e004010. [Google Scholar] [CrossRef]
- Naing, L.; Winn, T.; Rusli, B.N. Practical issues in calculating the sample size for prevalence studies. Arch. Orofac. Sci. 2006, 1, 9–14. [Google Scholar]
- Nasir, M.; Chowdhury, S.; Zahan, T. Self-medication during COVID-19 outbreak: A cross sectional online survey in Dhaka city. Int. J. Basic Clin. Pharmacol. 2020, 9, 1325. [Google Scholar] [CrossRef]
- Langford, B.J.; So, M.; Raybardhan, S.; Leung, V.; Soucy, J.-P.R.; Westwood, D.; Daneman, N.; MacFadden, D.R. Antibiotic prescribing in patients with COVID-19: Rapid review and meta-analysis. Clin. Microbiol. Infect. 2021, 27, 520–531. [Google Scholar] [CrossRef]
- Sermo.com. Breaking Results: Sermo’s COVID-19 Real Time Barometer Study. Available online: https://public-cdn.sermo.com/covid19/c8/be4e/4edbd4/dbd4ba4ac5a3b3d9a479f99cc5/wave-i-sermo-covid-19-global-analysis-final.pdf (accessed on 2 July 2021).
- Saha, T.; Saha, T. Awareness level of patients regarding usage of antibiotics in a slum Area of Dhaka City, Bangladesh. SSRG Int. J. Med. Sci. 2018, 5, 10–16. [Google Scholar]
- Haque, M.U.; Kumar, A.; Barik, S.A.; Islam, M.A.U. Prevalence, practice and irrationality of self-medicated antibiotics among people in northern and southern region of Bangladesh. Europe 2017, 8, 10. [Google Scholar]
- Biswas, M.; Roy, M.N.; Manik, I.N.; Hossain, S.; Alam Tapu, S.M.T.; Moniruzzaman, M.; Sultana, S. Self medicated antibiotics in Bangladesh: A cross-sectional health survey conducted in the Rajshahi City. BMC Public Health 2014, 14, 1–7. [Google Scholar] [CrossRef] [PubMed] [Green Version]
- Alam, N.; Saffoon, N.; Uddin, R. Self-medication among medical and pharmacy students in Bangladesh. BMC Res. Notes 2015, 8, 1–6. [Google Scholar] [CrossRef] [PubMed] [Green Version]
- World Health Organization. Clinical Management of COVID-19: Interim Guidance, 27 May 2020; World Health Organization: Geneva, Switzerland, 2020. [Google Scholar]
- Abelenda-Alonso, G.; Padullés, A.; Rombauts, A.; Gudiol, C.; Pujol, M.; Alvarez-Pouso, C.; Jodar, R.; Carratalà, J. Antibiotic prescription during the COVID-19 pandemic: A biphasic pattern. Infect. Control. Hosp. Epidemiol. 2020, 41, 1371–1372. [Google Scholar] [CrossRef] [PubMed]
- World Health Organization. AWaRe Policy Brief; World Health Organization: Geneva, Switzerland, 2019; Available online: https://adoptaware.org/assets/pdf/aware_policy_brief.pdf (accessed on 5 September 2020).
Demographic Characteristics (N = 1854) | N | % (95% CI) |
---|---|---|
Age (Years) | ||
18–30 | 1239 | 66.8 (64.7, 68.9) |
31–40 | 347 | 18.7 (17, 20.6) |
41–50 | 191 | 10.3 (9, 11.8) |
50+ | 77 | 4.2 (3.3, 5.2) |
Mean age in years (Range) | 28.5 (18–75) | |
Gender | ||
Male | 1124 | 60.6 (58.4, 62.8) |
Female | 649 | 35.0 (32.9, 37.2) |
Not willing to disclose | 81 | 4.4 (3.5, 5.4) |
Occupation | ||
Student | 842 | 45.4 (43.2, 47.7) |
Homemaker | 432 | 23.3 (21.4, 25.3) |
Service holder | 309 | 16.7 (15, 18.4) |
Businessman | 104 | 5.6 (4.7, 6.8) |
Skilled worker | 50 | 2.7 (2.1, 3.5) |
