Ivermectin and the odds of hospitalization due to COVID-19:

Ivermectin and the odds of hospitalization due to COVID-19: evidence from a quasi-experimental analysis based on public intervention in Mexico City


To measure the effect of Mexico City’s population-level intervention –an ivermectin-based Medical Kit – – in hospitalizations during the COVID-19 pandemic. Methods A quasi-experimental research design with a Coarsened Exact Matching method using administrative data from hospitals and phone-call monitoring. We estimated logistic regression models with matched observations adjusting by age, sex, COVID severity, and comorbidities. For robustness checks separated the effect of the kit from phone medical monitoring; changed the comparison period, and subsetted the sample by hospitalization occupancy,


We found a significant reduction in hospitalizations among patients who received the ivermectin-based medical kit; the range of the effect is 52%- 76% depending on model specification.

Conclusions The study supports ivermectin-based interventions to assuage the effects of the COVID-19 pandemic on the health system.


State of the evidence and discussion on ivermectin and COVID-19 Once COVID-19 cases are identified, early home interventions can reduce hospitalizations by treating patients in early stages. However, there is no standardized pharmacological treatment for COVID-19, nor a medical consensus about how to prevent those with mild or moderate symptoms from developing severe symptoms (Siemieniuk, R. A, 2020); mainly among patients who have not been hospitalized (Katherine J. Et Al, 2020). Uncertainty about the best way to treat infected patients translates into difficulty in designing population-based interventions. Ivermectin is a Food and Drug Administration (FDA) approved broad-spectrum antiparasitic drug used in the control of several tropical diseases (Navarro. M. et al, 2019). It was associated with COVID-19 treatment because in vitro lab studies showed that it can diminish SARSCoV-2 viral load (See Caly, et al 2020). The proposed antiviral action on coronavirus suggests that it inhibits the binding capacity of the virus to a protein that would lead it into the nucleus. This would avoid an exaggerated immune response, leading to a normal and efficient antiviral response, suggesting that “ivermectin’s nuclear transport inhibitory activity may be effective against SARS-CoV-2” (Caly, et al., 2020, 1). Some remarks against the use of ivermectin state that, to have effects similar to those shown in the in vitro test, doses higher than those usually administered would be necessary. Administration without medical follow up can have adverse effects in immunosuppressed people, and could cause negative interactions with other medical treatments (Chaccour, C. 2021)

Case study:

Policy intervention in Mexico City Facing an accelerated increase in COVID-19 cases and with critical levels of hospital saturation during December 2020, the Mexico City Government decided to expand population-based health interventions. This expansion consisted of the implementation of a prehospital home-care program that combines early detection with antigen tests, a phone-based follow-up for positive patients, and the provision of a medical kit containing ivermectin. The World Health Organization (WHO) recommended the early detection of COVID-19 cases. The Mexico City Government, therefore, extended the testing program, from health centers and hospitals into a massive testing program in 230 temporary mobile units called “kiosks”. These were opened in areas based on priority determined by COVID-19 incidence, sociodemographic characteristics, and ease of access1. The objective of the program is to reduce access barriers to identifying the infection at early stages, cut transmission chains through home isolation, and promptly attend positive cases. Some kiosks are rotated each week based on selection criteria fluctuations, and such that access is not restricted based on place of residence. The mass testing program began on July 8th, 2020 with 3,000 tests administered daily. By mid-November, capacity was expanded to 24,000 daily tests. The early detection of cases is complemented with a follow-up system for positive patients through Located (the Mexico City Government call center). Located contacts all patients who have tested positive for SARS-CoV-2 by telephone and by Whatsapp text message. In the call, patients who’ve not learned their test results are informed of the results and referred to a doctor through another phone call where appropriate. All positive patients are asked if they are in isolation. Alarming symptoms are monitored and a follow-up every two days is offered. If alarming symptoms are identified, the patient is referred to a doctor who evaluates the case through breathing exercises, and if necessary, the patient may be contacted by video call to assess other symptoms. Serious cases are immediately transferred to 911.


