|Year : 2016 | Volume
| Issue : 4 | Page : 288-291
Comprehensive index for community health assessment of typical district administrative units in Maharashtra State, India
Prakash Prabhakarrao Doke
Department of Community Medicine, BVDU Medical College, Satara Road, Pune, India
|Date of Submission||17-Mar-2015|
|Date of Acceptance||11-Jun-2016|
|Date of Web Publication||3-Nov-2016|
Dr. Prakash Prabhakarrao Doke
Professor, Department of Community Medicine, BVDU Medical College, Satara Road, Pune
Source of Support: None, Conflict of Interest: None
| Abstract|| |
Background: Health administrators require status of health of different administrative units under them. Use of large number of indicators may create confusion and uncertainty about health status. Availability of a comprehensive index is certainly useful.Objective: To evolve one comprehensive health index for a district as unit and measure district wise disparity.Materials and Methods: Ten indicators from categories of health outcomes, health system, determinants of health, and utilization of services were considered. Data for districts in Maharashtra State were obtained from different sources.For each indicator the best performing district was given score of 100 and other districts were given marks proportionately.Results: The comprehensive index for the state was 0.52. The district scoring lowest value of 0.36 was a tribal district and scoring highest value of 0.66 was a nontribal district.Conclusion: Computing such index of districts for monitoring and allocation of resources may be useful managerial tool.
Keywords: India,Comprehensive index,health disparities,health outcomes,health system,a desk analysis
|How to cite this article:|
Doke PP. Comprehensive index for community health assessment of typical district administrative units in Maharashtra State, India. Indian J Community Med 2016;41:288-91
|How to cite this URL:|
Doke PP. Comprehensive index for community health assessment of typical district administrative units in Maharashtra State, India. Indian J Community Med [serial online] 2016 [cited 2021 Oct 17];41:288-91. Available from: https://www.ijcm.org.in/text.asp?2016/41/4/288/193339
| Introduction|| |
Reliable information about health status of the community is the prerequisite for planning of service. Under National Health Mission preparation of District Health Action Plan (DHAP) with present status is mandatory. Numerous indicators are in vogue for assessment of health status of the community. Large scale survey using tool seeking the peoples ’view on various aspects of health is a common practice. Indicators like Disability-Adjusted Life Years and Disability-Adjusted Life Expectancy are probably the best indicators evolved. District wise prerequisite information for these indicators is not easily available. Usually, managers resort to some traditional indicators that have varied reliability and validity and provide information about specific aspects of health. Like human development index, comprehensive health index may be obviously very informative and desired. Such index will help to compare the districts and allocate resources to the area proportionate to the index. The region wise compiled information dilutes the actual situation in the districts, hencde, the district-wise analysis is best tool. Unfortunately, district-wise data of reasonable reliability are seldom available excepting District Level Household Survey (DLHS) and Annual Health Surveys (in selected states). Health Management Information System (HMIS) also provides information. An attempt is made here to develop a comprehensive index from available data sources for a district and use it for measurement of district wise disparity.
| Material and Methods|| |
As per Census 2011, Maharashtra has population of 112.37 million. In the state six administrative divisions are functioning. One division called Marathwada was a part of erstwhile Nizam State till 1956. Similarly another division Vidarbha was transferred from Madhya Pradesh to Maharashtra in 1956. For various reasons the development in Marathwada and Vidarbha was dissimilar to other divisions. This fact has been confirmed even in Human Development Report published by Government of Maharashtra in 2012. In that report all the districts below median were from Marathwada and Vidarbha (excepting two which are tribal districts).Conventionally, therefore, any status and progress in the state is compared between Marathwada, Vidarbha, and rest of the Maharashtra State; although the rest of the area of Maharashtra State is a very heterogeneous group of population. In each division there are districts having pockets of tribal population. The vulnerability of tribal population is well known. There are 35 districts inclusive of two districts of Mumbai Municipal Corporation, which are not considered in the study. The state has notified 16 districts as “ tribal ”.
Data collection and compilation
A speculative list of indicators of community health assessment was prepared. District wise reliable information of these indicators was sought from various sources. The sources included HMIS of Directorate of Health Services, Cause of Death Survey conducted by Directorate of Health Services, DLHS 3 Maharashtra, published or unpublished studies, internet, and so on. HMIS in Maharashtra State is well established and acknowledged system since 1982 –1983 and was considered comparable to other data sources. The investigator collected information of 68 indicators. District wise reliable data about few indicators like number of cancer cases, risk factors like tobacco consumption and so on was not available. Wherever there was more than one source, the latest available statistics were considered (excepting immunization coverage for which data from DLHS 3 was used). The list 68 indicators was first discussed and consensus arrived among multispecialty experts in economics, education, and administration, including senior most public health specialist from Society for Education, Action and Research in Community Health (SEARCH) Gadchiroli in series of meetings for determining priority. Then two senior public health specialists who have worked as Professor in Community Medicine have been consulted for finalizing the list. All the indicators excluding demographic were classified into following four categories health outcomes, health systems, other determinants of health, and utilization of services slightly different from conventional way. It was also agreed that there will be scoring system having 1000 marks and limit the indicators to 10 for easy calculation. The final score may be then converted into index. It was also decided to allocate 40% weightage to health outcomes, 30% to health system, 20% to other health determinants, and 10% to utilization of services. Infant Mortality Rate (IMR), Maternal Mortality Ratio (MMR), Total Fertility Rate (TFR), proportion under five children having malnutrition, doctor population ratio, nurse population ratio, bed population ratio, proportion of water contamination, age at marriage for girls, and proportion of fully immunized infants were considered for inclusion. Maximum 100 marks were allotted for each indicator. For each indicator the district having best value was given 100 marks and rests of the districts accordingly were given marks. Correlation coefficients between comprehensive index and proportion of tribal population, per capita income, and female literacy rate were calculated.
