Due to their key role in the pathogenesis of cancer through the regulation of apoptosis, the B-cell leukemia/lymphoma-2 (BCL-2) family proteins have been an attractive target for cancer therapy for the past decades. Throughout the years, many Bcl-2 family inhibitors have been developed, with Venetoclax being now successfully used in treating hematological malignancies. Although their effectiveness in the treatment of solid tumors is yet to be established, some preclinical evidence indicates their possible clinical application. This review aims to summarize current data from completed clinical trials that used Bcl-2 protein family inhibitors as monotherapy or in combination with other agents for the treatment of solid malignancies. We managed to include clinical trials of various phases which analyze the pharmacokinetics and pharmacodynamics of the drugs, as well as the effectiveness and adverse effects. Active and recruiting clinical trials are also briefly presented and future prospects and challenges are discussed. Cancer is defined by unregulated cell division and growth. The main pathogenetic mechanisms leading to carcinogenesis include mutations in oncogenes or tumor-suppressor genes, epigenetic or chromosomal alterations and environmental stress, often resulting in the evasion of apoptosis , 2. ApopOne of the main regulators of the intrinsic pathway is the B-cell leukemia/lymphoma-2 (Bcl-2), a family of regulatory proteins, which can be subclassified into different groups based on their morphology and Bcl-2 homology (BH) domain Fig. . The BH3It has been observed that in many types of cancer there is an up-regulation of anti-apoptotic proteins and a down-regulation of pro-apoptotic members of the Bcl-2 family . The malDespite major breakthroughs in hematological neoplasms, the effectiveness of these drugs in solid tumors is still under investigation. Several clinical trials have been conducted regarding the role of different Bcl-2 protein family inhibitors in solid malignancies. In this narrative review we sought to provide an update on the available evidence from clinical trials of Bcl-2 inhibitors in solid malignancies and to briefly state the limitations, challenges and future goals of this anticancer approach.Oblimersen sodium is an antisense oligonucleotide compound which targets the first 6 codons of the human Bcl-2 mRNA sequence, leading to degeneration of the Bcl-2 mRNA and decreased Bcl-2 protein production. It is the first oligonucleotide known to have an antisense effect on Bcl-2 protein translation . AlthougIn a phase I dose-escalation, multicenter study in 2000 oblimersen sodium was combined with irinotecan in patients with metastatic colorectal carcinoma to assess the pharmacokinetic behavior and the Bcl-2 protein inhibition in peripheral blood mononuclear cells (PBMCs), since preclinical studies have detected Bcl-2 overexpression in colorectal carcinoma specimens compared to normal colonic epithelium –15. The It is suggested that chemoresistance in metastatic melanoma can be attributed to high antiapoptotic activity by the Bcl-2 protein family. Under these circumstances, a randomized phase III trial was conducted in 2000 to compare the effectiveness of dacarbazine with or without oblimersen in advanced malignant melanoma. The overall survival (OS) time was not improved in the arm who received combined therapy, but the progression-free survival (PFS) time was significantly increased , 18. No Another randomized, phase II study in 2004 assessed the antitumor activity and safety of oblimersen sodium when administered before docetaxel versus docetaxel alone among patients with castration-resistant prostate cancer (CRPC). It has been found that Bcl-2 is upregulated in prostate cancer cells, leading to androgen independence and subsequent chemoresistance to docetaxel , 20. TheAlthough some studies exhibited a synergistic effect between oblimersen and specific chemotherapeutic agents, the results were not significant enough to continue examining this drug. The fact that the last study of oblimersen sodium in solid tumors we managed to find was more than a decade ago shows that this Bcl-2 inhibitor is probably no longer used in clinical trials.Unlike other more selective Bcl-2 inhibitors, obatoclax is considered a pan-Bcl-2 inhibitor, meaning that it targets all anti-apoptotic Bcl-2 family proteins . Thanks In 2006, an open-label, phase I/II study was conducted in patients with NSCLC that relapsed after first-line platinum-based treatment. NSCLC cells are known to overexpress Bcl-2 antiapoptotic proteins , so the In 2007, an open-label, single-arm phase II study evaluated the efficacy of combining topotecan with obatoclax mesylate in patients with relapsed SCLC. Topoisomerase inhibitors, like topotecan, trigger apoptosis by causing DNA damage to cancer cells. Antiapoptotic agents like the Bcl-2 family proteins can hinder this mechanism and lead to treatment resistance. Considering the above, a Bcl-2 inhibitor was expected to increase the chemotherapeutic effect. However, the addition of obatoclax did not manage to exceed the response rate of topotecan monotherapy in these patients. The most common adverse effects were neurologic, including ataxia and somnolence, lasting no more than 2 h. Hematologic toxicities were relatively infrequent, but more severe and required blood and platelet transfusion. Even so, it is worth mentioning that topotecan alone has a response rate as low as 7–10% in patients with platinum-refractory SCLC and, considering the small number of patients enrolled, it was not expected to observe a significantly higher efficacy .In a more extended, randomized, phase II study in 2008 obatoclax mesylate was added to carboplatin/etoposide chemotherapy as first-line treatment in patients with extensive-stage SCLC. As expected, transient neurologic and psychiatric adverse effects were present in the obatoclax + carboplatin/etoposide arm, but no treatment discontinuation was needed. Unfortunately, the addition of obatoclax did not prove to significantly increase objective response rate .In conclusion, obatoclax mesylate was not reported to add to the efficacy of other cytotoxic agents, neither in previously untreated nor in platinum-resistant lung cancer. To our knowledge, there are currently no recruiting clinical trials of obatoclax mesylate in patients with solid tumors.Navitoclax is one of the Bcl-2 family protein inhibitors which shows a high affinity to Bcl-2, Bcl-W and Bcl-xL antiapoptotic proteins. Early preclinical studies indicate that navitoclax alone is potent to cause suppression in tumors that rely on the overexpression of antiapoptotic Bcl-2 proteins for their survival, such as SCLC, acute lymphocytic leukemia (ALL) and fibrotic diseases . In thisIn 2007, a non-randomized Phase I study was conducted to evaluate tolerability, pharmacokinetics, and efficacy of navitoclax in SCLC and other solid tumors. Overall, navitoclax was well tolerated with most of treatment-related toxicities being grade 1–2. All patients experienced thrombocytopenia, a known adverse event of drugs that inhibit Bcl-xL, which was dose-dependent and manageable. This study also indicates that in some cases of SCLC, MCL-1 rather than Bcl-2/Bcl-xL is responsible for evading apoptosis. This might explain why these tumors show insensitivity to navitoclax and other Βcl-2/Bcl-xL inhibitors. Combination of navitoclax and other drugs that downregulate the expression of MCL-1 could subsequently have promising results in these tumors . Phase IIn a prospective, multicenter Phase II study in 2015 navitoclax was tested as a single agent on women with heavily pretreated, platinum-resistant ovarian cancer (MONAVI-GINECO study), since a promising antitumor effect on chemo-resistant ovarian cancer cells was demonstrated during preclinical trials. Thrombocytopenia was the main side-effect, but it was reversible with no significant bleeding or toxicity-related deaths. Unfortunately, navitoclax in monotherapy exhibited no significant antitumor activity. No correlation between the expression of the pro-apoptotic BIM and the anti-apoptotic MCL-1 with disease progression was observed from the analysis of tumor biopsies .In the same year, another Phase Ib study evaluated the safety and feasibility of navitoclax in combination with osimertinib among patients with epidermal growth factor receptor (EGFR)-mutant NSCLC who had exhibited resistance to prior tyrosine kinase inhibitor (TKI) exposure. Preclinical studies had shown that increased apoptotic activity significantly enhances the anti-tumor effect of a third generation TKI, possibly resulting in stronger and more long-lasting tumor regression in clinical models. Early thrombocytopenia was, as expected, the major adverse effect. However, this time the combination treatment not only proved to be safe, but also demonstrated clinical efficacy. It is indicated that further investigation of Bcl-2 inhibition and osimertinib combination is needed to validate these outcomes .A low platelet count was reported as the principal side-effect in almost all clinical trials of navitoclax in monotherapy or in combination treatment. This is a result of navitoclax directly inducing the apoptotic death of platelets, as it inhibits their main anti-apoptotic factor, Bcl-xL . AlthougPalcitoclax is a highly potent Bcl-2 family protein antagonist and, like navitoclax, targets mainly the Bcl-2 and Bcl-xL antiapoptotic proteins. It was developed in an effort to reduce on-target platelet toxicity while maintaining high anticancer activity. Preclinical studies have shown that palcitoclax can achieve tumor suppression in multiple xenograft models, including ALL, SCLC, colorectal and breast cancer , 34. We In 2017 the first in-human phase I study of palcitoclax was conducted to evaluate the safety, pharmacokinetics, and efficacy of the drug among US patients with metastatic SCLC or other solid malignancies . AlthougGossypol, a complex compound, naturally produced by cotton plants, was first discovered in the 1950s in China, as cooking with crude cottonseed was found to cause infertility in men , 36. TheIn 2007 a double- blind, multicenter, randomized phase II study was conducted to evaluate the efficacy and tolerability of AT-101 in combination with docetaxel for the treatment of relapsed NSCLC. In preclinical prostate and lung cancer models, docetaxel and AT-101 were found to have a synergistic or even additive effect. However, in this clinical trial the combination treatment did not achieve an improved PFS. The adverse effects in the docetaxel + AT-101 arm were generally the same as in the docetaxel + placebo arm, with the exception of headache, which was significantly more frequent in