Farmer | 43 | 2.3 (1.7, 3.1) |
Unemployed | 34 | 1.8 (1.3, 2.6) |
Retired | 14 | 0.8 (0.5, 1.3) |
Doctor | 7 | 0.4 (0.2, 0.8) |
Others | 12 | 0.6 (0.4, 1.1) |
No Response | 7 | 0.4 (0.2, 0.8) |
Median household size, members (IQR) | 5 (4–6) | |
Place of residence | ||
Urban | 939 | 50.6 (48.4, 52.9) |
Rural | 876 | 47.2 (45, 49.5) |
Not willing to disclose | 39 | 2.1 (1.5, 2.9) |
Division name of the residence | ||
Dhaka | 535 | 28.9 (26.8, 31) |
Chattogram | 403 | 21.7 (19.9, 23.7) |
Khulna | 353 | 19.0 (17.3, 20.9) |
Rangpur | 202 | 10.9 (9.6, 12.4) |
Barishal | 199 | 10.7 (9.4, 12.2) |
Sylhet | 84 | 4.5 (3.7, 5.6) |
Rajshahi | 17 | 0.9 (0.6, 1.5) |
Mymensingh | 15 | 0.8 (0.5, 1.3) |
Missing/No response | 46 | 2.5 (1.9, 3.3) |
Antibiotic Use | N | % (95% CI) |
---|---|---|
Antibiotics used for reported illness (n = 257) | ||
Yes | 84 | 32.7 (27.2, 38.6) |
No | 149 | 58.0 (51.9, 63.9) |
Don’t know | 12 | 4.7 (2.7, 8) |
Missing/no response | 12 | 4.7 (2.7, 8) |
Source of antibiotic prescription (n = 84) | ||
Pharmacy | 28 | 33.3 (24.2, 43.9) |
Formal private clinic | 18 | 21.4 (14, 31.3) |
Formal (MBBS/MD) doctor | 13 | 15.5 (9.3, 24.7) |
Public healthcare facility | 12 | 14.3 (8.4, 23.3) |
Quack, village doctor | 11 | 13.1 (7.5, 21.9) |
Self-medication | 2 | 2.4 (0.7, 8.3) |
Recalled names of antibiotics prescribed (n = 84) | ||
Yes | 36 | 42.9 (32.8, 53.5) |
No | 15 | 17.9 (11.1, 27.4) |
No response/don’t know | 33 | 39.3 (29.5, 50) |
Names of the prescribed antibiotics recalled (n = 36) | ||
Azithromycin | 8 | 22.2 (11.7, 38.1) |
Cefixime | 4 | 11.1 (4.4, 25.3) |
Ciprofloxacin | 2 | 5.6 (1.5, 18.1) |
Co-trimoxazole | 1 | 2.8 (0.1, 14.2) |
Flucloxacillin | 1 | 2.8 (0.1, 14.2) |
Metronidazole | 1 | 2.8 (0.1, 14.2) |
Tetracycline | 1 | 2.8 (0.1, 14.2) |
Purchase of prescribed antibiotics (n = 84) | ||
Full course purchased | 69 | 82.1 (72.6, 88.9) |
Partial purchase | 15 | 17.9 (11.1, 27.4) |
Duration of antibiotics taken (n = 84) | ||
3–5 | 37 | 44.0 (33.9, 54.7) |
6–7 | 27 | 32.1 (23.1, 42.7) |
8–14 | 6 | 7.1 (3.3, 14.7) |
14+ | 6 | 7.1 (3.3, 14.7) |
<3 | 5 | 6.0 (2.6, 13.2) |
Missing/No response | 3 | 3.6 (1.2, 10) |
Median duration of antibiotics taken (IQR) | 5 (3–7) | |
Antibiotics used for COVID-19 positive (n = 16) | ||
Yes | 2 | 12.5 (3.5, 36) |
No | 1 | 6.3 (0.3, 28.3) |
Missing/No response | 13 | 81.3 (57, 93.4) |
Symptoms Reported for the Current Episode of Illness (N = 1854) | N | % (95% CI) |
---|---|---|
Runny nose | 173 | 9.3 (8.1, 10.7) |
Fever | 42 | 2.3 (1.7, 3) |
Cough | 29 | 1.6 (1.1, 2.2) |
Loose motion/dysentery | 23 | 1.2 (0.8, 1.9) |
Loss of smell/taste | 13 | 0.7 (0.4, 1.2) |
Sore throat | 11 | 0.6 (0.3, 1.1) |
Headache | 11 | 0.6 (0.3, 1.1) |
Injury/accident | 8 | 0.4 (0.2, 0.8) |
Urinary tract infection | 7 | 0.4 (0.2, 0.8) |
Allergy | 5 | 0.3 (0.1, 0.6) |
Difficulty breathing | 4 | 0.2 (0.1, 0.6) |
Any others | 17 | 0.9 (0.6, 1.5) |
No symptoms | 1454 | 78.4 (76.5, 80.2) |
Missing/no response | 57 | 3.1 (2.4, 4) |
Healthcare seeking behavior for the current episode of illness (n = 1854) | ||