| Results|| |
The district wise comprehensive index, thus, calculated is given in [Table 1]. The state index was 0.52 (Interquartile range; 0.47–0.58). A tribal district has lowest index (0.36) and a nontribal district has highest value (0.66). There is negative correlation [r = -0.73; 95%confidence interval (CI) = -0.37 to -0.90] between proportion of tribal population  and comprehensive index. There is positive correlation (r = 0.65; 95% CI = 0.40–0.81) between comprehensive index and per capita income  and also between comprehensive index and female literacy rate  (r = 0.71; 95% CI = 0.49–0.85). Lowest scoring division is Marathwada and highest scoring is Rest of Maharashtra. Gadchiroli, Washim, and Nagpur from Vidharbha topped the list in one indicator each. No district from Marathwada has best performance. Sindhudurg district from rest of Maharashtra has the distinction of appearing twice in best performance. The division wise distribution of districts appearing in first (lowest quartile) and fourth quartile (best quartile) is depicted. All the districts excepting two in lower quartile are from Marathwada and Vidarbha, whereas all the districts excepting two in upper quartile are from rest of Maharashtra.
|Table 1: Comprehensive health index of the districts, Maharashtra State, India|
Click here to view
| Discussion|| |
There is a long list of health indicators. Each indicator reflects some specific aspect of health. In this study ten indicators from four categories have been used to develop a comprehensive index. The indicators have been selected considering Indian States. Selection of three indicators monitored under NRHM was imperative, as they are accepted valid for health outcomes. The child malnutrition has always been a hot topic for health professional, press, and politicians in Maharashtra. The series of national level surveys did not show appreciable decline in malnutrition. Second reason for its inclusion was to cover child health after infancy period over. Among the health determinants three most standard indicators of health system were given highest weightage pondering a separate category. The whole purpose of creation of resources is to transform the health system and bring out positive changes in the health status. The proportion of water contamination is the best proxy for all waterborne diseases. The role of waterborne diseases in transforming health status goes unargued in developing countries. The mean age at marriage for girls is one of the strong determinants of maternal health. Although there is an act prohibiting marriage before 17 years for girls, child marriages are rampant in Maharashtra. Among health determinants income and education were considered only for validity check and the correlation is as anticipated. By their inclusion the comprehensive index may certainly resemble Human Development Index. Also, they are not directly under health sector. The last indicator is one of the determinants of first indicator which itself is considered appropriate and sensitive indicator of health of the community. It was included as a proxy of utilization of health services.
The investigator agrees that the selection of indicators is skewed toward maternal and child health sector. In India such emphasis is needed. Even Government of India considers 16 RCH indicators for comprehensive assessment. Such appropriate mix of RCH indicators may be also used in developing countries. Even the report from United States on Health Disparities and Inequalities includes health outcomes in the form of morbidity and mortality covers, social determinants, and health care access and preventive services like present study. United States’ report also includes environmental hazards and behavioral-risk factors, which are surely locally relevant. In this study proportion of tribal population has emerged is strong impediment for health status. Among notified tribal districts the proportion of tribal population varies from 3.7% in Pune district to 69.3% in Nandurbar district and the index reflects it. In Gadchiroli and Chandrapur districts the Left Wing extremist insurgency may be one of the reasons for lagging behind. In Maharashtra an earlier committee laid emphasis on infrastructure alone, converting the gap into financial implications. The committee considered all the districts having figured less than state average as districts having backlog resulting into a large scope requiring special attention. Investigator recommends attention toward districts in poor performing quartile.
| Conclusion|| |
The study validates the concept and utility of comprehensive index. Comprehensive index may certainly help in assessment of overall health status, measure disparity and provide technical, and managerial robust tool to initiate measures for improvement through DHAP.
| Limitations|| |
The study has used HMIS data, which were not open source. IMR and TFR estimates are exclusively from rural units. District wise estimation of MMR may have compromised validity.
Investigator thanks Dr. Sanjay Chahande, Director General, Yashwantrao Chavan Academy of Development Administration and Dr. Abhay Bang.
Financial support and sponsorship
Conflicts of interest
There are no conflict of interest.
| References|| |
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