the docetaxel + AT-101 group (11.3% compared to 0%). An increase in median OS by 1.9 months was reported in the AT-101 group, but it did not meet statistical significance .Another randomized, double-blind, placebo-controlled, phase II study also in 2007 compared the potency of AT-101 in combination with docetaxel and prednisone (ADP arm) versus docetaxel and prednisone plus placebo (placebo-DP arm) in patients with chemotherapy-naïve metastatic hormone refractory prostate cancer. The ADP arm was associated with an increased incidence in cardiac and hematologic adverse events, as well as pulmonary embolism and peripheral neuropathy. Efficacy endpoints  were found to be increased in the subgroup of high-risk patients of the ADP arm, suggesting that Bcl-2 family protein expression may play a more significant role in such patients. However, no statistically significant difference in OS or PFS was observed between the two arms .In 2012 a new therapeutic approach to advanced laryngeal cancer was tested with the combination of platinum, docetaxel and AT-101 in a randomized, phase II study. While adverse effects were overall more manageable, AT-101 did not improve disease response .In a recent study with both preclinical and clinical aspects among patients with gastroesophageal cancer the combination of AT-101 with docetaxel, fluorouracil, and radiation exhibited surprisingly encouraging results, suggesting that the proapoptotic effect of AT-101 successfully overcomes the antiapoptotic pathways of gastroesophageal cancer stem cells . HoweverIn conclusion, AT-101 was well-tolerated in all combination treatments, but in most studies improvement in primary endpoints did not reach statistical significance.In an effort to limit the adverse hematological toxicities of navitoclax, a new highly selective Bcl-2 inhibitor was introduced. Unlike other Bcl-2 inhibitors, which target more than one anti-apoptotic proteins, venetoclax is a selective inhibitor of the Bcl-2 protein and was first FDA approved in 2016 for the treatment of CLL, especially with 17p deletion . It is nIn 2018 a randomized, phase II study (VERONICA) compared the efficacy of venetoclax in combination with fulvestrant compared with fulvestrant alone in women with estrogen receptor (ER)-positive, HER2-negative, locally advanced or Metastatic Breast Cancer (MBC) who experienced disease recurrence or progression during or after treatment with CDK4/6i therapy. Preclinical studies support that Bcl-2 is overexpressed in approximately 85% of primary ER-positive breast cancer cases, so the inhibition of the antiapoptotic protein could enhance the therapeutic effect of endocrine therapy with fulvestrant in such patients. However, the clinical benefit rate and the PFS were not significantly improved in the venetoclax + fulvestrant arm. More serious (grade 3–4) adverse events were also observed in the combination treatment . As of OLP-118 is an oral selective Bcl-2/Bcl-xL inhibitor and it is the newest of all drugs we have included in this study. Its special feature is that its anti Bcl-xL activity is adjusted to the minimum so that the risk of thrombocytopenia is limited . We repoAs noted earlier, Bcl-2 inhibitors have proven their effectiveness in treating hematological malignancies through multiple preclinical and clinical studies for many years now. Their crucial role in evading apoptosis still renders them as one of the most promising targets for cancer treatment. Early data from preclinical studies have supported that some Bcl-2 family proteins are overexpressed in many solid tumors too, raising a big question; are Bcl-2 inhibitors as effective in solid tumors as in hematological? To answer this question, we examined a great number of clinical trials using the Bcl-2 inhibitors oblimersen sodium, obatoclax mesylate, navitoclax, palcitoclax, AT-101, venetoclax and LP-118. The most frequently utilized approach seems to be the co-administration of a Bcl-2 inhibitor with other anticancer agents, such as but not limited to conventional chemotherapy.Oblimersen sodium and obatoclax mesylate did not manage to demonstrate statistically significant clinical efficacy in any case of solid malignancies. Further investigation is not suggested, and we report no recruiting clinical trials of these agents. AT-101 was found to successfully target cancer stem cells of gastroesophageal cancer in one clinical trial  but failIn terms of overall safety and toxicity, the adverse events that occurred could be explained by both the co-administered agent and the Bcl-2 inhibitor itself. More specifically, common toxicities included severe neutropenia and thrombocytopenia (grade 3 and 4), anemia, lymphopenia, fatigue, diarrhea, vomiting, hepatic and metabolic disturbances. However, few instances of serious adverse events such as myocardial infarction and pulmonary embolism were recorded. Neurologic adverse events were reported only in groups receiving palcitoclax, but were transient and did not require any intervention. Thrombocytopenia was more prominent with navitoclax and was attributed to direct inhibition of Bcl-xL. Results from most randomized trials exhibited a higher incidence of hematologic toxicities among patients receiving combination treatment compared to conventional chemotherapy. Although these adverse events were manageable, it is suggested that the addition of Bcl-2 inhibitors to chemotherapeutic agents can aggravate the hematologic toxicities.The main hindrance in evaluating the efficacy of these drugs was the limited number of participants, which predisposes to statistical errors, while a non-negligible number of patients had other comorbidities that could potentially aggravate the rate and severity of occurring adverse events. It is also worth mentioning that most trials were completed 10 or more years ago. Since then, patient care has evolved substantially and a variety of new chemotherapeutic agents has been released. These observations emphasize the necessity for renewed and more extended randomized clinical trials with stricter eligibility criteria regarding patients’ health status, as a means of objectively identifying clinical efficacy and safety issues.To our knowledge, this is the most recent review to summarize clinical trials of Bcl-2 inhibitors on solid tumors. It is evident that Bcl-2 inhibitors do not seem to be as efficient against solid tumors as they are against hematological cancers, when used as single agents. However, in combination with other anticancer drugs they are likely to enhance their antitumor effect while maintaining a good safety profile. We also highlight the importance of further preclinical research which may pave the way for new, more potent combinations of Bcl-2 inhibitors with other targeted agents. Currently 10 clinical trials are recruiting with their primary endpoints being the assessment of maximum tolerated dose, safety and adverse events, pharmacokinetic, pharmacodynamic parameters and effectiveness (Table


P = .019). From the ROC curve, the sensitivity was same at 95.5% for both methods, but the specificity of the urine-tenofovir point-of-care test was greater (96.6% vs 95.5%) than pharmacy refill record (P = .837). Urine-tenofovir point-of-care test provided equivalent adherence data to pharmacy refill data.Pharmacy refill records (PRR), are an accessible strategy for estimating adherence in low- and middle-income countries (LMICs). However, the low-cost urine-tenofovir point-of-care test opens up the possibility of an objective metric of adherence that is scalable to LMICs. This study compared adherence to tenofovir-based regimens using urine-tenofovir point-of-care (POC) test with pharmacy refill records in a Nigerian population of HIV-positive persons. This was a cross-sectional study among 94 HIV-positive adults, which was conducted from June to August 2021, in a large outpatient clinic in Lagos, Nigeria. Adherence to pharmacy appointments was automatically calculated using a computerized pharmacy appointment system (FileMaker Pro™). Urine drops on the urine-tenofovir POC test strip developed 2 lines for a negative test (tenofovir absent) and one line for a positive test. Fisher’s exact test was used to examine the association between pharmacy refill record and urine-tenofovir point-of-care test. Logistic regression was performed to predict viral suppression  using both methods of adherence determination. A Receiver Operating Characteristic (ROC) curve of the association between specificity and sensitivity was generated to evaluate the predictive value of adherence determined using pharmacy-refill record and urine-tenofovir point-of-care test in forecasting viral suppression. The statistical significance level was set at 0.05. Fisher’s exact test showed no statistically significant difference in adherence using urine-tenofovir point-of-care test or pharmacy refill record. The logistic regression model showed that an increase in pharmacy-refill record of ≥ 95% was associated with viral suppression ( Sixty-five percent of these persons living with HIV (PLHIV), were from Sub-Saharan Africa. Nigeria, a West African Country with a population of 313.4 million people reported an HIV prevalence of 1.4% in 2018. Nigeria has the third largest population of PLHIV,,4 and has also been referred to as “the largest HIV epidemic in Western and Central Africa.”There were an estimated 39 million people globally living with the Human Immunodeficiency Virus (HIV) at the end of 2022. In order words, 95% of the population of these countries knew their HIV status, 95% of those who knew their HIV status were on life-saving antiretroviral therapy, and 95% of PLHIV who were on antiretroviral therapy, had suppressed HIV viral load results. The level of the USAID 95-95-95 targets in Nigeria in 2022 were 86:89:93 among adults aged 15-45 years old. Nigeria has adopted policies which “lead to the path that ends AIDS.” Some of these policies include adoption of the World Health Organisation recommendation of rapid initiation of antiretroviral therapy (ART) within 7 days of HIV diagnosis . The treat-all policy (regardless of CD4 count) is implemented in over 95% of all HIV treatment sites for adults and adolescents in the country. Other policies such as multi month dispensing, 6-monthly viral load monitoring (for stable patients) or 3-monthly for patients who have unsuppressed viral load results, are outlined in the National HIV Treatment Guidelines.The world is making good progress towards the targets that were set to end the HIV epidemic by 2030. Five Southern African countries were reported to have achieved the 95-95-95 targets in 2022. Adherence is a patient’s ability to follow a treatment plan, take medications at prescribed times and