Healthcare sought | ||
Yes | 242 | 13.1 (11.6, 14.7) |
Self-medication | 15 | 0.8 (0.5, 1.3) |
No | 1546 | 83.4 (81.6, 85) |
Missing/no response | 51 | 2.8 (2.1, 3.6) |
Healthcare seeking point (n = 257) * | ||
Pharmacy | 120 | 46.7 (40.7, 52.8) |
Formal private clinic | 41 | 16.0 (12, 20.9) |
Formal (MBBS/MD) doctor | 31 | 12.1 (8.6, 16.6) |
Self | 22 | 8.6 (5.7, 12.6) |
Quack, village doctor | 20 | 7.8 (5.1, 11.7) |
Public healthcare facility | 18 | 7.0 (4.5, 10.8) |
Followed the previous prescription | 0 | - |
Others (NGO and Homeopathy) | 5 | 1.9 (0.8, 4.5) |
Hospitalization required (n = 257) | ||
Yes | 5 | 1.9 (0.8, 4.5) |
No | 252 | 98.1 (95.5, 99.2) |
Medicine taken (n = 257) | ||
Yes | 246 | 95.7 (92.5, 97.6) |
No | 11 | 4.3 (2.4, 7.5) |
Number of medicines taken (including Antibiotic) (n = 257) | ||
1–3 | 200 | 77.8 (72.4, 82.5) |
4–5 | 31 | 12.1 (8.6, 16.6) |
>6 | 6 | 2.3 (1.1, 5) |
Missing/no response | 20 | 7.8 (5.1, 11.7) |
Antibiotic Knowledge | N | % (95% CI) |
---|---|---|
Ever heard any name of antibiotics (N = 1854) | ||
Yes | 1600 | 86.3 (84.7, 87.8) |
No | 254 | 13.7 (12.2, 15.3) |
Name of antibiotics recalled spontaneously (n = 1600) | ||
Azithromycin | 192 | 12.0 (10.5, 13.7) |
Ciprofloxacin | 106 | 6.6 (5.5, 8) |
Cefixime | 76 | 4.8 (3.8, 5.9) |
Amoxicillin | 45 | 2.8 (2.1, 3.7) |
Flucloxacillin | 20 | 1.3 (0.8, 1.9) |
Cefradine | 9 | 0.6 (0.3, 1.1) |
Metronidazole | 4 | 0.3 (0.1, 0.6) |
Ceftriaxone | 3 | 0.2 (0.1, 0.5) |
Tetracycline | 2 | 0.1 (0, 0.5) |
None | 1143 | 71.4 (69.2, 73.6) |
Knowledge of antibiotics use duration (n = 1600) | ||
Yes | 772 | 48.3 (45.8, 50.7) |
No | 821 | 51.3 (48.9, 53.8) |
Missing/no response | 7 | 0.4 (0.2, 0.9) |
Knowledge of duration (days) of antibiotics use (n = 772) | ||
1–7 | 532 | 68.9 (65.6, 72.1) |
3–5 | 133 | 17.2 (14.7, 20.1) |
8–14 | 70 | 9.1 (7.2, 11.3) |
14+ | 26 | 3.4 (2.3, 4.9) |
<3 | 11 | 1.4 (0.8, 2.5) |
Median (IQR) | 7 (3–7) | |
Knowledge of side effects of taking antibiotics without consulting a doctor (n = 1600) | ||
Harmful effects (unspecified) | 194 | 12.1 (10.6, 13.8) |
Kidney problem | 88 | 5.5 (4.5, 6.7) |
Antibiotic resistance | 56 | 3.5 (2.7, 4.5) |
Liver problem | 29 | 1.8 (1.3, 2.6) |
Heart problem | 21 | 1.3 (0.9, 2) |
Headache | 21 | 1.3 (0.9, 2) |
Fatigue | 19 | 1.2 (0.8, 1.8) |
Neurological problem | 15 | 0.9 (0.6, 1.5) |
Nausea, vomiting | 10 | 0.6 (0.3, 1.1) |
Loose motion/abdominal discomfort | 15 | 0.9 (0.6, 1.5) |
Infection | 8 | 0.5 (0.3, 1) |
Cancer, stroke liver & kidney problem | 8 | 0.5 (0.3, 1) |
Lung problem | 7 | 0.4 (0.2, 0.9) |
Death | 7 | 0.4 (0.2, 0.9) |
Fever | 5 | 0.3 (0.1, 0.7) |
Stroke | 2 | 0.1 (0, 0.5) |
Allergy/rash | 2 | 0.1 (0, 0.5) |
Gynecological problem | 2 | 0.1 (0, 0.5) |
Mineral deficiency | 1 | 0.1 (0, 0.4) |
No response | 1090 | 68.1 (65.8, 70.4) |
COVID-19 Illness | n | % (95% CI) |
---|---|---|
Tested for COVID-19 since the start of COVID-19 pandemic (Self) | ||
Yes | 111 | 6.0 (5.0, 7.2) |
No | 1665 | 89.8 (88.3, 91.1) |
