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Aims: This study aimed to determine the adherence to treatment and affecting factors in tuberculosis patients during COVID-19 outbreaks.
Instrument & Methods: This is a mixed-method study with an explanatory sequential design. The first phase was a cross-sectional study on 108 tuberculosis patients. Data were collected with an adherence questionnaire in patients with chronic disease. Descriptive statistics and linear regression tests were used for quantitative data analysis. The second phase was a qualitative study with the conventional content analysis approach. Participants included 12 patients and healthcare providers involved in the directly observed treatment short course program for tuberculosis in related centers to Iranshahr University of Medical Sciences, Southeast of Iran.
Findings: The median scores of treatment adherence were 110.00±35.00, and 48 (44%) of subjects had poor to moderate scores. COVID-19 and non-COVID-19 factors have influenced the adherence to treatment in patients with tuberculosis patients.
Conclusion: A worldwide and unanticipated crisis can negatively affect the control of chronic diseases.  Anxiety and panic over the unknown nature, lack of definitive treatment, and mortality of COVID-19 led to the disruption of the directly observed therapies program by both tuberculosis patients and healthcare providers. Giving part of the tuberculosis management responsibility to the patient and family was not an effective solution during the COVID-19 crisis.
 

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In this study, using the classic and developed models of data envelopment analysis, the frontiers of efficiency and inefficiency were determined for 29 irrigated crops and the relative position of products was determined in terms of gross income and gross profit efficiencies. Research data included cost of inputs used in the preparation, planting, growing and harvesting stages along with income and profitability data for the crop year of 2014-15, which was extracted from the reports of Ministry of Agricultural Jihad.
The results showed that in the input-oriented approach for gross income, the efficiency ranged from 0.2698 to 0.8660. On average, at best, 12.4% and in the worst situation, 73.2% of inputs were overused to generate gross income. The worst rank devotes to barley in the optimistic scenario and to potato in the pessimistic scenario. For the gross profit, the average efficiency score in optimistic and pessimistic scenarios were 0.6353 and 0.1997, respectively, and minimum and maximum scores belonged to barley and potato, respectively. In the output-oriented approach, the average inefficiency for gross income was between 0.2805 and 0.8698, which indicated a possible reduction of income by 71.95% and 13.22%, respectively. In the case of gross profit, optimistic and pessimistic efficiencies were 18.15% and 55.4%, respectively. According to the findings, barley had the maximum inefficiency in all models.
 

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In recent years, the lack of a standard framework and the lack of a proper planning for the location of chain stores have caused most of these commercial locations not to be located according to defined standards; instead of scientific and knowledge-based work, it is mostly arbitrary. On the other hand, in most cases, the method of replacing stores in decision-making institutions is done with traditional methods and without scientific justification, and it is rather experimental and subjective. As a result, difficult access for customers and low sales and profitability have been among its consequences. Therefore, today, one of the reasons of the failure of chain stores is the lack of proper location in this area.
This research is descriptive in terms of purpose and applied in terms of results. In this study, the factors affecting the optimal location of chain stores were graded using interpretive structural modeling.
The geographical territory of this research is Guilan province.
Considering the importance of recognizing the effective factors in the optimal location of chain stores, in this study, first, by reviewing the literature and research background, important factors were identified based on the use of experts in the field of marketing and urban geography; out of about 45 effective factors,17 main ones were identified. In the next step, using a new analytical methodology called “interpretive structural modeling,” the relationships between the dimensions and indicators of the process are determined and analyzed in an integrated manner. The results of interpretive structural modeling showed that the indicators of demographic characteristics of the region, economic factors of customers, and market attractiveness are in the sphere of influence. These indicators have high influential power with minimal dependency which broadens an understanding of the other indicators.
This study presents a model for determining the causal relationships among the variables affecting the location of chain stores. The results of this study help investors and decision-makers in urban geography to choose the most appropriate path for the development of chain stores.