frequencies, and follow restrictions regarding food and other medications. There are few antiretroviral drugs available for combination antiretroviral therapy options in low- and middle-income countries (LMIC). Ensuring proper adherence to available antiretroviral drugs is very important to avoiding the development of resistance to the few accessible antiretroviral drugs.The Nigerian National Treatment Guidelines recommend intensified adherence counseling for patients with unsuppressed viral load. Unlike most other currently available objective tests , which involve expensive and time intensive mass-spectrometry-based methods, antibody-based urine-tenofovir immunoassay which can be performed at the point-of-care (POC), is easy to conduct, and results are available in real-time (3–5 minutes). A point-of-care test allows for a rapid assessment of adherence levels in a clinic, as well as providing immediate feedback to an individual patient, which can help initiate intensive adherence counseling targeted to the patient. Point-of-care monitoring and feedback could improve adherence to ART and help increase viral suppression rates.Adherence assessment is critical to the success of ART and pre-exposure prophylaxis (PrEP). Commonly used measures of adherence such as “self-report,” or “pharmacy-pick-up”/ “pharmacy refill records,” are acclaimed not to be as reliable as objective adherence tests measuring antiretroviral (ARV) concentrations in biological samples. Paired urine and plasma tenofovir concentrations have been shown to be highly correlated and measuring tenofovir levels in urine assesses adherence. Beyond the high predictive utility of an adequate urine-tenofovir level to assess adherence to PrEP, urine-tenofovir concentrations can evaluate time since dosing. Since tenofovir is the most widely used nucleotide reverse transcriptase inhibitor in HIV treatment and prevention, the University of California, San Francisco Hair Analytical Laboratory helped to develop a highly selective antibody against tenofovir which was converted into an enzyme-linked immunosorbent assay and validated via liquid chromatography/tandem mass spectrometry. A lateral flow assay, which permits POC testing, was then developed, which examines recent adherence over the last 4 to 7 days based on a cutoff of 1500 nanograms (ng)/milliliter (mL) of tenofovir. Absence of tenofovir detected by our assay predicted future PrEP seroconversion in large demonstration projects (iPrEx OLE study and Partners PrEP).,14 Moreover, urine collection has been demonstrated to be highly acceptable to research participants and patients.,15,16Concentrations of tenofovir in urine have been successfully used to monitor adherence to PrEP.; and pregnant women, for whom adherence is very important to prevent mother-to-child transmission of HIV.The urine-tenofovir POC test will be very useful for special populations such as adolescents and young adults who are reported to have problems with adherence to ARTSince self-reported adherence and pharmacy-refill record are easy to assess in LMIC, these metrics are used most often to estimate population or individual level adherence. However, the low-cost urine tenofovir test opens up the possibility of an objective metric of adherence that is scalable to LMICs.The purpose of this study was therefore to compare adherence to tenofovir-based antiretroviral regimens by the urine-tenofovir point-of-care test and pharmacy-refill record in a Nigerian population of people living with HIV, and to compare the HIV viral load suppression predictive ability of each measure of adherence.The Outpatient HIV Clinic in Lagos, Nigeria, is situated in the Nigerian Institute of Medical Research which is charged with conducting research relevant to the country. The Institute was among the 25 centers selected in 2002 to implement the Federal Government of Nigeria’s antiretroviral drug access programme. The Institute was selected principally to conduct clinical research in the large antiretroviral therapy (ART) access programme. Cumulatively over 24,000 HIV positive individuals have been enrolled into the HIV programme since its inception. A diverse population of persons living with HIV (PLHIV) access care at the Nigerian Institute of Medical Research clinic. The clinic population include infants exposed to HIV, children and adolescents, pregnant women, and other adults. This cross-sectional study to evaluate the relationship between urine-tenofovir test results and pharmacy refill records was conducted between June to August 2021.This was a cross sectional study.Ethical approval for the study was obtained from the Institutional Ethics Review Board of Nigerian Institute of Medical Research (IRB-16-360 of 21-06-2022). All study data was de-identified, confidentiality of participants and their data was maintained all through the study. Informed consent was obtained from study participants, refusal to participate in the studies, did not affect the quality of care a patient received at the clinic.HIV-positive adults (≥18 years old) on first-line or second-line tenofovir-based ART regimen.Patients on non-tenofovir-based ART regimens. This is because the urine-tenofovir test can only detect tenofovir metabolites and would not react to metabolites of other antiretroviral drugs.,19 Assumptions made in the sample size calculation were, 5% precision, 5% type 1 error, and population size < 10,000. was The calculated sample size was ninety-one (91), and 100 persons were assessed for study participation eligibility. Four of those assessed were ineligible because they were not on tenofovir-based ART regimens while 2 persons declined providing consent. Ninety-four study participants who were PLWH on tenofovir disoproxyl fumerate-based regimens (on first- or second-line ART) were therefore recruited consecutively from the clinic and consented for participation.Sample size calculation for the study was based on proportion of 27% of non-adherence reported for pharmacy refill records from 2 African studies.The urine tenofovir immunoassay was donated by Hair Analytical Laboratory, San Francisco, and the following steps conducted for each participant:Each participant was presented with a cup and asked to provide a urine sample (about 5–10 mL).They were asked to screw the lid on to the urine cup after collection and bring it back out to the researcher.The researcher (using gloves) used the dropper provided with the urine test to drip 3 to 4 drops of urine from the urine cup into the divot of the urine test strip  which uses the total number of days that the patient was late/absent for drug-refill visits to calculate adherence to ART. Optimal and suboptimal adherence was defined as a cumulative adherence to drug-refill visits of ≥ 95% and < 95% respectively. The adherence measure with the greatest total area was interpreted as having the best prediction of viral load suppression.Statistical tests: International Business Machine (IBM) Statistical Package for Social Sciences (SPSS) version 26.0, Armonk, NY: IBM Corporation USA was used for statistical analysis. Statistical significance was set at 5% (0.05) level of probability. Fisher’s exact test was used to examine the association between pharmacy pick-up data (≥95% and < 95%) and the tenofovir urine assay (yes/no). A logistic regression was performed to predict viral suppression with adherence measured using the urine tenofovir POC test and pharmacy refill records. A receiver operating characteristic (ROC) curve of the association between specificity and sensitivity was generated to evaluate the predictive value of pharmacy refill records and the urine POC test, in forecasting viral suppression. The ROC curve was constructed by plotting the false positive rate (1 minus specificity) against the true positive rate (sensitivity). Detectable viral load was defined as greater than 1000 copies/mL.A total of 94 participants were recruited for the study Table  majorityThe result of the Fisher’s exact test, which is shown in Table 2 (2) = 10.627, P = .005  of the variance in viral suppression and correctly classified 94.7% of cases. It also showed that an increase in pharmacy refill rate of ≥ 95% was associated with viral suppression (P = .019). The contribution of urine POC to the model was however not statistically significant (P = .487).The model explained 15.5% (Nagelkerke P = .837).Figure ,22 Our findings are however at variance with some other previous studies which reported that pharmacy refill record was not a reliable method of adherence determination. Of note, this finding is valid only for closed-pharmacy systems where patients access medications from only one source, and the Nigerian National ARV programme is implemented as a closed-pharmacy system where patients can only pick-up ARVs from the pharmacy at the treatment center where they are registered.Our study showed that there was no difference between adherence measured using either pharmacy pick-up record or urine-tenofovir point-of-care test. It also showed that an increase in pharmacy refill rate of ≥ 95% was associated with viral suppression. Our data on pharmacy pick-up which is a good surrogate of adherence corroborates earlier reports of the high specificity of pharmacy pick-up records in identifying individuals who are not taking enough medication to attain treatment goals.–28 A cutoff of 80% adherence has been demonstrated to successfully define viral suppression at both < 1000 copies/mL and 200 copies/mL. The level of adherence that is required to achieve HIV viral suppression is now no longer clear because with newer antiretroviral treatments, lower levels of adherence may still achieve successful virologic suppression.,30 This work however adopted the most stringent adherence cutoff of 95% as defined by Paterson et al, and viral suppression was defined as < 1000 copies/mL as specified by the WHO 2016 Consolidated Guidelines for HIV and the Nigerian National HIV Treatment Guidelines.In terms of the cutoff we used for the pharmacy pick-up record categorization, studies vary in the definition of the optimal level of adherence, using ≥ 85%, ≥ 90%, or ≥ 95% of pills taken as the threshold level necessary to achieve viral load suppression.Urine-tenofovir POC test provided equivalent adherence data to pharmacy refill data. The performance of the urine tenofovir test opens up the possibility of an objective metric of adherence that is scalable to LMICs. Unlike most other currently available objective tests  which cost about $140 to $200 per sample, urine-tenofovir POC is cheaper at $10 per test.The main study limitation was its cross-sectional nature and future studies will examine this relationship over time. On the other hand, there was no bias introduced from the participants knowing their pharmacy refill data was being monitored by this one-time evaluation.Another limitation of this study is the small sample size which may not allow for extrapolation of results. This result however provides preliminary data valid to “closed-pharmacy” settings, where patients receive ARV from only one source.This study showed that the urine-tenofovir point-of-care test provided equivalent adherence data to pharmacy pick-up data in the closed pharmacy system. The result portends well for the utility of the inexpensive urine-tenofovir point-of-care test in low- and middle-income countries to objectively assess adherence.The authors gratefully acknowledge the Director-General, management staff, clinicians, pharmacists and data staff at Nigerian Institute of Medical Research. Mr. Samson Folami, a pharmacist on research internship, who assisted in conducting the urine-tenofovir POC test is also acknowledged.Conceptualization: Ebiere Clara HERBERTSONFormal analysis: Ebiere Clara HERBERTSONFunding acquisition: Ebiere Clara HERBERTSON, Monica GandhiInvestigation: Ebiere Clara HERBERTSONMethodology: Ebiere Clara HERBERTSON, Matthew A. SpinelliWriting—original draft: Ebiere Clara HERBERTSONSupervision: Rebecca Soremekun, Olubusola Olugbake, Cecile Decille Lahiri, Monica GandhiValidation: Cecile Decille Lahiri, Monica GandhiWriting—review & editing: Cecile Decille Lahiri, Monica GandhiVisualization: Monica Gandhi