Missing/no response | 78 | 4.2 (3.4, 5.2) |
COVID-19 test result (n = 111) (self) | ||
Positive | 16 | 14.4 (9.1, 22.1) |
Unknown | 2 | 1.8 (0.5, 6.3) |
COVID-19 testing facility type (n = 111) (self) | ||
Public | 90 | 81.1 (72.8, 87.3) |
Private | 18 | 16.2 (10.5, 24.2) |
Both | 3 | 2.7 (0.9, 7.6) |
Hospitalization required for COVID-19 (n = 111) (self) | ||
Yes | 4 | 3.6 (1.4, 8.9) |
No | 107 | 96.4 (91.1, 98.6) |
COVID-19 information of other family members | ||
Number of family members tested for COVID-19 since the start of the COVID-19 pandemic | ||
1 | 68 | 3.7 (2.9, 4.6) |
2 | 25 | 1.3 (0.9, 2) |
3 | 20 | 1.1 (0.7, 1.7) |
4 | 11 | 0.6 (0.3, 1.1) |
>5 | 16 | 0.9 (0.5, 1.4) |
No | 1633 | 88.1 (86.5, 89.5) |
Missing/no response | 81 | 4.4 (3.5, 5.4) |
Mean Number of family members tested for COVID-19 (Range) | 2.3 (1–15) | |
The COVID-19 test result of family members (n = 141) | ||
Negative | 111 | 78.7 (71.3, 84.7) |
One to three members positive | 18 | 12.8 (8.2, 19.3) |
Missing/no response | 12 | 8.5 (4.9, 14.3) |
Hospitalization of family members due to Corona (n = 141) | ||
Yes | 7 | 5.0 (2.4, 9.9) |
No | 133 | 94.3 (89.2, 97.1) |
Missing/no response | 1 | 0.7 (0, 3.9) |
Death of family members due to COVID-19 (n = 141) | ||
Yes | 5 | 3.5 (1.5, 8) |
No | 133 | 94.3 (89.2, 97.1) |
Missing/no response | 3 | 2.1 (0.7, 6.1) |
Publisher’s Note: MDPI stays neutral with regard to jurisdictional claims in published maps and institutional affiliations. |
© 2021 by the authors. Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (https://creativecommons.org/licenses/by/4.0/).
Share and Cite
Akhtar, Z.; Mah-E-Muneer, S.; Rashid, M.M.; Ahmed, M.S.; Islam, M.A.; Chowdhury, S.; Khan, Z.; Hassan, M.Z.; Islam, K.; Parveen, S.; et al. Antibiotics Use and Its Knowledge in the Community: A Mobile Phone Survey during the COVID-19 Pandemic in Bangladesh. Antibiotics 2021, 10, 1052. https://doi.org/10.3390/antibiotics10091052
Akhtar Z, Mah-E-Muneer S, Rashid MM, Ahmed MS, Islam MA, Chowdhury S, Khan Z, Hassan MZ, Islam K, Parveen S, et al. Antibiotics Use and Its Knowledge in the Community: A Mobile Phone Survey during the COVID-19 Pandemic in Bangladesh. Antibiotics. 2021; 10(9):1052. https://doi.org/10.3390/antibiotics10091052
Chicago/Turabian StyleAkhtar, Zubair, Syeda Mah-E-Muneer, Md. Mahbubur Rashid, Md. Shakil Ahmed, Md. Ariful Islam, Sukanta Chowdhury, Zobaid Khan, Md. Zakiul Hassan, Khaleda Islam, Shahana Parveen, and et al. 2021. "Antibiotics Use and Its Knowledge in the Community: A Mobile Phone Survey during the COVID-19 Pandemic in Bangladesh" Antibiotics 10, no. 9: 1052. https://doi.org/10.3390/antibiotics10091052
APA StyleAkhtar, Z., Mah-E-Muneer, S., Rashid, M. M., Ahmed, M. S., Islam, M. A., Chowdhury, S., Khan, Z., Hassan, M. Z., Islam, K., Parveen, S., Debnath, N., Rahman, M., & Chowdhury, F. (2021). Antibiotics Use and Its Knowledge in the Community: A Mobile Phone Survey during the COVID-19 Pandemic in Bangladesh. Antibiotics, 10(9), 1052. https://doi.org/10.3390/antibiotics10091052