Research design To assess the effect of ivermectin on hospitalizations in Mexico City, we used a quasi-experimental research design. We make use of statistical methods that match cases based on observable co-variants, reducing the possible imbalance on those variables, and allowing us to estimate systematic differences in the dependent variable (i.e. hospitalization); between those who received the medical kit and those who did not. This method recreates the randomization of treatment by statistically making those treated and untreated indistinguishable on all relevant co-variants except the existence of the treatment (i.e.; got the medical kit with ivermectin or did not). We used the Coarsened Exact Matching method to match observations. This method belongs to the class of Monotonic Imbalance Bounding methods, in which balance between the control and treatment groups is chosen by the user and not by the continuous reestimation process (Blackwell, M., 2009).

Data sources and analytical sample

The sample used for this study was built through the merger of three data sources. First, all of the records of positive tests for COVID-19 registered in the SILVER system (Epidemiological Surveillance of Respiratory Diseases System), from 23 November 2020 to 28 January 2021. We selected individuals who were positive outpatients, both from tests performed at the kiosks and from Family Medical Units

From this database, we used comorbidities, symptoms, and some sociodemographic variables. Second, a database that integrates hospitalization data collected in Mexico City by public hospitals (such as SEDESA, IMSS, ISSSTE, CCINSHAE, and SEMAR4 ), from 24 November 2020 to 8 February 2021. Third, the Located telephone follow-up system, which takes advantage of the SILVER records to contact positive cases. The three data sources are merged using the Unique Population Registry Code (CURP), a national identifier unique to each Mexican citizen and legal resident. This allowed for the matching by this ID variable to the records of the three databases.

Measures Dependent/outcome variable:

Hospitalized, the dichotomous variable that identifies whether or not the person was hospitalized. Independent/Treatment Variable: I. Medical Kit: Dichotomous variable of each person who received the medical kit including ivermectin is assigned a 1 and those who do not receive it a 0. II. Located follow-up: A dichotomous variable in which 1 was assigned to people who agreed to receive telephone and medical follow-up via Located.

Identification Strategy After balancing the observations over the covariates with CEM, we run a robust binomial logistic regression to estimate the probability of being hospitalized, conditional on controls, and delivery of the medical kit. This guarantees that cases are identical in all factors but the presence or absence of the treatment. Given the data sources and distribution, there are some legitimate criticisms to be made on the potential confounding: 1. We can’t separate periods from treatment in the administrative data; 2. Along with the medical kit, patients are also subject to telephone medical monitoring, so we need to disentangle the effect of medication from attention; 3. Related to the first point, in the later period when the kit program began, the percentage of occupied beds was visibly higher, thus, we need to show that the effect of the medical kit with ivermectin holds at similar levels of hospitalizations. We perform some robustness checks and sub-sampling specifications to confirm that the effect of the ivermectin-based kit on the probability of hospitalization holds regardless of time, hospital saturation, and medical follow-up.

Results In all the specifications, we found a negative and significant effect of the ivermectin-based medical kit on the probability of hospitalization among the patients who received it vis-a-vis those who did not. Depending on the subsampling, the effect ranges from 50% to 76% difference in hospitalization odds between treated and untreated patients, statistically significant in all cases. Credited to José Merino I (i Digital Agency for Public Innovation, Mexico City) buy ivermectin | buy ivermectin India | buy ivermectin | buy ivermectin India | ivermectin tablet for humans | ivermectin tablet price ||ivermectin 12 mg tablet price in India | ivermectin buy online | where to buy ivermectin for humans | ivermectin dosage | where to buy ivermectin UK | ivermectin uses | ivermectin | Stromectol |buy ivermectin online | buy ivermectin online UK | buy ivermectin online NZ | buy ivermectin online south Africa | buy ivermectin online Malaysia | Buy Stromectol (ivermectin) Online at Lowest Price | Buy Ivermectin for Covid 19 Over the Counter |

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