Globally, excess deaths during 2020–21 outnumbered documented COVID-19 deaths by 9.5 million, primarily driven by deaths in low- and middle-income countries (LMICs) with limited vital surveillance. Here we unravel the contributions of probable COVID-19 deaths from other changes in mortality related to pandemic control measures using medically-certified death registrations from Madurai, India—an urban center with well-functioning vital surveillance. Between March, 2020 and July, 2021, all-cause deaths in Madurai exceeded expected levels by 30% . Although driven by deaths attributed to cardiovascular or cerebrovascular conditions, diabetes, senility, and other uncategorized causes, increases in these attributions were restricted to medically-unsupervised deaths, and aligned with surges in confirmed or attributed COVID-19 mortality, likely reflecting mortality among unconfirmed COVID-19 cases. Implementation of lockdown measures was associated with a 7% (0–13%) reduction in all-cause mortality, driven by reductions in deaths attributed to injuries, infectious diseases and maternal conditions, and cirrhosis and other liver conditions, respectively, but offset by a doubling in cancer deaths. Our findings help to account for gaps between documented COVID-19 mortality and excess all-cause mortality during the pandemic in an LMIC setting. Millions of excess deaths are estimated to have occurred in India during the COVID-19 pandemic, but their causes are not well documented at the national level. In this study, the authors use death registration records to describe the extent and causes of excess deaths in the large urban municipality of Madurai. According to estimates by the World Health Organization, the number of deaths occurring globally in 2020–21 exceeded pre-pandemic expectations by 14.9 million, with 86% of this burden occurring in low- and middle-income countries (LMICs) that reported smaller proportions of all confirmed COVID-19 cases and deaths2. In India alone, roughly 3.2–6.5 million excess deaths are estimated to have occurred through the first two waves of the COVID-19 pandemic4, a figure unmatched by any other demographic event in the country’s history since Independence in 19478.A reported 5.4 million people died of COVID-19 in 2020 and 2021 globally. However, this number is believed to be vastly lower than true mortality attributable to SARS-CoV-2. Excess mortality—the difference between the total number of deaths that have occurred and the number of deaths that would have been expected in the absence of the pandemic—has been used extensively to quantify the direct and indirect impacts of the pandemic12, reliable primary data on cause-specific mortality in India and other LMIC settings remain lacking. Lessons from the COVID-19 pandemic may inform assessments of policy decisions around the implementation and relaxation of non-pharmaceutical interventions as well as both global and within-country vaccine distribution13. An early country-wide lockdown was effective in slowing SARS-CoV-2 transmission and blunting the first wave of COVID-19 cases;14 however, concerns arose that such measures—when implemented in LMIC settings including India—would lead to disproportionate harm through secondary effects on healthcare provision, food security, accidents, and interpersonal violence21. Excess death estimates encompass deaths directly caused by COVID-19 and deaths indirectly resulting from overburdening of healthcare systems with COVID-19 cases. In addition, these estimates include both increases and decreases in deaths due to other causes which may have been altered by implementation of non-pharmaceutical interventions. Whereas studies of cause-specific mortality are thus needed to better distinguish the role of COVID-19 and other factors in excess pandemic-associated mortality23, a lack of a lack of functioning vital registration systems has precluded nationwide assessments of mortality due to COVID-19 and other causes within India and most other LMIC settings24.While understanding factors contributing to this substantial loss of life remains crucial to ongoing efforts aimed at documenting the burden of COVID-19 globally25. It is also one of few Indian states with a well-performing Civil Registration System, estimated to capture 100% of deaths, based on concordance with mortality estimates from India’s parallel sample-based mortality surveillance system26. Madurai, a major city and administrative district within Tamil Nadu, was the site of an active SARS-CoV-2 surveillance program during the early COVID-19 pandemic which included expanded case-detection efforts through door-to-door polymerase chain reaction (PCR) testing, serological testing, and syndromic surveillance. Previous analyses of data collected through these efforts have estimated that only one in nine expected COVID-19 deaths during the first wave was captured through case-based surveillance27, consistent with experience in other regions of India29. To understand this differential between observed and expected mortality, as well as changes in mortality attributed to different causes, we analyzed Civil Registration System (CRS) records, including causes of death recorded by registered medical providers, among all decedents in Madurai over the period encompassing the initial country-wide lockdown and first two COVID-19 waves from March 2020 to July 2021.While country-wide mortality and cause-of-death surveillance are infeasible in India, subnational settings with well-functioning vital surveillance systems provide a valuable opportunity to probe changes in the frequency and causes of death during the COVID-19 pandemic. The southern state of Tamil Nadu ranks highly among Indian states in its per-capita public health investment and medical workforce, and is well regarded for the effectiveness of its primary healthcare delivery system30, we first aimed to validate the tabulation of new deaths occurring during this transition period in early 2020. Whereas non-pharmaceutical interventions may have impacted cause-specific mortality beginning from March 24, 2020, changes in recorded mortality between 1 and 23 March 2020—before lockdown measures were implemented or SARS-CoV-2 transmission became widely established—would be expected to signify changes in CRS data accuracy based on reporting timelines for deaths in Madurai (see Methods). Comparing observed mortality from the “control” period of 23 March 2020 to expectations based on 2018–19 observations, the incidence rate ratio (IRR) of deaths was 1.06  overall, 1.11 (0.95–1.31) for males, and 1.00 (0.83–1.21) for females  deaths to occur between 1 March 2020 and 31 July 2021 Table ; Fig. 1.20, the period from 24 March to 31 May, 2020 saw 7% (0–3%) lower-than-expected all-cause mortality  and 85% (78–93%) higher than expected during June–September 2020 and March–July 2021, the periods encapsulating the most acute phases of the first and second waves of COVID-19 cases Table . Increas26, all deaths are issued medically-certified causes within Madurai; this process continued through the pandemic until late within the second wave , but provide important context when interpreting cause-of-death assignments from MCCD data.We used cause-of-death data from Medically-Certified Cause of Death (MCCD) reports issued for each death in Madurai to understand how changes in all-cause mortality tracked with changes in attributions of deaths to various immediate causes. Whereas medically-certified causes of death are available for only 43.9% of deaths within Tamil Nadu 34 Table  and from35 Table . These cWhile the above factors suggest MCCD data may not reveal the same distribution of causes of death as other methods, imperfect sensitivity and specificity of cause-of-death assignments would not be expected to bias the ability of MCCD data to capture changes in cause-specific mortality over time. We therefore focused our analyses on temporal changes in death attributions. We pair our presentation of changes in cause-specific mortality with earlier findings on the reliability of cause-of-death attributions in MCCD data, and with observations from the “control” period of 1–23 March, 2020 Table .Comparing observed data to expectations under a continuation of 2018–19 mortality levels, the period of 1 March 2020 to 31 July 2021 saw 50% (49–59%) fewer deaths attributed to infectious and maternal conditions, 18% (7–28%) fewer deaths attributed to injuries, and 21% (18–25%) more deaths attributed to noncommunicable diseases than expected Table –S12, as 38, and potential sequelae of SARS-CoV-2 infection40. These observations suggest that deaths in the community attributed to cardiovascular/cerebrovascular conditions and diabetes likely included fatal, unconfirmed COVID-19 cases.Cardiovascular and cerebrovascular conditions, which accounted for 61.5%  of all excess mortality observed, peaked in community settings during the first and second waves, but showed no overall increase in healthcare facilities throughout the pandemic (IRR = 0.95 0.88–1.03]). Although a less common attribution, diabetes showed similar patterns. The frequency of deaths attributed to each of these conditions did not depart from expectations based on pre-pandemic mortality levels during the first weeks of India’s country-wide lockdown (IRR = 1.07 [0.97–1.17] for cardiovascular/cerebrovascular conditions and IRR = 0.92 [0.62–1.41] for diabetes), or during the control period from 1–23 March 2020 . AlthouDeaths attributed to senility increased 147% 94–214%) and 117% (71–177%), respectively, during the first and second waves Table , with ne14% and 145, or changes in death attributions associated with lockdown-related disruptions to MCCD functions.Total deaths attributed to infectious diseases and maternal conditions were 61% (19–79%) lower than expected during the first weeks of lockdown, and 42% (11–62%) lower than expected during the first wave; by the time of the second wave, deaths attributed to infectious and maternal conditions were 29% (–4–73%) higher than expected Table . Inciden46, or may indicate the contribution of secondary respiratory infections such as mucormycosis to fatal outcomes among individuals with confirmed or unconfirmed SARS-CoV-2 infection, especially in healthcare settings.Increases in deaths attributed to lower respiratory tract infections during the second wave were observed only in healthcare settings Fig. . Moreove47.Both unintentional and intentional injuries declined during the first weeks of lockdown , whereas injury-associated mortality during the control period from 1–23 March 2020 did not differ from expected levels  during the early lockdown and remained at lower-than-expected levels throughout the first and second waves  as well as reduced cancer screening and diagnosis of new cases within India during the pandemic54.Last, deaths attributed to cancer increased 109% (63–169%) during the first weeks of lockdown, and remained 20% (0–45%) above expected levels through the first wave Table . These oLast, to understand the role of socioeconomic factors in observed changes in mortality, we constructed a ward-level deprivation index using the first principal component extracted from a set of indicators measured in the most recent (2011) Census of India Table . On averWards with greater degrees of deprivation experienced greater excess mortality attributed to infectious diseases and maternal conditions, and greater excess mortality attributable to noncommunicable diseases, throughout the total pandemic period and during the second wave, in particular Table . In cont2. The majority of unaccounted-for deaths are believed to have occurred in LMIC settings, where excess mortality over this period amounted to 12.7 million deaths, in contrast to 1.2 million deaths reported among confirmed COVID-19 cases. India alone accounts for 3.2–6.5 million excess deaths through 2021, representing 21–44% of the estimated total globally. Although India is a diverse country with significant variation in health systems, demographics, and completeness of civil registration records, findings from our analyses may provide insight into changes in mortality with implications extending beyond Madurai, where continuity of CRS and MCCD functions uniquely enabled our study. Discrepancies between all-cause and reported COVID-19 mortality in Madurai closely resemble findings in other settings within India, both over time and across demographic strata58.Globally, excess mortality during the first two years of the COVID-19 pandemic exceeded deaths attributed to COVID-19 by nearly three-fold (14.9 vs. 5.4 million through 31 December 2021)While all-cause deaths increased 30% overall during the period from March 2020 to July 2021, excess deaths were concentrated during periods of peak SARS-CoV-2 transmission associated with the ancestral and Delta (B.1.617.2) variants. The first and second waves saw 25 and 32% of all deaths occurring during the study period, respectively, and 35 and 63% of all excess deaths estimated to have occurred, respectively; mortality occurring during the intervening periods was slightly lower than expected. Excess deaths attributed to infectious and maternal conditions—most prominently including lower respiratory tract infections—as well as cardiovascular/cerebrovascular conditions, diabetes, senility, and other uncategorized causes, exhibited strong temporal associations with surges in deaths attributed to COVID-19 or occurring among individuals with confirmed SARS-CoV-2 infection. Thus, these findings may reveal attributed causes of death among fatal COVID-19 cases, a majority of which likely went undiagnosed.59. Implementation of lockdown measures accompanied expanded social welfare programs targeting older adults and other vulnerable populations in Madurai and other regions of India; these included increasing food rations for individuals covered by the National Food Security Act and door-to-door mobilization of social and community healthcare workforces to deliver essential supplies during the lockdown as well as screen individuals for symptoms and infection27. These efforts may have helped to mitigate vulnerable individuals’ risk of death due to causes unrelated to COVID-19.In contrast, deaths attributed to several other causes, including unintentional injuries and cirrhosis and related liver conditions, exhibited sustained reductions throughout the pandemic which were not observed during a “control” period from 1–23 March 2020, when lockdown-associated disruptions may have been expected to impact reporting. Non-pharmaceutical interventions may have played a role in reducing individuals’ risk of death due to these causes, consistent with findings in several high-income settings30, this total greatly exceeds global average increases of 8% in 2020 and 18% in 20214. Other settings with >25% increases in mortality during the pandemic have included Russia and LMICs within Latin America, Eastern Europe, and central Asia4. As total excess mortality encompasses both COVID-19 deaths as well as increases and decreases in deaths due to causes unrelated to COVID-19, it is important to note that excess mortality ratios do not directly measure the burden of deaths attributable to COVID-19 in any setting.While the 30% increase in deaths in Madurai due to all causes is consistent with observations in other settings across India60. Deaths for which these causes were assigned could also have increased in the community as a result of avoidance of healthcare settings, or due to patients’ lack of access to healthcare facilities during periods with substantial COVID-19 caseload.While excess medically-supervised deaths attributed to lower respiratory infections varied in association with deaths among confirmed COVID-19 cases and deaths attributed to COVID-19, excess deaths attributed to cardiovascular/cerebrovascular conditions, diabetes, and senility or other uncategorized causes occurred primarily in community settings. Such attributions should be viewed with particular scrutiny, as medical practitioners issuing cause-of-death assignments for individuals who died in the community without prior medical care may have had limited access to clinical information to inform their assessment; attributions to senility and uncategorized causes pose particular risks of inaccuracy61. Our findings should thus be interpreted as representing changes in assigned causes of death over time rather than characterizing the true distribution of causes of death within Madurai. Many deaths were not assigned causes at the height of the second wave, when the number of deaths occurring overwhelmed local health and vital surveillance systems. While our study benefits from continuous mortality records in 2018–19 to define baseline expectations, unstable counts for rare death attributions and in small age- or sex-specific strata may limit our ability to reliably predict expected mortality levels in 2020–21. Analyses of ward-level socioeconomic characteristics associated with excess mortality are likewise limited by the lack of updated Census of India data since 2011. As slums or makeshift settlements may be closely interspersed with higher-income communities, analyses undertaken at the level of city wards, and drawing on older data on socioeconomic characteristics of wards, may mask the full extent of socioeconomic variation in excess and cause-specific mortality associated with the COVID-19 pandemic. While the population of Madurai Corporation was estimated at 1,017,865 as of the 2011 census, a lack of reliable up-to-date estimates of population size, overall and within age- and sex-specific strata, and uncertainty about migration which may have occurred during the pandemic in association with lockdown measures, precluded direct estimation of per-capita mortality associated with excess COVID-19 deaths. Last, although it is reassuring that age- and sex-specific observations of all-cause mortality in Madurai broadly reflect those reported in other settings within India57, Madurai is only one city, and observations in this setting may not be uniformly generalizable across regions or to rural contexts. For example, whereas maternal and perinatal conditions are not a major cause of death in Madurai, poorer and rural regions of the country experience markedly higher maternal and infant mortality62. Notwithstanding these limitations, Madurai presents an important setting for analyses, as CRS data have limited reliability throughout much of the rest of the country. Data on medically-certified causes of deaths are even more scarcely available within other Indian states and LMICs, where understanding of the impact of the COVID-19 pandemic on cause-specific mortality remains limited.Our study has several limitations. First, cause-of-death determinations in MCCD data are expected to be imperfect12. While mitigation of COVID-19 morbidity and mortality during the first wave was largely dependent upon non-pharmaceutical interventions, administration of the ChAdOx1 vaccine in India began 16 January 2021, prior to the Delta variant surge. Effectiveness of ChAdOx1 against Delta variant-associated severe disease has been estimated at ≥80% in multiple postlicensure studies64, including in India65. Thus, a majority of COVID-19 deaths during India’s second wave could have been programmatically preventable under a scenario with improved vaccine coverage. Reduced burden on healthcare systems may have also helped to prevent mortality associated with causes unrelated to COVID-19. While our study identifies that lockdowns may have been associated with increases in deaths due to cancer and other conditions for which treatment was interrupted, it is important to note that all-cause mortality declined during this period in Madurai, consistent with observations elsewhere in India57. This finding, together with prior evidence of the effectiveness of India’s nationwide lockdown in delaying widespread SARS-CoV-2 transmission14, presents a reassuring contrast to expectations in early 2020 that lockdowns could cause greater harm than public health benefit, although measures of impact besides mortality remain important to consider.Correctly accounting for deaths and their causes during acute emergencies carries important societal ramifications1. While such gaps have received particular attention in LMICs68, where surveillance and vital registration systems may encounter particular strain, the need to reconcile COVID-19 deaths with all-cause excess mortality has also arisen in high-income countries71. Our findings support ongoing efforts to quantify mortality associated with the COVID-19 pandemic globally, to identify causes of systematic undercounting, and to evaluate the impact of COVID-19 lockdown measures on mortality associated with various causes. Changes in cause-specific mortality during acute phases of the COVID-19 pandemic should inform planning for future public health emergencies necessitating non-pharmaceutical interventions.India is one of many settings where excess all-cause mortality during the pandemic vastly exceeded reported COVID-19 deaths72. Data included unique records for each death within Madurai Corporation during the periods of interest (2018–19 and 2020–21) abstracted from standardized MCCD forms , including MCCD reporting. Vital surveillance in India is mandated under the Registration of Births and Deaths Act of 1969, which provides standardized elements for reporting births and deaths. Operating procedures of the CRS across India are decentralized, with the expectation that local registration units (states and districts) develop customized strategies adapted to their unique contexts. In Tamil Nadu, a local Coordinating Committee leads vital surveillance within districts. Heads of affected households, in coordination with executive officers of lower administrative units  have legal responsibility for notification of all births and deaths occurring in the community to their local registration unit. Police, community healthcare workers, operators of crematories or cemeteries, and other officials who come into contact with the deceased have a responsibility to report deaths that they are the first to observe. Under the MCCD system, attending physicians have responsibility for reporting deaths occurring in healthcare facilities under their supervision, and for assigning medically-certified causes of death to these patients. In addition, physicians who provide care to individuals who die in the community have the responsibility for assigning causes of death; causes of death for individuals who die in the community without prior care are assigned by registered medical providers at the point of declaring each deathms Table  and CRS ms Table  includinWe aimed to compare observed mortality during the pandemic period (2020–21) to expectations under a status-quo scenario of pre-pandemic mortality levels observed in 2018–19. Periods of interest for analysis included the total pandemic period (1 March 2020 to 31 July 2021); early lockdown period (24 March–31 May 2020); first wave (1 June–30 September 2020); and second wave (16 March–15 July 2021). For each period, we summed total deaths across the applicable date range during the pandemic (2020–21) and pre-pandemic (2018–19) periods. We also defined a “control” period from 1–23 March 2020 to assess changes in reporting potentially associated with implementation of non-pharmaceutical interventions. Whereas deaths during 1–23 March 2020 are not likely to have been attributable to COVID-19, and preceded implementation of non-pharmaceutical interventions, reporting of deaths occurring during this period could have been affected by acute lockdown-associated disruptions . We, therefore, aimed to compare observed to expected mortality during this period to determine whether implementation of lockdown measures was associated with changes in deaths reporting.For primary analyses of excess all-cause mortality, we fit Poisson regression models to total mortality counts for each period of interest; data included period-specific total mortality during the applicable date ranges in 2018, 2019, 2020, or 2021. For each period, we defined population offsets accounting for expected changes in age- and sex-specific population sizes (log-transformed) for 2020–21 Table . We usedTo understand the potential association of various attributed causes of death with unconfirmed, fatal COVID-19 cases, we next aimed to determine whether cause-specific excess death time series were correlated with two measures of mortality related to COVID-19: (1) the number of deaths attributed to COVID-19 in MCCD data, and (2) the number of deaths occurring among individuals with confirmed SARS-CoV-2 infection. We generated 2-week moving average time series of expected mortality for all causes and for specific causes, in both healthcare and community settings, by sampling from Poisson distributions for total deaths each calendar day; these Poisson distributions were parameterized by mortality rates over the period from 7 days before to 7 days after each calendar date. We defined two-week moving averages of excess mortality by subtracting sampled draws from the distribution of expected mortality over each period 7 days before to 7 days after each period of interest from observed mortality over the same period. We then computed Pearson correlation coefficients measuring the association of the resampled time series of 2-week moving averages of excess mortality with each of the two independent variables measuring COVID-19-related mortality.N = 100); whereas the census is ordinarily carried out on a decennial basis, data collection for the 2021 Census of India was delayed to 2023 due to disruptions from the COVID-19 pandemic. Consistent with prior analyses57, extracted variables included the following: household crowding ; adult illiteracy ; membership in scheduled castes or tribes ; household condition ; water and sanitation access ; access to banking (measured as the proportion of households with bank accounts); and lack of material assets , within each ward, as the outcome, and the community deprivation index as the exposure. To propagate uncertainty in ward-level excess mortality, we repeated regression analyses across 10,000 samples from the distribution of excess mortality estimates by ward. As a secondary analysis, we also estimated associations of excess ward-level mortality with each of the deprivation indicators used to generate the principal component-based index individually, applying the same regression framework.Finally, we aimed to assess whether community socioeconomic attributes were associated with the completeness of COVID-19 mortality reporting. We assessed reporting completeness using case-based and mortality-based surveillance, defining two independent variables as potential measures of completeness: (1) the proportion of all excess deaths occurring among reported cases within each ward, obtained by dividing ward-level deaths among confirmed COVID-19 cases by total excess deaths; (2) the proportion of all excess deaths attributed (on death certificates) to COVID-19, obtained by dividing ward-level deaths attributed to COVID-19 by total excess deaths. Each of these analyses propagated uncertainty in excess death measures according to the framework described above; parameter estimates are pooled across analyses undertaken on unique draws from the distribution of excess mortality by ward.Local authors  were involved in all components of the research process including study conception in relation to local priorities, study design, and authorship of publications. Analyses of de-identified mortality data generated through routine vital surveillance functions of the Civil Registration System were considered to constitute non-human subjects research and ethical review of the project was not required by local institutions  as a condition for data sharing, or by foreign institutions with which the authors are affiliated  as a condition for data analysis (NIH Exemption Category 4 for non-human subjects research). This research project did not involve risks to human or animal subjects or environments, or international transfer of biological materials or cultural artefacts from India. Studies undertaken in India have been cited appropriately.Further information on research design is available in the Supplementary InformationPeer Review FileReporting Summary


Compared with lifestyle counseling alone, is liraglutide, mid-dose phentermine and topiramate, top-dose phentermine and topiramate, or semaglutide adjunct to lifestyle counseling cost-effective in treating obesity among adolescents in a simulated cohort?In this economic evaluation including 100 000 simulated adolescents, no pharmacotherapy was estimated to be cost-effective after 13 months and 2 years of treatment. After 5 years, top-dose phentermine and topiramate was projected to be cost-effective, with an incremental cost-effectiveness ratio of $56 876 per quality-adjusted life year gained.This economic evaluation found that top-dose phentermine and topiramate adjunct to lifestyle counseling was cost-effective for the treatment of obesity in adolescent patients after 5 years. This economic evaluation estimates the cost-effectiveness of lifestyle counseling alone and as adjunct to liraglutide, mid-dose phentermine and topiramate, maximum-dose phentermine and topiramate, or semaglutide among adolescent patients with obesity. Antiobesity pharmacotherapy is recommended for adolescents ages 12 years and older with obesity. Several medications have been approved by the US Food and Drug Administration for adolescent use, but the most cost-effective medication remains unclear.To estimate the cost-effectiveness of lifestyle counseling alone and as adjunct to liraglutide, mid-dose phentermine and topiramate (7.5 mg phentermine and 46 mg topiramate), top-dose phentermine and topiramate (15 mg phentermine and 92 mg topiramate), or semaglutide among adolescent patients with obesity.This economic evaluation used a microsimulation model to project health and cost outcomes of lifestyle counseling alone and adjunct to liraglutide, mid-dose phentermine and topiramate, top-dose phentermine and topiramate, or semaglutide over 13 months, 2 years, and 5 years among a hypothetical cohort of 100 000 adolescents with obesity, defined as an initial body mass index  of 37. Model inputs were derived from clinical trials, published literature, and national sources. Data were analyzed from April 2022 to July 2023.Lifestyle counseling alone and as adjunct to liraglutide, mid-dose phentermine and topiramate, top-dose phentermine and topiramate, or semaglutide.The main outcome was quality-adjusted life years (QALYs), costs (2022 US dollars), and incremental cost-effectiveness ratios (ICERs), with future costs and QALYs discounted 3.0% annually. A strategy was considered cost-effective if the ICER was less than $100 000 per QALY gained. The preferred strategy was determined as the strategy with the greatest increase in QALYs while being cost-effective. One-way and probabilistic sensitivity analyses were used to assess parameter uncertainty.The model simulated 100 000 adolescents at age 15 with an initial BMI of 37, of whom 58 000 (58%) were female. At 13 months and 2 years, lifestyle counseling was estimated to be the preferred strategy. At 5 years, top-dose phentermine and topiramate was projected to be the preferred strategy with an ICER of $56 876 per QALY gained vs lifestyle counseling. Semaglutide was projected to yield the most QALYs, but with an unfavorable ICER of $1.1 million per QALY gained compared with top-dose phentermine and topiramate. Model results were most sensitive to utility of weight reduction and weight loss of lifestyle counseling and top-dose phentermine and topiramate.In this economic evaluation of pharmacotherapy for adolescents with obesity, top-dose phentermine and topiramate as adjunct to lifestyle counseling was estimated to be cost-effective after 5 years. Long-term clinical trials in adolescents are needed to fully evaluate the outcomes of pharmacotherapy, especially into adulthood. However, these are proprietary medications and have annual costs ranging from $1100 to $15 000. Estimating the cost-effectiveness of these adjunctive AOMs may help guide uptake and pricing.Significant placebo-subtracted BMI reductions from baseline to approximately 1 year have been seen with pharmacologic treatment of obesity in clinical trials of adolescents ages 12 years to younger than 18 years, with reductions of 4.6% reported for liraglutide,The purpose of this study was to estimate the costs, quality-adjusted life-years (QALYs), and cost-effectiveness of lifestyle counseling alone and adjunct to liraglutide, mid-dose phentermine and topiramate, top-dose phentermine and topiramate, or semaglutide to treat US adolescents with obesity over 13 months and estimate projections out to five years.CHEERS) reporting guideline. The Criteria for Health Economic Quality Evaluation tool was referenced for methods and reporting quality.15This economic evaluation used computer-based modeling and is not considered human participants research at Columbia University; therefore, neither institutional review board approval nor informed consent were sought. Reporting of this study followed the Consolidated Health Economic Evaluation Reporting Standards  female, and BMI of 37.14 While clinical trials stated their eligibility as BMI at the 95th percentile or higher according to sex- and age-specific growth charts, the average participant had severe obesity, with a mean BMI of 120% or more of the 95th percentile and/or BMI of at least 35.11 Cost-effectiveness was assessed at 13 months , 2 years, and 5 years , mid-dose phentermine and topiramate, top-dose phentermine and topiramate, or semaglutide (2.4 mg weekly) in adolescents with obesity.17 Other model inputs were derived from clinical trials, published literature, and national sources.35Each month of the simulation, individuals could remain on treatment, permanently discontinue treatment, or die . Adverse12 Liraglutide and semaglutide are glucagon-like peptide 1 analogues that are administered via subcutaneous injection daily and weekly, respectively, when used for weight management.37 Phentermine and topiramate is a fixed-dose combination of immediate-release phentermine and extended-release topiramate administered orally daily for weight management.20 AOMs were administered with lifestyle counseling. Bariatric surgery was not a comparator because it is typically considered for adolescents with a BMI greater than that of our modeled cohort.38Lifestyle counseling consisted of counseling on healthy nutrition, including a diet aligned with dietary guidelines, and daily moderate-to-high intensity physical activity, with the inclusion of parents or guardians.39 At age 15 years, a BMI of 37.0 is greater than 120% of the 95th percentile for girls and boys. We assumed no change in the BMI-for-age percentile occurred, resulting in a BMI of 42.5 after 5 years without treatment.A natural BMI trajectory estimating BMI change without treatment was derived from extended BMI-for-age growth charts from the CDC.12 and was dependent on how long treatment was received , psychiatric , and severe adverse events (AEs) were incorporated in the model as they were the main 3 types of AEs reported in adolescent clinical trials . Data analysis was performed from April 2022 to July 2023. Our primary end points were quality-adjusted life years (QALYs), total costs (2022 USD), and incremental cost-effectiveness ratios (ICERs)  were female. The model replicated intention-to-treat values from randomized clinical trialsOver 13 months, liraglutide was strictly dominated  and mid-dose phentermine and topiramate was extendedly dominated  compared with top-dose phentermine and topiramate . Top-dosAt 13 months, lifestyle counseling remained the preferred strategy across all parameter ranges in 1-way sensitivity analyses. At 2 years, top-dose phentermine and topiramate became preferred when its cost was at its minimum value and utility of BMI reduction was at its maximum value. Mid-dose phentermine and topiramate was preferred at its minimum cost. At 5 years, top-dose phentermine and topiramate was the preferred strategy across most parameter ranges . LifestyUsing a WTP threshold of $100 000 per QALY gained, probabilistic sensitivity analysis estimated lifestyle counseling to be the preferred strategy in 100.0% of 1000 probabilistic iterations at 13 months and 81.3% of 1000 probabilistic iterations at 2 years . By 5 yeDue to semaglutide’s effectiveness in maintaining BMI reduction from baseline, we performed a threshold analysis to determine the price reduction needed for it to become the preferred treatment strategy. At 13 months, the monthly cost of semaglutide would need to be reduced by 97.5%  for it to become cost-effective 49 and long 52 time horizons with some exceptions.53 Four adult cost-effectiveness analyses included liraglutide, phentermine and topiramate, and semaglutide as comparators.52 All models estimated semaglutide to have the greatest gain in QALYs, and 2 models projected phentermine and topiramate to be the cost-effective strategy,52 similar to our findings.Several analyses have evaluated the cost-effectiveness of school-based programs,11 Physical health benefits with AOMs may not be observed for years, but quality of life improvements may be seen more quickly. Adolescents with obesity experience weight stigma, especially in school settings.54 Therefore, short-term analyses may be useful in this school-age population as short-term weight loss was still associated with increased QALYs in our model. With the recent release of guidelines from the AAP, which recommend the use of AOMs for adolescents ages 12 years or older,7 our analysis is timely and underscores the need to compare the effectiveness and cost-effectiveness of AOMs.The goal of obesity treatment in adolescents is to improve quality of life and reduce the risk of future chronic disease.7 Most state Medicaid plans do not cover AOMs, and private insurance often has strict criteria for authorization.56 Given that obesity is a chronic disease, mounting AOM costs over time may prevent use of these medications.Though semaglutide was the only strategy estimated to have a BMI reduction relative to baseline at 5 years, its monthly price of nearly $1300 resulted in an ICER well above our WTP threshold. The high cost of AOMs, especially out-of-pocket, is a major barrier that may discourage patients from receiving or adhering to the aggressive treatment that the AAP recommends.7 While lifestyle counseling often has high attrition rates, it was included as a comparator because it is the foundational approach for BMI reduction.7 Our cost estimate for lifestyle counseling may not have represented the intensity of counseling provided in the adolescent clinical trials, since the number of contact hours in the trials was not reported. We used a limited societal perspective that did not include all indirect costs. We assumed patients receiving no treatment maintained their BMI-for-age percentile and did not incorporate secular trends or individual heterogeneity in BMI trajectories. Our model assumed BMI regain occurred at a constant rate and may not have accounted for nonlinear associations of costs and mortality with BMI. Because model inputs were estimated from multiple sources, sensitivity analyses were used to address input uncertainty and showed that model results remained robust. Due to lack of long-term adolescent data, we extrapolated BMI changes and treatment adherence from adult data and assumed no treatment discontinuation or further BMI reduction after 2 years of treatment. While there is evidence that adults discontinue AOMs within 2 years,57 we modeled a maximum of 5 years of treatment, since obesity requires long-term treatment.8 We may have underestimated the benefit of AOMs, since obesity-related comorbidities may be prevented or delayed in adulthood.7 However, due to limited research with long-term follow-up, it is unknown whether AOMs lead to sustained BMI reduction, and it is unclear how comorbidities develop through childhood to adulthood.7 Modeled patients were based on participants in clinical trials and were not a nationally representative sample. Future clinical trials should enroll participants who are representative of the US population, including low-income; racial, ethnic, gender, or sexual minority; and other underrepresented patients and should report differential outcomes based on patient subgroups. The ideal follow-up time of future trials is at least 10 to 20 years to capture benefits of disease prevention.The results of our analysis should be considered in the context of several limitations. We did not include all FDA-approved AOMs for adolescents. We excluded orlistat, since it is currently rarely used due to AEs and limited tolerability.In this economic evaluation, we projected top-dose phentermine and topiramate adjunct to lifestyle counseling to be cost-effective for the treatment of obesity in US adolescent patients after 5 years. While semaglutide led to the greatest reduction in BMI, it was not cost-effective due to its high cost. Long-term clinical trials are needed to fully understand the safety, efficacy, and cost-effectiveness of AOMs in adolescents.


The most common cannabinoid detected in pregnancy was Δ9-THC, whereas the most common cannabinoid detected in childhood was CBD. Postnatal exposure to cannabis was associated with more aggressive behavior , attention deficit/hyperactivity problems , and oppositional/defiant behaviors , as well as less cognitive flexibility  and weaker receptive language . By contrast, prenatal exposure to cannabis was associated with fewer internalizing behaviors  and fewer somatic complaints . Conclusions: Our study suggests that postnatal exposure to cannabis is associated with more behavioral and cognitive problems among 5-year-old children, independent of prenatal and postnatal exposure to tobacco. The potential risks of cannabis use (including smoking and vaping) during pregnancy and around young children should be more widely communicated to parents.Background: Prenatal exposure to cannabis may influence childhood cognition and behavior, but the epidemiologic evidence is mixed. Even less is known about the potential impact of secondhand exposure to cannabis during early childhood. Objective: This study sought to assess whether prenatal and/or postnatal exposure to cannabis was associated with childhood cognition and behavior. Study design: This sub-study included a convenience sample of 81 mother–child pairs from a Colorado-based cohort. Seven common cannabinoids (including delta 9-tetrahydrocannabinol (Δ9-THC) and cannabidiol (CBD)) and their metabolites were measured in maternal urine collected mid-gestation and child urine collected at age 5 years. Prenatal and postnatal exposure to cannabis was dichotomized as exposed (detection of any cannabinoid) and not exposed. Generalized linear models examined the associations between prenatal or postnatal exposure to cannabis with the NIH Toolbox and Child Behavior Checklist T-scores at age 5 years. Results: In this study, 7% ( Cannabis use is becoming increasingly common among pregnant people and parents. In 2017, 7% of pregnant people and 11% of U.S. adults with children in the home reported past-month use . ConveniThe relatively high prevalence of this exposure is a concern because early-life exposures to cannabis may alter the child’s cognitive and behavioral development. At least 44 epidemiologic reports have examined the potential impact of prenatal exposure to cannabis on cognitive outcomes among offspring, ages 1–22 years. Fifteen of these reports found strong evidence of an association between prenatal exposure to cannabis and impaired cognition, including lower developmental quotients  and geneFewer studies have assessed whether prenatal exposure to cannabis is associated with childhood behavior. To our knowledge, 18 epidemiologic reports have examined the association between prenatal exposure to cannabis and offspring behavior between 0–16 years of age ,64,65,66There is a paucity of data on the potential impact of postnatal exposure to cannabis on child cognition and behavior. In a diverse cohort of women with low household incomes and low maternal education, Eiden and colleagues  reportedTo address these gaps in knowledge, we leveraged data from an ongoing, Colorado-based pre-birth cohort. We sought to estimate the associations between prenatal and postnatal exposure to cannabis with childhood cognition and behavior at age 5 years. A novel aspect of our approach is the objective measurement of twelve cannabinoids/metabolites in both maternal urine (collected mid-gestation) and child urine (collected at age 5 years). We hypothesized that both prenatal and postnatal exposure to cannabis would be associated with more cognitive and behavioral problems at age 5 years, independent of early-life exposure to tobacco and other sociodemographic factors.Healthy Start is a racially and ethnically diverse cohort of 1410 mothers and their offspring born between 2010 and 2014. Pregnant females were recruited from the outpatient obstetrics clinics at the University of Colorado Hospital prior to 24 weeks of gestation. Participants were excluded from this study if they were expecting multiple births or had pre-existing diabetes, asthma, cancer, or a self-reported psychiatric illness. Enrolled pregnant people were invited to participate in three pregnancy visits at <23 weeks gestation, 24–28 weeks gestation, and delivery. A childhood follow-up visit occurred in-person when children were 5 years of age, between 2015 to 2021. Prior to participation, written informed consent was obtained. The protocol was approved by the Colorado Multiple Institution Review Board.Mother–child pairs were eligible for the current analysis if they had complete exposure data (cannabinoids were measured in stored child urine samples) and complete outcome data (the mother completed the Child Behavior Checklist (CBCL) and/or the child completed the NIH Toolbox Cognition Battery assessment). Mother–child pairs were excluded from this analysis if delivery occurred prior to 37 weeks of gestation or if they had a birth weight less than 2500 g. These factors are independently linked to neurodevelopmental delays , though Fetal and postnatal exposure to cannabis: Thirteen cannabinoids/metabolites were measured in stored maternal urine samples collected at 27 weeks gestation and child urine samples collected at age 5 years using a validated high-performance liquid chromatography-tandem mass spectrometry (LC-MS/MS) assay . SamplesIn brief, samples were analyzed on an Agilent 1 limited by the cannabis exposure assessment. High-performance liquid chromatography system  and AB SCIEX API5000 tandem mass spectrometer  were used, as previously described . DetectiThe limit of quantification (LOQ) varied as follows: 0.39 ng/mL (for Δ9-THC), 0.78 ng/mL , 1.56 ng/mL , and 7.82 ng/mL (for Δ9-THC-C-gluc). Due to the limited sample size, postnatal exposure to cannabis was dichotomized as exposed (where any cannabinoid/metabolite was at or above the lower LOQ) and not exposed (where no cannabinoid or cannabinoid metabolites reached the lower LOQ). For the prenatal exposure to cannabis variable, we dichotomized the variable based on the limit of detection (LOD), which is approximately half of the LOQ. This is in line with the Clinical Laboratory Institute guidelines, which allow for the use of concentrations above the LOD and below the LOQ. The rationale for including values below the LOQ and above the LOD was to the ensure that we had a sufficient sample size to assess these associations in this small pilot study.Childhood cognition: The NIH Toolbox Cognition Battery is a series of tests designed to measure cognitive processes across the lifespan (ages 3 to 85 years) . Three tChildhood behavior: The preschool version of the Child Behavior Checklist (CBCL) was completed by mothers at the 5-year research visit. The CBCL is a widely used assessment of behavioral adaption with excellent reliability and validity . MothersCovariates: Cotinine (the major metabolite of nicotine ) was meaMaternal age at delivery was calculated based on offspring delivery date and maternal date of birth. Maternal education, race, ethnicity, and annual household income were collected via questionnaires. Maternal height was measured using a stadiometer at the first prenatal research visit. Pre-pregnancy weight was obtained from medical records or from questionnaires completed at enrollment. Gestational weight gain was calculated as the difference between the last available weight measurement during pregnancy and the pre-pregnancy weight. Although maternal psychiatric illness was part of the initial exclusion criteria, some participants did not self-report a condition or were diagnosed after recruitment into our study. Therefore, we obtained information about maternal psychiatric disorders (non-specified) via medical records.Statistical analyses: Generalized linear models estimated the associations between prenatal or postnatal exposure to cannabis (not exposed or exposed) with age- and sex-corrected behavior and fully corrected cognition T-scores at age 5 years. We present adjusted beta coefficients with corresponding 95% confidence intervals (CIs) for all models. Our models adjusted for potential confounders and precision variables, which were identified by the construction of a directed acyclic graph. These covariates include maternal age (years), maternal education (college degree or less than a college degree), maternal race and ethnicity , a non-specified maternal psychiatric diagnosis (yes or no), child sex, child age at the behavioral or cognitive assessment (years), prenatal exposure to tobacco , and childhood exposure to tobacco (child cotinine < LOD or child cotinine ≥ LOD).p < 0.05.Stata version 14.2  was used for all analyses. The criterion for significance was set at n = 11) or a birth weight less than 2500 g (n = 9). An additional 86 participants were missing information about maternal psychiatric disorders (n = 12) and childhood exposure to tobacco (n = 74). Of the eligible sample (n = 93), twelve did not complete the CBCL. Therefore, the final analytic sample for the CBCL analyses was 81. Of the eligible sample (n = 93), 47 did not complete the NIH Toolbox Cognition Battery, since this assessment was introduced later during the study. Therefore, the final analytic sample for the NIH Toolbox analyses was 42.Healthy Start initially enrolled 1410 participants. A subsample of 199 participants with stored urine samples collected at 27 weeks gestation were included in this sub-study. Of these, twenty participants were excluded due to delivery prior to 37 weeks (n = 199), participants in the analytic sample (n = 81) were less likely to have prenatal exposure to cannabis  and postnatal exposure to cannabis  (n = 1410), participants in the sub-study (n = 199) and the analytic sample (n = 81) were more educated, had higher incomes, were less likely to have a maternal diagnosis of a psychiatric illness, and had offspring with higher birthweights. Differences in maternal age, pre-pregnancy BMI, gestational weight gain, infant sex, or gestational age at birth between the sub-study and analytical sample were negligible.Compared to the full sub-study (and 12%) . Comparen = 6, 7%), followed by Δ9-THC-COOH , and Δ9-THC-gluc . Of those with detectable levels of CBD or CBG in maternal urine, Δ9-THC-C-gluc was also detected in urine. In child urine, the most commonly detected cannabinoid metabolite was CBD-gluc , followed by CBD-COOH . None of the children had detectable levels of the following cannabinoid metabolites in urine: Δ9-THC, Δ9-THC-COOH, Δ9-THC-gluc, 11OH-Δ9-THC-gluc, CBC, CBN, CBG, THCV, or CBDV.Cannabinoids were detected in 7% of the maternal urine samples and 12% of the child urine samples . In maten = 6), only two (33%) offspring had postnatal exposure to cannabis, three (50%) had concurrent prenatal exposure to tobacco, and three (50%) had childhood exposure to tobacco. A similar pattern held for postnatal exposure to cannabis. Among those with postnatal exposure to cannabis (n = 10), three (30%) offspring had prenatal exposure to tobacco and four (40%) had concurrent childhood exposure to tobacco.n = 6) of the children had prenatal exposure to cannabis and 12% (n = 10) of the children had postnatal exposure to cannabis at age 5 years. Additionally, we provide novel evidence that postnatal exposure to cannabis is associated with more aggressive behaviors, attention deficit/hyperactivity problems, and oppositional/defiant behaviors in the offspring, as well as less cognitive flexibility and receptive language, independent of prenatal and childhood exposure to tobacco. These effects were meaningful, with at least one standard deviation difference in behavior and cognition scores between those with and without postnatal exposure to cannabis. Contrary to our hypothesis, prenatal exposure to cannabis was associated with fewer internalizing behaviors and somatic complaints, but not with any of the cognitive outcomes measured.Early-life exposure to cannabis is an emerging concern. In this sub-study from a well-phenotyped, pre-birth cohort in Colorado, we observed that 7% , only 7% had evidence of prenatal exposure to cannabis. If the loss to follow-up was non-differential with respect to the exposure, this may result in a bias towards the null or, at worst, a change in the direction of the association [Contrary to our hypothesis, we found some evidence that prenatal exposure to cannabis was slightly protective against internalizing behaviors and somatic complaints. This could be explained by several of our study’s limitations. First, our results may have been biased by self-selection bias. In our sub-study, we measured cannabinoids in maternal urine among 199 participants. Of these, 13% had detectable cannabinoids in maternal urine collected in mid-pregnancy. Yet, in our analytic study . As sucConsistent with previous research , young mA distinct advantage of our approach is the population we studied. Pregnant people in Colorado were recruited between 2010 and 2014, amid state-wide legalization of cannabis for recreational use , but prior to widespread health messaging about the potential harm to the fetus. This may explain the relatively high prevalence of this exposure (7%) during mid-gestation (after knowledge of the pregnancy) and in early childhood (13%). Additionally, our study is strengthened by the ability to control for many important covariates, including prenatal and childhood exposure to tobacco (confirmed by the measurement of cotinine).Pregnant people and parents may use cannabis for recreational or medical purposes. However, they may be unaware of the potential risks to their child. Our study suggests that postnatal exposure to cannabis is associated with more behavioral and cognitive problems among 5-year-old children. However, the association between prenatal exposure to cannabis with childhood cognition and behavior requires further investigation. Nevertheless, it is crucial to educate parents and parents-to-be about the potential risks of cannabis use (including smoking and vaping) during pregnancy and around young children.